Polarimetrie Signatures and Classification of Alpine ... · fore, a polarimetric sensor operating in various frequency bands provides information about the imaged target over a wide

Polarimetrie Signatures and Classification

of Alpine Terrain by Means of SIR-C/ X-SAR

Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften

an der Leopo ld-Franzens-Universität

Innsbruck

eingereicht von Dana-Marie Floricioiu

Innsbruck, im Oktober 1997

Abstract The potential of multiparameter Synthetic Aperture Radar (SAR) for hydrological and glacio logical applications in high alpine regions is evaluated based on the SIR-C/X-SAR d ata of the test site Ötztal, Austria. The three-frequencies po larimetric Spaceborne Imaging Radar-C/X-band (SIR-C/X-) SAR operated o n bo ard the Space Shuttle Endeavour during two missions, in April and October 1994.

During the overfiights field campaigns were carried o ut includ ing the deployment of trihedral corner refl.ectors and measurements of snow and ice properties. The calibration and image quality parameters of SIR-C/X-SAR data derived from the corner refiector analysis are in good agreement with the values given in the literature.

A short overview of basic theory of radar po larimetry is given and the used po larimetric measures are defined . The scattering mechanisms in snow covered terrairr are studied by means of models for various snow conditions taking the po larization and frequency dependence into account.

The po larimetric response of four surface types ( accumulation area, glacier ice, vegetation and bare surfaces) is analyzed at X-, C- and L-band includ ing the incidence angle dependence, short term and seasonal variations of the elements of the covariance matrix. On the accumulation areas significant d ifferences between April and October d ata are observed for the co - and cross-po larized backscattering co efficients at C- and X-band and for the magnitude of the HHVV correlation coefficient at C-band . For glacier ice seasonal differences are pronounced at L-band co-polarization. For unglaciated areas the soil roughness has a strong infiuence on backscattering in both seasons. Short term changes in snow conditions can be monitared at C- and X-band .

Three classificatio n pro cedures based on SIR-C/X-SAR d ata are tested : maximum likelihood classification, segmentation of multitemporal ratios and hierarchical classifiers. The aim is the separation of accumulation and ablation areas o n the glaciers , and of unvegetated areas and three vegetation classes o n ice-free areas. On the unglaciated areas the performance of the algorithms is estimated through comparison with a classification based o n Landsat TM d ata. SAR data are less suitable than optical imagery for mapping bare so il and short vegetation. On two main glaciers in the site the areal ratio of the accumulation area to the total glacier area, a key parameter in glaciology, was derived from the classifications and found to be in good agreement with the ratio derived from field o bservations.

Contents

Acknowledgements

1 Introduction

2 Sensor characteristics and available data

2. 1 The SIR-C/X-SAR radar system . . . . . . . . . . 2 .2 SIR-C/X-SAR data of the supersite Ötztal, Austria

3 Background of radar polarimetry and basic definitions

3 . 1 Mathematieal representations of seattering . . . . . . 3 .2 Baekseatter measurements and polarization synthesis 3 .3 Polarimetrie measures . . . . . . . . . . . . . .

4 Calibration and quality of SIR-C/X-SAR data

4. 1 SIR-C data . . . . . . . . . . . . . . . .

4 .2

4 . 1 . 1 Mathematieal model . . . . . . .

4 . 1 . 2 4 . 1 . 3 4 . 1 .4

4. 1 . 1 . 1 Radiometrie ealibration 4 . 1 . 1 . 2 Polarimetrie ealibration Radiometrie eorreetion . Polarimetrie ealibration Absolute ealibration . .

4.1 .5 Estimation of additive noise . . . . . . . . . . . . . . . . . . 4 . 1 . 6 Image quality parameters derived from point target analysis 4.1 . 7 Data quality plots . . . . . . . Calibration equation for X-SAR data . . . . . . . . . . . . .

5 Field observations during the SIR-C/X-SAR experiments

5 . 1 Deseription of the test site . . . . . 5 .2 Field measurements during SRL-1 . 5 .3 Field measurements during SRL-2 . 5.4 rdeteorologieal Observations

6 Backscatter modelling of snow covered terrain

6. 1 Pcrmittivity funetion and penetration depth 6.2 Seattering from snow eovered terrairr

6 .2. 1 Snow layer eharaeterisation 6 .2 .2 Model for surfaee seattering 6 .2 .3 Valurne seattering model .

iv

1

4

4 6

15

15 17 19

21

22 22 22 23 24 25 26 30 35 38 43

44

44 44 48 50

54

54 56 57 58 64

Contents 11

6.2 .4 Effeets of layering and erusts . . . . . . . . . . . . . . . . . . 69 6 .2 .5 Combined surfaee and volume seattering effeets . . . . . . . 71

6 .3 Conclusions for the interpretation of SIR-C / X-SAR measurements 75

7 Microwave polarimetric signatures of alpine terrain 79

79 82 83

7. 1 Auxiliary data for signature analysis . . . . . . . . . 7.2 Review of published results on polarimetrie signatures . 7.3 The angular dependenee of baekseattering . . . . . . .

7 .3 . 1 Ineidenee angle dependenee of baekseattering m Apri l'94 . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 .3 .2 Ineidenee angle dependenee of baekseattering in Oetober'94 . 7.4 Seasonal variations of the baekseattered signal . . . . . . .

83 87 90 95 7.5 Short term variations of the baekseattered signal . . . . . .

7.6 7.7

7 .5 . 1 Changes m the baekseattering eoeffieients during SRL-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

7 .5 .2 7 .5 .3

Changes in the eorrelation eoeffieients during SRL-1 . Changes m the baekseattering eoeffieients SRL-2 . . . . . . . . . . . . . . . . . . . . . . . . . .

during

7.5.4 Changes in the eorrelation eoeffieients during SRL-2 . Polarimetrie signatures derived for seleeted snow and iee sites Polarimetrie signatures derived for seleeted moraine, meadow and forest sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97

100 108 108

1 16

8 Target classification in the high alpine test site 121

8 . 1 Review of snow mapping proeedures with SAR . . . . . . . . . . . . 121 8. 1 . 1 Snow classifieation algorithms based on C-band polarimetrie

parameters . . . . . . . . . . . . . . . . . . . . 122 8. 1 . 1 . 1 The classifieation proeedure proposed by H . Rott

(1994) . . . . . . . . . . . . . . . . . . . . . . . . . . 122 8. 1 . 1 .2 The classifieation proeedure of Shi et al. (1994) . . . 123

8 . 1 . 2 Classifieation with repeat pass SAR data developed by T . Na-gler (1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

8 . 1 . 3 Classifieation with polarimetrie multi-frequeney SAR data . . 125 8. 1 . 3 . 1 Classifieation algorithms of J . Shi and J . Dozier (1997) 125 8 . 1 .3 .2 Deeision tree classifieation algorithm developed by

Forster et al. ( 1996) . . . . . . . . . . . . . 126 8.2 Feature analysis and target separability for the Ötztal data . 127

8 .2. 1 Seleetion of the feature veetor . . . . . . . . . . . . . 127 8.2. 2 Seleetion of the surfaee classes . . . . . . . . . . . . . 131 8 .2 .3 Ineidenee angle dependenee of the eomponents of the feature

veetor . . . . . . . . . . . . . . . . . . . . . 134 8 .2 .4 Diseriminant analysis . . . . . . . . . . . . . . . . . . . . . . . 136

8.2.4. 1 Speetral signature data generation . . . . . . . . . . 136 8.2.4.2 Separability measures for two or more speetral classes 136 8.2.4.3 Separability of alpine targets based on the eompo-

nents of the feature veetor 145 8.3 Supervised classifieation based on MLE . . . . . . . . . . . . . . . . . 145

Contents

8.3. 1 8 .3 .2

Description of the algorithm . . . . . . . . . . . . . . . . MLC classification results . . . . . . . . . . . . . . . . .

lll

147 148

8.4 Classification of wet snow based on multitemporal segmentation 155 8.5 Decision tree classification algorithms . . . . . . . . . . . . . . . 158

9 Summary and conclusions 162

A Incidence angle dependence of backscattering derived from DT 14,

18 and 78 170

Bibliography 179

Acknowledgements

Firstly, I would like to thank my supervisor Helmut Rott for giving me the opportunity to carry out research in radar remote sensing, for his scientific guidance and encouragement throughout the last years.

I would also like to thank Shaun Quegan from the University of Sheffield, the second reviewer of the thesis, for his corrections and comments. He very kindly gave up his time to discuss radar polarimetry on a number of occasions.

I am grateful to Ji ancheng Shi from the University of California, Santa Barbara for the stimulating discussions about backscattering models for snow and for advice on the interpretation of polarimetric data. Thanks are also due to Mihai Datcu, Richard Bamler and Manfred Zink at DLR for helping me understand topics related to SAR processing and calibration.

Everybody w ho took part in the field campaigns deserves praise for making ground truth measurements despite the severe weather conditions. Many thanks are also due to my colleagues of the remote sensing group at the Institute of Meteorology and Geophysics, University of Innsbruck for the friendly working atmosphere and in particular to Thomas Nagler, Wolfgang Rack and Martin Stuefer for providing the rep orts on the field work.

Furthermore, I want to thank Hannes Raggarn from the Insti tute for Digital Image Processing of Joanneum Research, Graz, for his p rompt supp ort w henever questions arose regarding the RSG software package.

Finally, I want to thank my parents for their support during all these years of study and Tommy for pushing me forward to finish the thesis, for freely sharing his knowledge and for his suggestions and constant encouragement.

The investigations have been supp orted by the Austrian Academy of Sciences, National Space Research Program. The SAR data were generously made available by NASA/JPL, the German Aerospace Research Center (DLR) and the Ita.lian Space Agency (ASI) for the SIR-C/X-SAR High Alpine SAR Experiment.

lV

Chapter 1

Introduction

Sp aceborne sensors provide valuable information about the earth's surface and environment. Active microwave sensors are of particular interest for this task due to their high resolution and their abi li ty to image through clouds and at night. The conventional spaceborne imaging radars imp lemented for long-duration missions (the SAR sensors ERS-1 , ERS-2, J-ERS-1 , Radarsat) operate in a single-frequency singlepolarization mode. The advances in technologies in the last two decades have led to the development of imaging radar polarimetry, where the complete, complex scattering matrix for every resolution element is measured. This cap abi lity enables the measurement of a target's polarization properties, thus p ermitting a much more deta.iled understanding of the electromagnetic scattering process. Radar backscatter is strongly infiuenced by objects comparable i n size to the radar wavelength. Therefore, a p ol arimetric sensor operating in various frequency bands provides i nformation about the imaged target over a wide range of scales.

This thesis is based on multi-frequency polarimetric SAR data from the Spaceborne Ima.ging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) , the most advanced imaging radar system fiown in earth orbit . Carried in the cargo bay of the Space Shuttle Endeavour in Apri l and October 1994, SIR-C/X-SAR recorded polarimctric data at two wavelengths (L- and C-band) and at vertical polarization in the X-band, at various look angles.

SIR-C is part of a series of spaceborne imaging radar missions that began with the Seasat SAR in 1978 and continued in 1981 with SIR-A and 1984 with SIR-B. The Seasat SAR affered the first large-scale radar observations at L-band uti lizing a single p olarization, passive array antenna at a fixed 20° look angle from an 800 km alti tude. SIR-A fiew spare radar equipment from the Seasat program aboard the Space Shuttle. SIR-B was a repeat of SIR-A, but with variable look angle through mechanical steering of the antenna.

The SIR-C/X-SAR experiment was focused on methods and app lications of SAR in geology, ecology, hydrology, and oceanography. Other i nvestigation topics covc red SAR calibration, electromagnetic theory, and validation of inversion algorithms [Stofan et al. , 1995] . During the last days of the second fiight repeat-pass SAR int.erferometry data were obt.ained. They allowed studies of earth surface change such as the derivation of velocity maps on the Moreno Glacier i n the South Patagonian Icefield [Rott et al . , 1997] , or tracking advancing lava fiows at Kilauea volcano in Hawaii [Evans et al. , 1997] .

1

Chapter 1. Introduction 2

The goal of the hydrology experiments on SI R-C/X-SAR is the improvement of understanding of the hydrologic cycle for global climate modeHing and managing water resources. Hydrology supersites were Montespertoli (Italy) , Chickasha, OK (USA) , Bebedouro (Brazil) , and Ötztal (Austria) . Mammoth, CA (USA) was the back-up site for snow-hydrology.

The SIR-C/X-SAR data from the high alpine region Ötztal are analyzed in this thesis. Preparatory experiments had been carried out in Ötztal with the threefrequency (C- , L- and P-band) polarimetric AIRSAR of NASA/JPL in June 1989 and August 1991 [Rott and Davis, 1993] . Additional investigations on SAR applications started in 1992 and were related to the ERS- 1 mission of the European Space Agency.

The overall goal of this experiment in the test site Ötztal was to evaluate the potential of multiparameter SAR for hydrological and glaciological investigations in high alpine regions and to develop techniques for data analysis. These objectives raised the following main questions:

• How do the published calibration and image quality parameters of SIR-C/XSAR data compare with the results derived from the corner reflectors deployed on the test site?

• What are the capabilities of theoretical models to describe backscattering from snow covered surfaces and glaciers?

• Which are the main target properties influencing the backscattering signatures?

• Which techniques are useful for target classification in alpine terrairr?

• Which polar imetric parameters provide the highest information content?

• Which additional information is gained by multifrequency polarimetric SAR in comparison to single channel SAR?

• What is the potential of multiparameter SAR concerning the detection of dry snow?

• Which information about glaciers and ice-free alpine surfaces can be derived from multifrequency polarimetric data?

These questions comprise the main aspects of the investigations performed in this study. An outline summary of the thesis is presented below.

The characteristics of the SIR-C and X-SAR sensors are specified in chapter 2 as weil as the SAR data used in this study. The innovations in sensor technology implemented for the first time by SIR-C are briefly presented. I nformation about the available data takes over the test site Ötztal is given tagether with the illustration of their various imaging geometries.

The basic theory of radar polarimetry and the relationships between different mathematical representations of polarimetric data are summarized in chapter 3. Definitions of the polarimetric measures used later for signature analysis and classification are also included.

Chapter 1. Introduction 3

Aspects of the calibration of SIR-C and X-SAR systems are presented in chaptcr 4. The trihedral corner reflectors deployed on the glaciers are used to test the accuracy of the external calibration of the polarimetric system. Image quality parameters are calculated from the reflccted power of the p oint targets. The additive noise level is estimated from radar shadow zones at C- and L-band.

Chapter 5 starts with the description of the test site, then rep orts on the field measurements and meteorological Observations carried out on the test site during the two Shuttle missions. Tagether with the theoretical backscattering calculations presentecl in chapter 6 these data comprise the basis for signature i nterpretation. Surface aud volume scattering models give insight i nto the scattering mechanisms occurring in snow covered terrain and the causes of spati al and temporal variabi lity of backscattering.

The variation of the SAR derived backscattered signal with the frequency, inciclence angle, polarization, and target prop erties is analyzed in chapter 7. The analysis focuses also on seasonal and short term changes of the elements of the covariance matrix.

Chapter 8 addresses classification procedures based on polarimetric multi frequency data. At first the main surface classes to be discriminated are defined. The complexity of the data available for each pixel requires the elimination of redundant i nformation. The significant components of the feature vector for classification are selected from depolarization and spectral ratios of backscattering coefficients and correlation coefficients. The separability between surface classes is calculated. Three classification methods are app lied: Maximum Likelihood Classifier, segmentation of multi temporal ratios, and hierarchical classification. The results of the different algori thms are compared with each other and with glaciological field observations.

Finally, chapter 9 contains a summary and conclusions of this work and answers the questions raised above.

Chapter 2

Sensor characteristics and available data

2.1 The SIR-C/X-SAR radar system The Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/XSAR) imaging radar system was fiow n aboard the Space Shuttle Endeavour i n two missions from 9 to 18 April 1994 (Space Radar Laboratory-1 , SRL-1) and from 30 September to 1 1 October 1994 (SRL-2) . SIR-C/X-SAR is the first spaceborne SAR capable of obtaining simultaneaus multifrequency, polarimetric radar imagery. The SIR-C, developed at NASA/ JPL, is a polarimetric SAR operating at 1 . 25 GHz (Lband) and 5 .3 GHz (C-band) . The X-SAR, a joint project of the Deutsche Agentur für Raumfahrtangelegenheiten (DARA) and the Italian Space Agency (ASI) , operates at 9 .6 GHz (X-band) with single polarization, vertical transmit and receive (VV) . SIR-C and X-SAR were designed to op erate synchronously, collecting data over common sites. Some characteristics of the SIR-C/X-SAR system are summarized in table 2 . 1 [Jordan et al. , 1995] , [Zink and Bamler, 1995] .

The resolution depends on the acquisition mode of the SAR. The slant range resolution is variable in that the pulse bandwidth is selectable from three and two values for SIR-C and X-SAR, respectively. In azimuth the required resolution was 30 m.

The SIR-C antenna is an active phased array with the cap abi lity for electronic beam steering and multiple swath width illumination. X-SAR has a slotted waveguide antenna mechanically steerable in elevation. The SIR-C and X-SAR antennas were mounted in the Shuttle Endeavour cargo bay in a common structure measuring 12 m in azimuth by 4 m across track (figure 2 . 1 ) . Part of the cross track dimension (3.7 m) is shared between C- and 1-band antenna panels proportionately to thei r w avelength which results in approximately equal beam widths in elevation. By prop erly phasing the energy from the transmitters, the beam can be electronically steered in the range direction from the nominal 40° off nadir p osition without p hysically moving the large radar antenna, thus allowing imaging at look angles from 20° to 55° [Jordan et al . , 1995] . Because of the large number of transmit/receive (T/R) modules ( 126 for the 1-band antenna and 252 for the C-band antenna) loss of a few elements has a minor impact and the system is highly reliable. During SRL-1 one

4

Chapter 2. Sensor characteristics and available data

Parameter SIR-C ( after

5

X-SAR ( after [Jordan et al. , 1995] ) [Zink and Bamler, 1995] )

Frequency C- and L-band X-band Polarization HH,HV,VH,VV vv Orbital altitude 225 km Look angle range 20° to 55° from nadir Azimuth resolution1 6.2 to 8. 1 m 6.3 to 7.9 m Slant range resolution1 1 1 .3 to 27.2 m 7.3 and 14.7 m Pulse bandwidth 10, 20, or 40 MHz 10 or 20 MHz Transmit pulse length 33, 17 or 8.5 J.LS 40 J.LS PRF rangc 1240 to 1736 Hz Data rate 90 Mbps 45 Mbps Data format 8,4 bitsjword or (8,4) BFPQ 4 or 6 bits I /Q Swath width 15 to 90 km 20 to 70 km Total instrument mass 1 1000 kg DC Power consumption 3000 to 7500 watts

Table 2.1: SIR-C/X-SAR system characteristics. BFPQ block fioating point quantization; 1/Q in-phase and quadrature acquisition mode. 1 Azimuth and range resolutions correspond to single look data from the test site Ötztal.

C-band panel, consisting of 14 T /R modules [Elachi, 1988] , failed and during SRL-2 two C-band panels partially failed [Freeman et al. , 1995] .

The orbit inclination for both SIR-C/X-SAR flights was about 57° at an altitude of approximately 225 km. The altitude was selected to generate a slight day-to-day westward drift. Thus, during one mission, the same site could be imaged at different incidence angles, on ascending and descending passes. When the SIR-C system is operating in its polarimetric mode, the maximum incidence angle must be limited to about 48° bccause of range ambiguities when operating at twice the nominal pulse repetition frequency [Jordan et al . , 1995] .

The acquired data were stored on magnetic tape on-board. Some SIR-C and X-SAR data takes were downlinked to JPL during the missions. They were used

The SIR-C/X-SAR antenna 12m

�� ----- C-band panels

y ----- X-band antenna

Figure 2.1: Sketch of the SIR-C/X-SAR antenna. The C-band aperture contains a row of 18 panels. The L-bancl aperture, located in the center, contains two rows of 9 panels each. The X-band aperture contains three subpanels, each 4 m in length.

Cllapter 2. Sensor cllaracteristics and available data 6

to check the system performance and, after calibration, they were delivered to the investigators to perform real-time science. For the test site Ötztal snow and glacier ice classification was performed during SRL-1 [Stofan et al. , 1995] (fig. 4) and SRL-2 . SIR-C data from the two SRL fiights were processed at the Jet Propulsion Laboratory; X-SAR data were processed at the Processing and Archiving Facilities in Germany and Italy, D-PAF /DLR and I-PAF, respectively. The data products are of two types. The survey product is intended to proviele a fast image to allow the investigators to survey the data acquired. The high precision data products are used for studies in geology, ecology, hydrology, oceanography and electromagnetic theory.

During each SRL mission 18 supersites and 380 test sites located all over the world were imaged at different incidence angles. One of the hydrology supersites was Ötztal , Austria. A short description of the site is given in section 5 . 1 .

2.2 SIR-C/X-SAR data of the supersite Ötztal, Austria

Some specifications about the data acquired over Ötztal are presented in table 2 .2 . SIR-C has 23 acquisition modes. For Ötztal data the modes 16 (fully polarimetric or quad-pol) and 1 1 (dual-pol, with HH and HV polarizations) were selected, both accompanied by X-SAR mode (x) . The data takes from ascending passes (A) have a south (right) radar looking direction, those belonging to descending passes (D) have a north (left) radar looking direction, as illustrated in figure 2.2. All the data takes were acquired within the first 5 days of the missions. During the first mission the two descending data takes, 18 .21 and 34.31 were lost for SIR-C due to technical problems with overlapping data takes ( data takes spaced 10 seconds apart) . The orbits during SRL-1 and SRL-2 were nearly the same, therefore seasonal changes in the imaged sites can be derived from repeat pass data takes. For the test site Ötztal the incidence angles at the swath center on a horizontal surface vary between 36.0° and 58.0° .

I nformation about the SIR-C and X-SAR products processed for the test site Ötztal ancl used for this study are presented in tables 2 .3 and 2.4. The SIR-C high precision products provided by JPL are absolutely calibrated, Single-Look Complex (SLC) data in slant range geometry, scattering matrix format (see chapter 3) compressed in 10 bytes per pixel ( quad-pol) or 6 bytes per pixel (dual-pol) . These data have been block averaged in range and azimuth directions to obtain approximately square ground pixel sizes (see table 2 .3) . Because the SLC data are oversampled, the effective number of looks (Lef 1) differs from the number of averaged pixels. After averaging, the data become Multi-Look Complex (MLC) , symmetrized (a�v = a�h), in Stokes matrix format, with 10 and 5 bytesfpixel for quad- and dual-pol data respectively, and still in slant range geometry [Chapman, 1 994] .

For simplicity the data takes (DT) over the test site Ötztal will be abbreviated as 14 , 18, 34, 46, and 78 with the specification SRL-1 or SRL-2.

The X-SAR high precision data products used in this study are summarized in table 2 .4 . The pixel spacing of the Single-look Slant range Complex (SSC) X-SAR images is the same as for the SLC SIR-C for the corresponding data take. Therefore


Ä

7

Figure 2.2: SIR-C/X-SAR ascending and descending fl.ight directions and look directions over the test site Ötztal.

Data Orbit Look Date T ime Heading I ncid. Acq. Take Dir. Dir. UT C [0] Angle [0] Mode SRL- 1 14. 2 A s lO.Apr. 06:32 53 .8 36.0 16x 18.21 D N lO.Apr. 12:42 132. 2 44.8 -x 34.3 1 D N 1l .Apr. 12: 24 133 . 1 52.8 -x 46. 1 A s 12.Apr. 05:55 55.6 50.4 16x 78.0 A s 14.Apr. 05: 16 57.5 58. 1 l lx SRL-2 14.2 A s l .Oct. 06:41 53 .7 37 .2 16x 18 .21 D N l .Oct. 12:50 132.5 45. 1 16x 34.3 1 D N 2.0ct. 12:32 133 .4 52.0 llx 46.0 A s 3 .0ct. 06:02 55.5 50.3 16x 78. 1 A s 5.0ct. 05:23 57.0 58.0 llx

Table 2.2: Acquisition of SIR-C/X-SAR data of the test site Ötztal. A-ascending orbit, D-descending orbit. S-south looking, N-north looking. 16x fully polarimetric SIR-C acquisition mode and X-SAR, llx dual polarization SIR-C and X-SAR, x only X-SAR.

Cl1apter 2. Sensor characteristics and available data

SIR-C SLC products Ötztal Data Product CEOS Pulse Nom. Pixel Take icl. writer Band- resol. spacing

L-/C- software width rgxaz rgxaz band verswn [MHz] [m2] [m2 ]

SRL- 1 14.2 11592/93 1.2.7 10 27.2x6.2 13.3x4.2 46.1 11430/31 1 .2.7 10 20.7x6.2 13.3x4.5 78.0 11511/12 1.2.7 10 18.8x8.1 13.3x5.2 SRL-2 14.2 41955/56 1.3.0 10 27.0x6.2 13.3x4.2

18.21 41357/58 1.3.0 20 11.3x6.8 6.7x4.9 34.31 41355/56 1.3.0 10 20.3x7.4 13.3x5.0 46.0 41353/54 1.3.0 10 20.8x6.2 13.3x4.5 78.1 41182/83 1.2.7 10 18.8x8.1 13.3x5.2

8

1No.of 1Ground 1Lett pixels pixel size aver. rgxaz rgxaz

rgxaz [m2]

2x10 45.3x41.8 6.6 2x8 34.6x35.8 7.5 3x9 47 .1x46 .8 1 2.3

2x10 44.0x41.8 6.6 4x8 37.7x39.0 13.7

3x10 50.7x50.3 13.3 2x8 34.6x35.8 7.5 3x9 47.1 x46.8 12.3

Table 2.3: A vailable SIR-C data of the test site Ötztal. SLC-Slant range Single Look data; Let t the effective number of Iooks. 1 Ground pixel size and Let t correspond to the number of averagecl pixels which was chosen for image display and analysis so that approximately square ground pixel sizes are obtained.

X-SAR products Ötztal (SRL-1 and SRL-2) Data Prod. Pulse Nom. Pixel Prod. Nom. Pixel Take Type Band- resol. spacmg Type resol. spacmg

X-SAR width rgxaz rgxaz rgxaz rgxaz [MHz] [m2] [m2] [m2] [m2]

14 ssc 10 14.7x6.3 13.3x4.2 MGD 29.5x 25.0 12 .5x 12.5 18 20 7.3x6.7 6.7x4.9 & 25.0x25.0 12 .5x 12.5 34 10 14.7x6.7 13.3x4.9 GTC 25.0x 25.0 12 .5x 12.5 46 10 14 .7x6.3 13.3x4.5 25.0x 25.0 12 .5x 12.5 78 10 14.7x7.9 13.3x5.2 25.0x25.0 12 .5x 12.5

Table 2.4: X-SAR products used and their characteristics. SSC-Slant range Single Iook Camplex clata, MGD-Multilook Ground Detected data, GTC-Geocoded Terrain Corrected clata


DT 14 18 Bc[0] 36.0 44.8

layover [%] 4 .1 2.4 shadow [%] 1 .6 4.0

9

46 78 50.4 58. 1 0.8 0.2 1 1 . 1 28.6

Table 2.5: The extent of the layover and shadow zones for the various data takes acquired over Ötztal; Be is the incidence angle at image center

for multifrequency analysis, performed in slant range geometry, SSC X-SAR absolutely calibrated data were used. The multitemp oral studies at X-band in chapter 7 are based on Geocoded Terrain Corrected (GTC) images, which are derived from Multilook Ground range Detected (MGD) image products. The geocoding was p erformed with the software package GEOS at D-PAF. For geocoding the Ötztal data, a DEM of 25 m x 25 m resolution was provided by the Institute for Meteorology and Geophysics, University of Innsbruck, and was implemented in the GEOS software database. Additional to the GTC image product local incidence angle maps, masks for layover, and shadow regions are provided for each data take (GIM product) .

Because for SIR-C data geocoded terrairr corrected products are not available and in order to preserve image statistics all the calculations at C- and L-band are p erformed in slant range geometry. Using the orbit points given in the CE OS header of the SIR-C data, orbit p olynomials of second order are calculated for each data take. The seven corner refiectors deployed on the test site (figure 4. 1 ) are used to adjust t. he orbit p olynomials. Using the high precision DEM local incidence angle maps, layover and shadow masks are simulated in map projection and then transformed into radar geometry. For these transformations the Remote sensing Software package Graz (RSG) , developed at the Institute for Digital Image Processing Joanneum Research, Graz, was used.

Amp litude images, local incidence angle maps, and shadow and layover masks in radar geometry are shown for different data takes (figures 2.3 , 2.4, 2 .5 , and 2 .6 a to c) . In t.able 2.5 the areas of layover and shadow, calculated in % of the image, corresponding to these masks are presented. For DT 14 (figure 2.3) , which was viewed with the steep est look angle, layover zones cover 4 . 1% of the image and shadow regions only 1 .6% . In the image corresponding to DT 78, acquired at the highest look angle, the layover is almost nonexistent whereas large parts of the scene belong to shadow zones. Shadow and layover areas are not used in classification and signature analysis. The shadow masks are used in section 4. 1 .5 to estimate the additive noise levels at C- ancl L-band.

Geometrie consistency between SRL-1 and SRL-2 images as well as between SIRe ancl X-SAR images are important for observing seasonal variations a.nd frequency clependences of backscattering. As control points the corner refiectors deployed on t.he test sit.e are available. The relative shift. of the master and t.he slave images is calculatecl in ra.nge and azimuth by using the cross-correlat.ion of a data window centered at. the cont.rol p oint. The precision of the method is 0 . 1 pixel. The coregistra.t. ion bet.ween the SIR-C images from DT 14, 46 and 78 SRL-1 and SRL-2 ancl between SIR-C and X-SAR images corresponding to DT 46 SRL- 1 and SRL-2 is good as shown in t.ables 2.6 and 2. 7. A large shift is obtained for t.he refiectors H1 and H2 beca.use t.heir positions in SRL-1 a.nd SRL-2 did not coincide.

Chapter 2. Sensor characteristics and availabJe data

� ;e

10

�1------:11� ...... .:;:::: look dir.

Figure 2.3: DT 14 SRL-2 C-band HH amplitude image of the test site Ötztal in radar geometry (a), local incidence angle map (b), layover (in white) and shadow (in black) masks ( c). The grey Ievels of the amplitude image and local incidence angle map are proportional to the backscattering and incidence angle, respectively. Cantrast enhancement functions have been applied to the (a) and (b) images.


Figure 2.4: As figure 2.3 but for DT 18.

SRL-1 and SRL-2 Corner DT 14 DT 46 DT 78

refiector Rg. shift Az. shift Rg. shift Az. shift Rg. shift Az. shift [pixels] [pixels] [pixels] [pixels] [pixels] [pixels]

H1 0.7 1 .7 -0. 1 4 . 1 0.8 1 .0 H2 0.2 3 .2 -0.4 4 .9 0 . 1 2.0 K 1 0.0 -0.8 0.0 -0 . 1 0.4 -1 .3 K2 0 .0 - 1 . 1 0.0 0.3 0.2 -1 .3 K3 0.0 0.8 -0. 1 0.0 0 . 1 - 1 .6 K4 0.0 0 .0 0.0 -0. 1 0 .2 0 .2 K5 0 .0 0 .3 0.0 0 . 1 0 . 3 -0. 1

1 1

Table 2.6: The relative shift between SRL-1 and SRL-2 images derived from spatial crosscorrelation using the response of the corner reflectors.



1-i :.a

12

��----� ...... l;::l look dir.

(a)



� :.e

13

�t---"""'iill� C8 Iook dir.

(a)

Chapter 2. Sensor characteristics and availabJe data

SIR-C and X-SAR DT 46 Corner SRL-1 SRL-2

refiector Rg. shift Az. shift Rg. shift Az. shift [pixels] [pixels] [pixels] [pixels]

H1 -0. 1 -1 .6 0 .0 1 .2 H2 -0. 1 - 1 . 1 0 .0 1 .4 K1 -0. 1 0 .0 -0 . 1 0 .3 K2 -0. 1 0.0 -0. 1 0 .2 K3 -0. 1 0.0 -0. 1 0 .0 K4 -0. 1 -0.2 0.0 0 .0 K5 -0. 1 -0.2 -0 . 1 -0 . 1

14

Table 2.7: The relative shift between SIR-C and X-SAR DT 46 SRL-1 images and between SIR-C a.ncl X-SAR DT 46 SRL-2 images derived from spatial correlation using the response of the corner reftectors.

Chapter 3

Background of radar polarimetry and basic definitions

Scattering calculations are performed in two types of coordinate systems, the forward scatter alignment (FSA) convention and the backscatter alignment (BSA) convention [Ula.by and Elachi, 1990] . In both cases, the electric fields of the incident and scattered w aves a.re expressed in local coordinate systems centered on the transmitting and receiving antennas, respectively. The global coordinate system is centered inside the scatterer in both conventions. The difference is in the definition of the polarization unit vectors. In the FSA convention they are defined relative to the propagating wave, whereas in the BSA convention they are defined w ith resp ect to the radar antennas. This means that, for the BSA convention, the polarization state of the antenna is the polarization of the wave radiated by the antenna, even w hen it is used as a receiving antenna.

The FSA convention is used in connection w ith problems involving wave scattering by pa.rticles and wave propagation in inhomogeneaus media, therefore the indices used are i for " incident" wave and s for "scattered" wave. The BSA convention is preferred in calculating the radar backscatter from a given target or medium, and the indices used are t for "transmitting" and r for "receiving" antenna orientation. The i coordinate system and the t coordinate system are identical.

In this chap ter the propagation and scattering of the electromagnetic wave a.re clescribed in the FSA convention, whereas for the definition of the backscattering coefficients and other polarimetric measures derived from polarimetric SAR data the BSA convention is used.

3.1 Mathematical representations of scattering

An elliptically polarized monochromatic p lane wave propagating in the direction k can be represented as:

(3 . 1 )

Consider a scatterer illuminated by an electromagnetic p lane wave with incident electric field ßi. The scattered field ßs at the distance R from the scatterer is given

15

Chapter 3. Background of radar polarimetry and basic definitions

by:

16

(3 .2)

where k is the wave number and Sqp (q,p = h or v) are the complex elements of the target scattering matrix. For a given frequency each element of the scattering matrix is a function of the scattering and illuminating angles and the orienta tion of the scatterer relative to the coordinate system. This means that each element of the scattering matrix can be written as

(3.3)

where the pairs (Bi , rA) and (88 , cf>s) define the direction of the incident and scattered wave, respectively, and (Bi, cpi) angles define the orientation of the sca tterer.

The polarization state of a plane wave may be a lso characterized by the Stokes vector or Stokes parameters defined as:

(3.4)

where '1/J is the orientation and x the ellipticity angle of the polarization ellipse (figure 3 . 1 ) and * derrotes complex conjugate. If the incident electromagnetic wave is expressed in terms of the Stokes vector, §i , the scattered wave is given by

(3.5)

where [1\11] is the 4x4 Stokes matrix (a lso ca lled the Mueller matrix) with rea l elements, and 1/ R2 appears because §s must be defined for a spherica l wave [Fung, 1994] . The Stokes matrix is provided by [Ishimaru, 1978] :

Re(Shhshv) Re(Svhs:v)

Re(Shhs:v + Shvs:h) Im(Shhs:v + Shvs:h)

If the plane wave is polychromatic the Stokes parameters should be expressed as averages. For a partia lly polarized wave the degree of polarization is defined:

d = J Q2 + u2 + y2 --=------�--- (3.7)

where d = 1 for elliptic polarization, 0 < d < 1 for partia l polarization, and d = 0 for an unpolarized wave.

The elements of the scattering matrix may be given in a polarimetric feature vector:

Chapter 3. Background of radar polarimetry and basic deflnitions

Äv

17

Figure 3.1: The shape of the polarization ellipse is determined parametrically from wave amplitucles in the h ancl v directions. The ellipse can be described by the ellipticity angle x ancl the orientation angle '1/J.

where + clenotes transpose. The 4x4 polarimetric covariance matrix is defined as [Swartz et a l . , 1988]

[ C] = E[ XX*+]

where E [ ] denotes the expected value. Performing the multiplica tions, we find tha t

(3.8)

3.2 Backscatter measurements and polarization

synthesis

The SIR-C polarimetric radar records the amplitude and relative phase of the scattered wave a t four linear polarizations hh, hv, vh, and vv and at two frequencies almost simultaneously. For each resolution element in the radar image the complex scattering matrix is measured. In this section the definitions are related to the radar system, thus the BSA convention is used.

The relation between the scattering matrix and the received power measured a t the antenna with far-zone electric field vectors ßt and ßr for transmit and receive, respectively, was established by [van Zyl et a l . , 1987] :

(3 .9)

Chapter 3. Background of radar polarimetry and basic definitions

be expressed in terms of the Stokes or covariance matrix: .... r+ .... t Prt = K(>-., Bt, c/Jt) · S [ MJPL]S

Prt = K(>-., Bt, c/Jt ) · W*+[ C]W

18

(3. 10)

(3. 1 1 )

where [ M.JPL] is the 4x4 Stokes scattering operator or the JPL Stokes matrix with elements rnij, and W is a complex vector with elements related to the electric fields of the transmitting and receiving antennas as fo llows

w+ = ( Er Et Er Et Er Et Er Et ) h h h v v h v v The relation between the Stokes matrix defined in FSA convention, [ M] , ( equation 3.6) and the Stokes scattering operator in the BSA convention, [ MJPL] , i s given in [Ulaby and Elachi, 1990] . The received power may a lso be expressed as the bistatic scattering cross section of an iso lated scatterer which is defined as:

1. R2 Prt (Jrt = Im 47r o R-+oo .L transm

where Ptransm/ 47r R2 is the transmitted power density. For a distributed target, which is considered to be composed of a very large number of statistica lly identical targets, the problern must be reformulated in terms of the average received power [Ulaby et al., 1982] . The scattering coefficient can be cxpressed as [Ulaby and Elachi, 1990]

(3. 12)

where rJ�1t) is the scattering cross section of the nth measurement, A the illuminated area a nd 0 denoting ensemble averaging, and the angles (Xr, '1/Jr) and (Xt, '1/Jt) are the orientation and the ellipticity angles of the receiving and transmitting antenna polarization ellipses, respectively. In the equations 3 .9 , 3 . 10, and 3 . 1 1 the total power is then expressed as a sum of N measurements and, assuming that K is constant, the matrices must be replaced with their average.

Equatio n 3 .9 shows that , if the scattering matrix is known, the ca lculatio n of the received power for any desired transmit and receive antenna polarizations is possible. This process is ca lled polarization synthesis.

Although the equations 3 .9 , 3 . 10, and 3. 1 1 provide the same information, the Stokes and covariance matrices have the advantage that the collective properties of a group of pixels can be expressed using a single matrix rather than individual scattering matrices for each pixel. In addition, the three different approaches require different amounts of computation time for the polarization synthesis. The Stokes matrix method requires fewer computations than the scattering matrix and covariance matrix methods. For applications which require maximum reso lution the scattering matrix representation is preferable.

All the above discussion is applicable to any radar imaging geometry. Certa in simplifications are possible in the case of monostatic radar (transmit and receive antennas are approximately collocated in position) , which is the operating mode of SIR-C. In this case the reciprocity principle of electromagnetic theory dicta tes that Shv = Svh· If [S ] is symmetrica l [ MJPL] is a lso symmetrical and for distributed scatterers there are 9 independent parameters in the Stokes scattering opera tor . The covariance matrix [ C] can be reduced to a Hermitian 3x3 matrix.

Chapter 3. Background of radar polarimetiy and basic definitions

Polarization 7/Jt Xt 7/Jr Xr [0] [0] [0] [0]

HH 0 0 0 0 vv 90 0 90 0 HV 0 0 90 0 VH 90 0 0 0

19

Table 3.1: Commonly used transmit and receive polarizations and the corresponding orientation, '1/J, and ellipticity, X angles in degrees. H is horizontal polarization, V is vertical polarization.

3.3 Polarimetrie measures

The SIR-C ca libration processor only supports data in sca ttering matrix format, not symmetrized, ca lled SLC data . (section 2 .2) . For applications like signature study or classification the data have been block averaged in order to reduce their amount and to obtain approximately square ground pixels. After averaging the data are symmetrized so that Shv = 0.5(Shv + Svh) and stored as cross products between scattering matrix elements.

In order to synthesize any transmit and receive polarization from quad-po l SIR-C data , the fo llowing operation is performed [Chapman, 1994] :

In table 3. 1 the orientation and ellipticity angles necessary to ca lculate linear coand cross-polarized backscattering coeffi.cients are given. The elements of the symmetrized [ MJPL] may be formed from the cross pro ducts o f the scattering matrix with the fo llowing equations:

mu 0.25(IShhl2 + ISvvl2 + 2 IShvl2 ) (3. 13a)

m12 - 0.25(1Shhl2 - ISvvl2 ) (3. 13b)

m13 0 .5 (Re(ShhS�v ) + Re(ShvS�v ) ) (3 . 13c)

m14 - 0 .5(- Im(Shhs,:v ) - Im(Shvs:v ) ) (3. 13d)

ffi22 0.25(IShhl2 + ISvvl2 - 2IShvl2 ) (3 .13e)

ffi23 0.5 (Re(ShhS�v )- Re(Shvs:v ) ) (3. 13f)

m24 0 .5(- Im(ShhS�v ) + Im(ShvS�v ) ) (3. 13g)

m33 - 0.5(IShvl2 + Re(Shhs:v ) ) (3. 13h)

m34 0 .5(- Im(Shhs:v ) ) (3 . 13i )

m44 0 .5 (IShvl2- Re(Shhs:v ) ) (3. 13j )

After averaging and symmetrization the polarimetric covariance matrix defined by equation 3.8 becomes:

Chapter 3. Background of radar polarimet1y and basic definitions

[ C]� (

20

(3.14)

For azimuthally symmetric targets the co- and cross-polarized elements of the scattering matrix are uncorrelated, i .e. ( SiiS0) = 0 if i =J. j, and the covariance matrix will have 4 zero elements:

[ C]� ( � I o o * V (J hh(J vvPhhvv

V "-�h��Phh"" ) (Jvv

(3.15)

The complex correlation coe.fficient between the two like-polarized measurements is defined as:

Phhvv = (3.16)

The magnitude of the HHVV complex correlation coefficient for a group of pixels takes values between 0 and 1 and may be calculated from the elements of the averaged Stokes scattering Operator over that group of pixels:

lts phase takes values between 0 and 21!' and is given by:

( 2 (m34) ) c/Jhhvv = arctan -

( ) ( ) m33 - m44

In this study the total power (span) is used as a normalization factor:

(3.17)

(3.18)

(3.19)

In classifications the degree of polarization for vertical ( or horizontal) incident polarization is used. This is calculated as the degree of polarization ( equation 3 . 7) of the received Stokes vectors:

§r ( [ MJPL ] ) . §t ('l/Jt = 90°, Xt = 0°) for V incident polarization

§r - ( [ MJPL] ) · §t ('l/Jt = 0° , Xt = 0°) for h i ncident polarization

(3.20)

(3 .21)

The ratio between cross- and co-polarized backscattering coeffi.cients is called the depolarization factor, rr�v/rr�h , and the ratio between two co-polarized backscattering coeffi.cients , (J?th/(J�v , is called the co-polarization ratio.

Chapter 4

Calibration and quality of SIR-C /X-SAR data

SIR-C acquires data in all linear polarization configurations (HH, HV, VH, VV) , at two frequencies (L- and C-band) , three pulse bandwidths (10 , 20 and 40 MHz) , different pulse repetition frequencies (between 1240 and 1736 Hz) , and has 19 acquisition modes. All these options have an impact on the data quality and the calibration of the SIR-C system [Freeman et al. , 1995] . Generally, a serious source of calibration errors is the SAR antenna. This is particularly true for the SIR-C radar which has an active phased array antenna with hundreds of phase shifters and transmit/receive (T /R) modules inserted in the feed system to improve system SNR ancl to proviele electronic beam steering. It is clifficult to characterize these clevices inclividually. Compared to a passive antenna an active phased array antenna is not necessari ly reciprocal. For spaceborne systems temperature variations of the antenna are about 40° C and may cause changes in phase and amplitucle of the antenna elements.

X-SAR operated in parallel with SIR-C at 9.6 GHz and VV polarization. From prefiight studies the sensitivity of the receiver gain clue to temperature variations ancl shuttle attitude uncertainties, which cause insufficient knowleclge of the antenna boresight direction, were iclentified as the main radiometri c error sources. During the missions the Shuttle's attitude control and information system are better than specified. The receiver gain was very stable because of the low temperature variation in the cold-plates. Thus the sensor showed more stability than expected.

For the test site Ötztal absolutely calibrated products are available. At the beginning of this chapter the mathematical model of a polarimetric SAR image is presented . In the following sections aspects of the internal calibration are discussed, parameters published and annotated in the SIR-C CE OS header are compared ancl the sources of uncertainties in the radiometric calibration are presented. The accuracy of the external calibration of the polarimetric system is testecl based on the triheclral corner refiectors deployed on the test site Ötztal during SRL-1 and SRL-2 (figure 4. 1 ) .

The SIR-C image quality parameters are calculated from the reflected power of the triheclral corner reflectors. Sources of additive noise in the SIR-C system are presentecl and the thermal noise level in the available image products is determined. For X-SAR data the calibration equation is given and the additive noise level is

21

Chapter 4. Calibration and quality of SIR-C/X-SAR data 22

Figure 4. 1 : SIR-C DT 18.21 C-band amplitude image of the test site Ötztal in raclar gcometry, acquirecl on 1 October 1994. The corner reftectors cleployecl on the glaciers can be clearly observecl due to the low backscattering of the wet snow. K1 , . . . ,K5 corner reftectors on Kesselwandferner; H1 ancl H2 corner reftectors on Hintereisferner.

briefiy discussed .

4.1 SIR-C data

4.1.1 Mathematical model

4.1.1.1 Radiometrie calibration

In [Freeman et al . , 1995] a complex SAR image is theoretically described by the following equation:

where V represents the measured voltage at p transmit and q received polarization, x ancl y the pixel coordinates within the image, Sqp the desired sc attering matrix measurement, h the impulse response func tion, and ® the convolution in x ancl y. Ks ancl </Js are the gain and phase imposed by the radar on the backsc atter measurement. nqp(x, y) represents the additive noise, Kn the radar receiver and processor gain in the presence of noise.

The behavior of Ks is modelled as a function of polarization and frequency,

K _ PtG�(eet, eaz)G�(eet, eaz)>.2G�Gp(x, y) s- (41r)3R4LsLa

(4.2)


where Pt is the peak of the transmitted power, Gf (Bet , Baz) and G� (Bet , Baz) are the antenna gains on transmit and receive, which depend on the elevation angle, Be1 , and azimuth angle, Baz , ).. is the radar wavelength, G� is the electronic gain in the radar receiver, Ls is a system loss term, which takes the cable losses into account, La describes the propagation losses through the atmosphere, R is the range delay, and Gp is the processor gain. The loss due to the atmosphere is assumed zero for C- and 1-band [Freeman et al. , 1995] .

The antenna gains are functions of the antenna temperature and the bandwidth of the transmitted pulse, and are different for each polarization and frequency. The receiver gain also depends on the receiver temperature. During Operation different receiver gains were used for like- and cross-polarized returns. The electronic delay through the system is a function of polarization, frequency and bandwidth. It must be removed when the range delay is calculated.

In the model of Ks are also included: the electronic steering angle of the antenna, the radar polarization, frequency, the bandwidth, the pulse repetition frequency, the pulse length and the mode of the data take. The variations in azimuth of Ks are negligible and only corrections for range dependent terms must be applied. The radiometric correction is applied to the full resolution complex data after SAR processing.

4. 1. 1 .2 Polarimetrie calibration

Once the radiometric calibration is performed the polarimetric measurements of the SIR-C system can be represented by the following model [Freeman, 1992] :

where A is a residual absolute calibration factor, o1 and 02 are the HV and VH crossta.Ik ( or polarization impurity) terms on receive o3 and 04 are the corresponding terms on transmit . The cross-talk terms represent contamination resulting from the crosspolarized antenna pattern, as weil as poor isolation in the transmitter switchers and circulators [Curlander and McDonough, 1991] . fi and h are complex numbers representing channel imbalance terms between the H and V channels on receive and transmit respectively. lgnoring the phase term eJciJ., the polarimetric calibration of NI consists of four steps:

• symmetrization of the cross-polarization measurements.

• cross-talk removal and calibration (estimate and correct oi , with i = 1 , . . . , 4) .

• channel gain imbalance ( estimate and correct the amplitude and phase of !I and f2) : a correction of the residual offsets in the various polarization channels is made.

• absolute calibration ( estimation of the A term) : after the correction of absolute gain of the radar system is performed, the backscatter coeffi.cient for each pixel at all polarization states may be measured.

Chapter 4. Calibration and quality of SIR-C/X-SAR data

4. 1 . 2 Radiometrie correction

The calibration equation given in the CEOS header description is:

(I) = K . 0"0 + KN (R) . (N)

24

(4.4)

where (I) is the expected value of the image pixel power, K the absolute calibration constant (K = 1 for SSC data) , KN(R) the noise gain of the processor as a function of range R and (N) raw data noise power per sample ( (N) = 81281 .25 data number units) . For the available data sets KN (R) is not annotated in the Radiometrie Data Rccord, only the processor gain for noise data before applying the radiometric correction is given (KN,o = 1 .0909525) . An estimation of the thermal noise level in SIR-C data is presented in section 4 .1 .5 .

The supplied products are corrected for incidence angle effects by modeHing the Earth as an ellipse. In order to obtain correct values of the backscattering coefficient, the processor incidence angle (Bp) must be replaced with the local incidence angle (Bi) for each pixel by multiplying a0 by sin Bd sin Bp. Local incidence angle maps of the test site Ötztal are obtained for each data take using orbit parameters and a 25 m resolution DEM (see chapter 2) .

The SIR-C data are radiometrically calibrated according to the mathematical model presented in subsection 4. 1 . 1 . 1 . The data may still have range uncertainties in the following situations [Freeman et al. , 1995] :

• the electronic steering angle exceeds ± 17.5° ,

• the uncertainties in the shuttle roll angle are of the order of 0. 1-0.2° ,

• there are significant terrairr variations within the scene and the assumed constant scene altitude is in error.

For the data takes acquired over the test site Ötztal the range electronic steering angle was less then ±8. 7° for the quad-pol data and equal to 15.68° for DT 78.0 and 78. 1 (dual-pol) .

As described in chapter 5 the test site has large altitude variations from 1800 m to 3700 m a.s.l . The constant scene altitude used in processing should be 2500 m but some data takes were processed at 3599 m. The radar look angle relative to the vertical is the angle between the radar beam and the normal to the earth's surface at the point of interest. After [Holecz, 1994] the radar look angle for spaceborne SAR is determined as:

_ { R2 + (RE + H)2 - (RE + z)2 } "f - arccos 2R(RE + H)

(4.5)

where "f is the look angle, RE is the Earth radius for a given latitude c/J, z is the topographic altitude, R is the platform distance at the image center H is the geodetic altitude of the Shuttle above the ground. An altitude error t:lz = 1 100 m will cause, according to equation 4.5 , a 0 .01 o error in the look angle of the antenna.


HV and VH channel imbalances C-band Ir-band

BW ampl. phase ampl. [MHz] [dB] [0] [dB]

10 -1 .4±0.2 183±6 -0.4±0.2 20 -1 .9±0 . 1 169±5 -0.7±0.2

25

phase [0]

66±2 45±4

Table 4. 1 : Residual values for amplitude and phase of HV VH channel imbalances, published by [Freeman et al. , 1995] .

C-band L-band BW 1! 1 arg(!) 1 ! 1 arg(!)

[MHz] [dB] [0] [dB] [0] SRL-1 10 -3 .3 180 -0.3 -48 SRL-2 10 -3.2 180 -0. 1 -48 SRL-2 20 -3.8 167 -0.8 -49

Table 4 .2 : Amplitude and phase of HH VV channel imbalances of the SSC clata of the test site Ötztal, extracted from the CEOS header.

4. 1 . 3 Polarimetrie calibration

The polarimetric calibration is performed as described in subsection 4. 1 . 1 . 2. By looking at the variations in range of the HV /VH ratio the reciprocity of the radar antenna was checked. The symmetrization parameter fd h is calculated for quadpol data as described in [Freeman et al. , 1992] and applied to VH and VV measurements for symmetrization and removal of the cross-polarization channel imbalances. The HV and VH residual channel imbalances are not provided in the CEOS header, but are published in [Freeman et al . , 1995] and shown in table 4 . 1 .

The cross-talk was estimated after the method described in [van Zyl, 1990] and was found to be lower than the specification. After symmetrization of the measured scattering matrix (Mhv = Mvh) the modeled (desired) scattering matrix in equation 4 .3 must also be symmetrical (Shv = Svh) · For a real system the following relationships are obtained:

where 61 is now the cross-talk when a vertically polarized electric field is transmitted or received, 62 the cross-talk when a horizontally polarized electric field is transmitted or received, f is the one way co-polarized channel imbalance in amplitude and phase. For dual-pol data it was assumed that the cross-talk was negligible.

Symmetrization and cross-talk calibration do not need external calibration devices [van Zyl, 1990] and are performed before co-polarized channel imbalance and absolute calibration, where trihedral corner refl.ector responses are required.

Amplitude and phase values of HH and VV channel imbalances extracted from the CEOS header of available data are presented in table 4.2.

At C-band the HH-VV imbalance was checked for all trihedral corner refl.ectors with 1 .8 m size deployed on the test site Ötztal. For trihedral corner refl.ectors the


C-band SRL-1 1 8/ l arg(8f) SRL-2

DT CR [dB] [0] DT CR 14.2 H1 0.01 0 14.2 H1

K2 0.02 2 K2 K5 0 .29 2 K5

46. 1 H1 0 . 13 1 46.0 H1 K2 0 . 10 0 K2 K3 0.23 0.5 K5

18.21 H1 K2 K3 K5

Published ±0.6 ±4

26

l 8f l arg(8f) [dB] [0] 0 .21 0 .5 0 .21 0 .5 0 .28 1 .5 0 .32 12 .5 0 .41 5 . 0.40 7.5 -0.22 2 .5 0 .01 2 -0.01 1 .5 -0.04 2 ±0.6 ±5

Table 4 .3 : Residual amplitucle and phase of RH VV channel imbalances clerivecl from analysis of triheclral corner reftectors observecl in Ötztal C-band images .

following relations are valid:

and

(4.6)

arg(s,:hsvv ) = 0 (4.7)

In [van Zyl , 1990] the amplitude and phase of the co-polarized channel imbalance f are defined as: ( S S* ) 1/4

1 ! 1 = vv vv shhshh er arg(!) = 0.5 arg(S,:hsvv )cr

(4.8)

(4.9)

where the subscript er refers to the measured values for a trihedral corner refiector. For well calibrated data l f l � 1 , arg(!) � 0 and I Phhvv l cr = 1 .

The calculated residual values for amplitude and phase channel imbalance shown in table 4 .3 are all within the published calibration uncertainties ( except the residual phase for two corner reflectors in DT 46 SRL-2) , so one may conclude the data are correctly relatively calibrated. The amplitude of the HHVV correlation coeffi.cient at the peak of the corner reflectors is always 1 . The co-polarized polarimetric responses of the corner refiector Hl calculated from C-band quad-pol data are shown in figure 4 .2 .

4.1.4 Absolute calibration

The absolute calibration is defined as the accuracy of the estimate of the normalized backscatter coefficient from a pixel or group of pixels [Curlander and McDonough, 1991] . The absolute calibration of C-band SIR-C data is checked using the corner refiectors deployed on the test site Ötztal, following t.he integral method described in [Gray et al. , 1990] . The method can be applied on


1 b "0 Ql -� 0 E 0 z

1 b "0 'II � 0 § 0 z

DT 14.2 SRL-1

1 80

DT 14.2 SRL-2

1 80

Trihedral comer reflector H1 C-band

b 1 u 111 � 0 § 0 z

1 0 u Ql -� 0 E 0 z

0 0

1 0 "0 111 � 0 § 0

z

27

DT 46.1 SRL-1

DT 46.0 SRL-2

DT 18.21 SRL-2

1 80

Figure 4 .2 : Measured C-band co-polarization response of the corner reflector Hl in the absolutely calibrated SIR-C data of test site Ötztal.

Clmpter 4. Calibration and quality of SIR-C/X-SAR data 28

Figure 4 .3 : Subarca of a SAR image containing a point target . The target arca., centerccl at thc triheclral corner reflcctor, a.ncl two ba.ckground a.reas a.rc selectccl.

single- or multilook data. For quad-pol data J((J�h + (J�v)/2 imagcs were calculated from multilooked SLC data. In thc casc of dual-pol data � imagcs were uscd. The total intensity backscattered from a point targct is est.imated by intcgrating over an arca araund the point targct. Two typcs of subarcas wcrc selectecl from the imagcs (figure 4.3) : thc target arca, which includes the peak and siele lobes of the rcfiectccl power from thc corner rcfiector, a.nd one or more background areas, which contain homogeneaus rcgions in the vicinity of thc corner rcfiector.

The backscattered intcnsity from the targct area, leB , is Ncs

leB = L ik k=l

where ik is the backscatterecl intensity of the pixcl k, and Nen the number of pixels in the data window. leB includes thc refiected intensity from the corncr refiector and that. from the backgrouncl. The contribution from the background, I u , may be calculated as

Ns IB = L ink

k=l where Nu reprcsents the number of pixels in the uniform area. Thus the backscattcrccl intcnsity of the point target may be obtained:

Nen Ie = leB - -IB (4. 10) Nn If two or more homogeneaus areas arc selectecl in the vicinity of the reficctor thc last tcrm in 4. 10 represcnts the average intcnsity from these areas.

The t.hcoretical scattering cross scction of a trihedral corner refiector is givcn by [van Zyl et al . , 1992] :

(J thcory = 4: l4 { cos a + sin a ( sin c,b + cos c,b) - 2 [ cos a + sin a ( sin c,b + cos c,b) r 1 } 2 ,\

(4 . 1 1 )


z

e

X

I I I I I

' I ' -...1

l y

Figure 4.4: Definition of angles for trihedral corner refl.ectors.

where >. represents the radar wavelength, l , e, and <P are defined in figure 4.4.

29

e may be calculated as the elevation angle of the corner refiector base, ß, added to the incidence angle of the radar wave at the refiector:

where eP is the incidence angle at the range location of the corner refiector assuming an ellipsoidal Earth. eP was calculated from a linear equation based on the values of near and far range incidence angles annotated in the CEOS header. The boresight of the corner refiector, </J, is given by:

where a is the difference between the corner refiector azimuth angle and the radar fiight direction (called track angle at image center in the SIR-C CEOS header) .

The absolute calibration factor (the correlator gain) , K, is then the ratio between the measured corner refiector response and the theoretical backscattering coeffi.cient:

K = Ic . e 8r8a , with K = A2 (J theory sm i (4. 12)

where A is the calibration factor in equation 4.3, 8r , 8a are pixel spacing in range and azimuth directions and ei is the local incidence angle at the refiector when the terrain geometry is taken into account. The factor sin ei appears because the data are in slant range projection and a correction for the true scattering area is clone. The absolute calibration coeffi.cient, A, annotated in the CEOS header of the Ötztal data takes and the values for A and its peak to peak variations over the mission, published in [Freeman et al. , 1995] are shown in table 4.4.


Ötztal data Band c L c L

BW A [dB] A [dB] A [dB] A [dB] 10 :tviHz SRL-1 -0.700 -3.099 SRL-2 -0.800 -3.700 20 MHz SRL-1 - - SRL-2 -1 .300 -2.300

30

Publishecl c L

A [c!B] A [c!B] -0.7±1 .2 -3. 1±1 .3 -0.2±0.9 -0.7±1 .3

Table 4.4 : Absolute calibration coefficients for Ötztal data annotated in the CEOS heacler ancl values, published in [Freeman et al. , 1995] .

Published Band c L

8A [dB] 8A [dB] SRL- 1 ±2.2 ±2.3 SRL-2 ±3.2 ±2.0

Table 4 .5 : Absolute calibration uncertainties for SRL-1 ancl SRL-2 clata publishecl in [Freeman et al. , 1995] .

In this study the trihedral corner refl.ectors deployed on the test site are used to verify the accuracy of the absolute calibration of the Ötztal data at C-band. The procedure could not be applied at L-band because of the weak cantrast between the background and the refl.ectors at this frequency. Because the SIR-C data were calibrated before delivery, the absolute calibration residuals are calculated with equation 4. 12 and summarized, for the corner refl.ectors with l =1 .8 m, in table 4.6. Almost all values are lower than the absolute calibration uncertainties published in [Freeman et al. , 1995] and presented in table 4.5. Some errors may be due to poor orientation of the corner refl.ectors because this is always problematic in soft snow.

Detailed information about the corner refl.ectors, aß and atheory are presented in tables 4. 7 to 4. 13.

4.1.5 Estimation of additive noise

In polarimetric SAR data the principal sources of additive noise are [Freeman, 1993] :

1 . Analog-ta-Digital (ADC) conversion noise, which is classified as quantization ancl saturation noise.

SRL-1 C-band (Ötztal data) SRL-2 C-band (Ötztal data) DT 14.2 46. 1 78.0 DT 14.2 18.2 46.0 78. 1 CR size 8A 8A 8A CR SIZe 8A 8A 8A 8A

[m] [dB] [dB] [dB] [m] [dB] [dB] [dB] [clB] H1 1 .8 0. 15 0 .3 -0.8 H1 1 .8 1 .0 0.85 0.08 -0. 1 K2 1 .8 0. 15 0.02 -2.25 K2 1 .8 0.8 -3.5 - 1 .0 - 1 . 8 K3 1 .8 -0.65 -0 . 25 -0.25 K3 1 .8 -0.48 -0.65 -0.85 0 .2 K5 1 .8 -0 . 1 -0. 15 -3.45 K5 1 .8 -0.85 0. 1 -0.4 -0. 25

Table 4.6: Residual values of the absolute calibration constant A calculatecl from corner reftectors analysed in SRL-1 and SRL-2 images.


SRL-1 DT 14 CR l ß (}p (}i Ia: I (Jtheory NcB

[m] [0] [0] [0] [0] [m�] [pix.] H1 1 .8 17 36.9 36 0 13712.92 12 H2 1 .25 17 36.9 34 0 3189 .19 9 K1 1 . 5 17 36. 1 40 1 .3 6573.46 12 K2 1 .8 18 36.2 42 1 .7 13681 .87 12 K3 1 .8 18 36.3 42 2.7 13626.77 12 K5 1 .8 18 36.3 30 1 .7 13681 .87 12

31

Ic K [dB]

8.700 0 .3 2.369 1 .7 4.392 0.6 8 .736 0 .3 6 . 100 - 1 . 3 5.808 -0.2

Table 4. . 7: Detailed information about the calculation of the absolute calibration constant J( from DT 14 .2 SRL-1 . CR . . . corner reflector code, l . . . the size of the corner reflector, ß . . . the elevation angle of the corner reflector base, ()P . . . the incidence angle at the range location of the corner reflector assuming an ellipsoidal Earth, ()i . . . the local inciclence angle at the location of the corner reflector calculatecl from high resolution DEM, a . . . the clifference between the azimuth angle of the corner reflector ancl the raclar flight clirection, rr theory . . . t he theoretical scattering cross section of the corner reflector, Ic . . . the intensity reflectecl from Ncs pixels in the point target area. The area of one pixel in slant range projection is OaOr =11 12.9 m2 .

SRL-1 DT 46 CR l ß (}p () . • Ia: I rJtheory Ncs Ic K

[m] [0] [0] [0] [0] [m2] plX. [dB] H1 1 .8 4 51 53 0.4 13719.27 15 13.095 0.6 H2 1 .25 4 5 1 47 0.4 3190.66 15 2.580 0 .2 K1 1 .5 4 50.5 55 0.4 6616 .16 9 3 .969 - 1 . 5 K2 1 .8 4 50.6 56 0.6 13719.27 9 12.051 0 .04 K3 1 .8 3 50.6 54 0.4 13709.47 12 10.284 -0.5 K4 1.5 4 50.7 56 2.6 5575.77 9 5 . 103 0 .2 K5 1 .8 4 50.6 45 0.6 13719.27 12 9.408 -0.3

Table 4 .8 : As table 4. .7 but for DT 46. 1 SRL-1 . Area of one pixel is OaOr =953.7 m2 .

SRL-1 DT 78 CR l ß (}p (}i io: l rJtheory Ncs Ic K

[m] [0] [0] [0] [0] [m2] [pix.] [dB] H1 1 .8 -3 58.2 62 1 13708.78 12 4.488 -1 .6 H2 1 .25 -3 58.2 54 1 3188.23 9 1 .701 0 .9 K2 1 .8 -2 57.9 66 1 13676.70 9 2 .358 -4.5 K3 1 .8 -3 57.9 64 0 13722.77 12 5 .868 -0.5 K4 1 .5 -3 58.0 64 0 6617.85 16 2 . 192 -1 .6 K5 1 .8 -3 58.0 54 2 13666.90 12 1 .212 -6.9

Table 4.9 : As table 4 .7 but for DT 78.0 SRL-1 . Area of one pixel is OaOr =1869.5 m2 .

( ,'J��tpter 4. Calibration and quality of SIR-C/X-SAR data

SRL-2 DT 14 CR l ß ()p ()i io: l rJtheory Ncn

[m] [0] [0] [0] [0] [m2] [pix.] H1 1 .8 17 37.0 40 0 .3 13709.47 12 H2 1 .25 17 37.0 40 0.3 3188.38 12 K1 1 .5 17 36.2 42 1 . 2 6581 .65 12 K2 1 .8 17 36. 3 42 0 .2 13661 .32 18 K3 1 .8 16 36.4 40 0 .7 13564.85 18 K4 1 .5 17 36.5 42 2.3 6547.27 18 K5 1 .8 17 36.4 28 0 .3 13657.92 12

32

Ic K [dB]

12 .528 2 .0 2.748 1 . 7 6 .504 2 . 1 12 .006 1 .6 6 .210 -0.99 7.308 2 .6 3 .576 -1 .9

Table 4. 10 : As table 4 .7 but for DT 14 .2 SRL-2. Area of one pixel i s 8a8r = 1 1 12 .6 m2 .

SRL-2 DT 18 CR l ß ()p ()i lo: i rJtheory Ncn Ic K

[m] [0] [0] [0] [0] [m2] [pix.] [dB] H1 1 .8 9 45.0 49 0.5 13709.47 16 15 .36 1 . 9 H2 1 .25 9 45 .3 49 0.5 3188.38 9 3 .681 2 .0 K1 1 .5 9 45.8 44 0.5 6616. 16 12 6.396 1 .6 K2 1 .8 9 45.7 47 1 .5 13659.04 15 1 .905 -7.0 K3 1 .8 8 45.7 47 0.5 13709.47 20 7. 100 - 1 .3 K4 1 . 5 9 45.7 46 0.5 6616 . 16 15 6.405 1 . 5 K5 1 .8 9 45.6 46 0.5 13709.47 25 9.925 0 .2

Table 4. 1 1 : As table 4.7 but for DT 18.21 SRL-2. Area of one pixel is 8a8r = 1038.3 m2 .

SRL-2 DT 46 CR l ß ()p ()i i o: l (Jtheory Ncn Ic K

[m] [0] [0] [0] [0] [m2] [pix.] [dB] H1 1 .8 4 50.4 53 0.5 13709.47 15 1 1 .91 0 . 16 H2 1 .25 4 50.4 47 0 .5 3188.38 20 3 .740 1 .8 K1 1 . 5 4 50.0 55 0 .5 661 1 .43 18 5 .220 -0.4 K2 1 .8 4 50.0 59 0.5 13709.47 18 7.326 -2.0 K3 1 .8 4 50.0 55 0.5 13709.47 18 7.884 -1 .7 K4 1 .5 4 50.0 55 0.5 661 1 .43 9 4.743 -0.8 K5 1 .8 4 50.0 45 0.5 13709.47 12 8.448 -0.8

Table 4. 12 : As table 4 .7 but for DT 46.0 SRL-2. The area of one pixel is 8a8r =953.8 m2.


SRL-2 DT 78 CR l ß ()p ()i Ia i rYtheory NcB

[m] [0] [0] [0] [0] [m2] [pix.] H1 1 .8 -3 58.0 62 0 13722.77 12 H2 1 . 25 -3 58.0 54 0 3191 .48 9 K1 1 .5 -3 57.7 62 0 6617.85 12 K2 1 .8 -4 57.7 66 1 13699 . 12 9 K3 1 .8 -4 57.7 64 0 13712.92 12 K4 1 .5 -4 57.7 64 0 6613.10 16 K5 1 .8 -4 57.7 54 1 13699. 12 12

33

Ic K [dB]

6. 156 -0.2 1 .962 1 . 5 3 .084 -0.05 2 .745 -3.9 7.236 0.4 4 .256 1 .3 5. 244 -0.5

Table 4. . 13 : As table 4.7 but for DT 78. 1 SRL-2. Area of one pixel is 8a8r = 1869.5 m2.

2 . the thermal noise from the radar receiver, antenna and background radiation.

3. range and/or azimuth ambiguities , which may result in 'ghost' images.

The saturation noise is due to the analog signals exceeding the maximum or minimum range of the ADC. The quantization noise is the error resulting from input signals within the ADC dynamic range. For SIR-C data the quantization noise is about 18 dB lower than the signal power of one block of data. An 8 bit quantization level of the ADC would improve the signal to distortion noise ratio, but increases the data rate downlink to values which cannot be achieved in practice. Most of the SIR-C data were acquired in (8,4) bit Block Floating-point Quantization (BFPQ) mode. This technique allows the reduction in the downlink data rate [Curlander and McDonough, 1991] and avoids the problern of saturation during analog-ta-digital conversion. The digitized SAR video data (represented on 8 bits/sample) from the ADC are received by the block fioating point quantizer and divided into blocks. Then the samples of each block are scaled by the estimated sta.ndard cleviation for the block of clata and representecl in 4 bits. The 4 bit clata a.ncl the estimatecl thresholcl are clownlinked. The ground receiver decodes and reconstructs the original data.

For the noise matrix in equation 4.3 the following assumptions are made [van Zyl, 1990] :

• if the cross-polarized measurements are symmetrized then the noise matrix can be assumed to be symmetrical, i .e. nhv = nvh ;

• the noise in different channels is uncorrelated: (nhhn'hv ) = 0, (nhhn�v ) = 0, (nvvn'hv ) = 0;

• the noise and signal are uncorrelated: (nijSic� ) = 0, where i , j, k, l = h , v ;

• the noise power is the same in all four polarization channels: rY�h noise rY�v noise = 2rY�v noise · The factor 2 is due to symmetrization [van Zyi, 1990] , [Fr�eman, 1994]'. Thus the HH ancl VV noise powers are 3 dB higher than the HV.


Data Take SRL- 1 14.2 46. 1 SRL-2 14.2 18 .21 46.0

Publ .

Freq. Band

c c

c c c

c

ro . (J hv,nozse [dB]

-29.07 -32.25

-31 .73 -28.98 -33.72

-28.00

Product Freq. 0 (J hv,noise id. Band [dB]

1 1593 L -35.86 1 1431 L -36 .25

41956 L -33.70 41358 L -34.25 41354 L -37. 16

L -36.00

Product id.

1 1592 1 1430

41955 41357 41353

Table 4 . 14: Additive noise values in SIR-C clata of the test site Ötztal.

34

The additive noise may be subtracted from the cross-products according to the following equations:

( IS�t,i) (MhhM�h) - (J�h,noise ( 1Shv l2) - (MhvM,:J - (J�v,noise ( ISvv l2) (MvvM:v) - (Jev,noise (S�thS�v) - (M�thM;v) (Shhs,:v ) (M�thM�v ) (SvhS�v ) (MvhM:v)

or from the elements of the 4x4 Stokes matrix (section 3.3) : noise 0 25 ( 0 + 0 + 2 0 ) ml l - . (J hh,noise (J vv,noise (J hv,noise noise 0 25 ( 0 + 0 2 0 ) m22 - . (Jhh,noise (Jvv,noise - (Jhv,noise noise 0 5 0 ffi33 m33 - . (Jhv,noise noise 0 5 0 ffi44 m44 - . (J hv,noise

(4.13)

(4. 14)

Due to the last assumption about the noise matrix, the above formulae are simplified. CJ�v noise was calculated as the mean cross-polarized values in the shadow regions of the ·(J�v images in the quad-pol data available from SRL-1 and SRL-2. The results are presented in table 4.14 and they confirm the values estimated from cross-polarized measurements over smooth water published in [Freeman et al. , 1995] : -28 dB for C-band and -36 dB for 1-band data. The noise equivalent sigma zero estimates depend on the incidence angle because of the radiometric correction vector applied ( section 4. 1 . 7) .

The additive noise level is low and may be neglected for most natural targets at co-polari11ation. When the cross-polarized backscattering coefficients are very low, e .g. wet snow, CJ?tv noise must be taken into account. If the noise is subtracted the clata are decompre�sed and agairr compressed to 10 bytesjpixel ( quad-pol data) or 5 bytesjpixel (dual-pol data) . The successive decompressions and compressions of the data will cause an error of � w-6 in the value of the cross-products.


In addition to the methoel presented above, the algorithm for estimating the noise levels in polarimetric SAR data described in [Freeman, 1994] was tested. The algorithm uses the measured signature of surfaces with Bragg scattering, e.g. lake surfaces in the Ötztal scene. After substituting the scattering matrix model in eq. 4.3 and several simplifications, equations between the expected values of the cross-products, the noise power and the elements of the Bragg scattering matrix are obta.ined. Finally, the noise power can be obtained as the elifference between cross-products a.nd a term inversely proportional to (MhhM;v) . The algorithm is not applicable because of the low correlation between the like polarizeel returns for the selected sites.

The a.mbiguity noise is also important for spaceborne SAR. Constraints to avoid overlapping azimuth Doppler spectra anel overlapping cchoes in range are applieel when the dimensions of the antenna are calculateel. But these conelitions a.re not exact enough, therefore azimuth anel range ambiguities arise. The azimuth ambiguity to signa.l ra.tio for a. SAR system, inclueling SIR-C, is -20 elB. In the situation when the cross-polarizeel power is 20 dB lower than the co-polarizeel power the ambiguities dominate the cross-polarizeel signal.

4. 1 . 6 Image quality parameters derived from point target

analysis

The most common image quality parameters measureel from SAR images are elerived from the impulse response function (IRF) anel elescribe the shape of the IRF [Freeman, 1992] . An exa.mple of the interpolateel IRF of a triheelral corner reflector is shown in figure 4.5 .

The following features are eletermined from the point target:

• the ra.nge anel azimuth -3 elB resolution, which represent the distances between the - 3 elB points on the main lobe,

• the pea.k to sielelobe ratio (PSLR) which is elefineel as the ratio of the power of the highest sielelobe to the main peak of the response,

• the integrateel sielelobe ratio (ISLR) , defineel by the ratio of the energy contained in the sielelobes to the energy containeel in the main lobe of the response.

Image quality parameter values for SIR-C data publisheel in [Freeman et al. , 1995] are compared with those obtaineel from the analysis of the corner reflectors deployed on the test site Ötztal calculated from C-band SLC data. Tables 4. 15, 4. 16, 4 . 17, 4 . 18 , 4 .19 , 4.20, and 4.22 summarize the following image quality paramcters: the na.me of the corner reflector (CR) , the background to signal ratio (BSR) , the spatial resolution in range and azimuth (rr9 , raz ) , the integrated sielelobe ratio in range (ISLR,.9) anel in azimuth (ISLRaz) , near and far range peak to sielelobe ratio (PSLR,..q) the PSLR for the sidelobes before anel after the peak (PSLRaz ) . For the analysis data takes acquired with 10 MHz pulse bandwidth (DT 14.2, 46. 1 , and 78.0 SRL- 1 and DT 14.2 , 46.0 , and 78. 1 SRL-2) and with 20 MHz pulse bandwidth (DT 18 .21 SRL-2) were used.

Chapter 40 Calibration and quality of SIR-C/X-SAR data 36

Figure 4 05 : Impulse response function of trihedral corner reflector H1 , SIR-C DT 460 1 SRL-1 imageo

SRL-1 DT 1402 C-band CR BSR rrg raz ISLRr9 ISLRaz PSLRr9 PSLRaz

[dB] [m] [m] [dB] [dB] [dB] [dB] H1 -3202 16 09 70 1 -1603 - 1808 -2202 -2200 -2906 -29 02 K2 -2500 1608 70 2 -15 06 -2305 -1805 -18 09 -2206 -3403 K5 -20.4 1703 6 06 -1207 - -12 05 -2409 -18.4 -3509

Table 40 15 : Image quality parameters of DT 1402 SRL-1 , acquired with 10 MHz pulse bandwidth 0

The IRF was obtained by extracting a subarea of 24x24 pixels centered on the peak of the main lobe from the J(a-�h + a-2v )/2 full resolution SLC imageo In the case of dual-pol data � images were usedo This area was interpolated by a factor of 8 in each directiono The interpolation was carried out on the amplitude data in two dimensions by Fast Fourier Transformation (FFT) , zero padding, and inverse FFTO The mean background power was subtracted from the datao The registration between the HH and VV images was found to be within ±1/8 pixel and corresponds with [Freeman et al. , 1995] 0 Details of the determination of the IRF and the estimation of background power are presented in [Nagler, 1996] 0 Tables 40 21 and 4023 show the image quality results published in [Freeman et al. , 1995] which were derived from the analysis of several sceneso

For images acquired with 10 MHz pulse bandwidth the range and azimuth 3 dB resolution determined from the corner refiectors in Ötztal agree well with the mea-


SRL-1 DT 46. 1 C-band CR BSR rrg raz ISLRr9 ISLRaz PSLRr9

[dB] [m] [m] [dB] [dB] [dB] H1 -35 . 1 16 .5 7 .5 -15 .3 -22.9 -19.9 -19 .0 K2 -29.3 16.8 7.4 -15 .0 -35.2 -18 .6 -20 .7 K3 -26.8 16.9 7 .5 -18. 1 -19.9 -21 . 7 - 19 .8

37

PSLRaz [dB]

-35.9 -38 .7 -25.8

-26 .3 -19 .4

Table 4. 1G : Image quality parameters of DT 46. 1 SRL-1 , acquired with 10 MHz pulse bandwidth .

SRL-1 DT 78.0 C-band CR BSR rrg raz ISLRr9 ISLRaz PSLRr9 PSLRaz

[dB] [m] [m] [dB] [dB] [dB] [dB] H1 -35.5 16 .5 9 .3 -15 .0 -17 .3 -20.9 -18 .7 -27.2 -25 .6 K2 -27.3 16.6 9 .0 -14.8 -16 .5 -19. 1 -16 .2 - -22 .2 K3 -28.4 17.5 9 .3 -18. 1 -16.8 -24.4 -19.8 -24.6 -27.0 K4 -32.9 16.7 9 .0 -16 .0 -17. 1 -21 . 7 -23.3 -26.4 -26 . 1 K5 -23 . 1 17.7 9.2 -16.9 -22.5 -17.0 - 17.6 -21 .7 -16 .0

Table 4. 17: Image quality parameters of DT 78.0 SRL-1 , acquired with 10 MHz pulse bandwiclth.


[dB] [m] [m] [dB] [dB] [dB] [dB] H1 -35 . 1 16.9 7.2 -16.0 -24.6 -20.4 -19.3 -28 .7 -31 . 9 K2 -36.9 16 .7 7 .2 -15 .7 -22.9 -20.5 -18.4 -28.7 -28.6 K3 -34.6 16.8 7.2 -15 .4 -22.7 -20.4 -18 .7 -32 .7 -30. 1 K4 -35.7 16.8 7.2 -15.4 -22.7 -20.4 -18 .6 -30.3 -27.6 K5 -29.0 16.6 7.2 -14.7 -20.5 -19.4 -17.8 -28.5 -33. 1

Table 4. 18 : Image quality parameters of DT 14.2 SRL-2, acquired with 10 MHz pulse banclwidth.


[dB] [m] [m] [dB] [dB] [dB] [dB] H1 -37.4 17. 1 7 .3 -17.4 -22.8 -21 .9 -22.0 -30.4 -32.6 K2 -37.2 16.7 7 .2 -15 .6 -20.5 -20.6 -18.5 -28.8 -29.5 K5 -36. 5 17.5 7 . 1 -20.4 -22.3 -24.7 -24.2 -28.6 -33. 1

Table 4 .19 : Image quality parameters of DT 46.0 SRL-2, acquired with 10 MHz pulse banclwiclth.


SRL-2 DT 78. 1 C-band CR BSR rrg raz ISLRr9 ISLRaz PSLRr9 PSLRaz

[dB] [m] [m] [dB] [dB] [dB] [dB] H1 -37.6 16.7 9 .0 -16.3 -22 .6 -20.6 -22.0 -27.3 -28 .7 K3 -39. 1 17 . 1 8.8 -18.9 -19.9 -22 .5 -25.6 -26 .9 K4 -32.4 17.6 8.8 -18.9 -18.3 -20 .7 -24.3 -29 .5 K5 -38.4 17.4 9 .0 -19 .9 -21 .5 -22.8 -22.3 -25.8

Table 4 .20: Image quality parameters of DT 78. 1 SRL-2, acquired with 10 MHz pulse bandwidth.

Published BW f rrg raz ISLRr9 ISLRaz PSLRr9 PSLRaz

[MHz] [m] [m] [dB] [dB] [dB] [dB] 10 c 14.5±2.8 7.7±0.8 - 16.0±0.9 -13.7±4.7 -17.2±2.9 -22.8±3.7

L 14.9±2.9 7 .6±0.9 -13.0±2.2 -12 .2±4.7 -17 .2±2. 1 -21 .3±3.9

Table 4 .21 : Image quality parameters at 10 MHz pulse bandwidth publishecl by [Freeman et al. , 1995] .

surements published by [Freeman et al. , 1995] (see table 4.21 ) and with the specifications, with the exception of the azimuth resolution of dual-pol data which is always weaker at this mode. ISLR and PSLR values in range and azimuth are sometimes lower t han the published ones.

The results of the corner reflector analysis performed with the data take acquired with 20 MHz pulse bandwidth are presented in table 4 .22. The azimuth resolution confirms the published results, while the other parameters are clearly better than the specifications.

4.1. 7 Data quality plots

The CEOS format SIR-C products provide several Quality Assurance (QA) plots. These show whether the different polarization channels at each frequency were behaving the same and whether the different beams for each polarization were pointing in the same direction.


[dB] [m] [m] [dB] [dB] [dB] [dB] H1 -42 .5 9. 1 7 .6 -21 . 5 -19.5 -23.9 -35.6 -30.3 -30.4 K2 -36.5 8 .9 7 .4 -22.3 -19.5 -27.8 -28.3 -31 .3 -26 .5 K3 -41 . 4 8 .8 7 .7 -18.5 -21 .4 -24.7 -21 .4 -29.3 -28 . 1 K4 -41 .2 8 .7 7 .6 -17.0 -19.3 -21 .6 -20.3 -27.8 -28.7 K5 -41 . 9 8 .8 7.8 -18.8 -22.6 -27.9 -22.3 -35.3 -33 .0

Table 4 .22 : Image quality parameters of SRL-2 DT 18 .21 , acquired with 20 MHz pulse bandwidth.


Published BW f rrg raz ISLR,.9 ISLRaz

[MHz] [m] [m] [dB] [dB] 20 c 7.9±0.3 8.0±1 . 1 -14.2±2.0 -12 .8±4.5

L 8.3±0.3 8.0±1 . 1 -15 .0±1 .0 -14.0±1 .0

39

PSLR,.9 PSLRaz [dB] [dB]

-15.6±4.6 -22 .0±3.0 -17.8±3.2 -18.0±2.0

Table 4.23: Image quality parameters at 20 MHz pulse bandwidth publishecl by [Freema.n et al. , 1995] .

Histograms of raw and processed images a.re genera.ted over blocks of da.ta. for HH, HV, VH, a.nd VV pola.riza.tions. Their sta.tistics a.re a.nnota.ted in the Da.ta. Histogra.ms Record of the SAR Leader File. Most of the ra.w da.ta. histograms have a. rela.tively small deviation from the mean (SD « 1�7 ) . The amplitude distribution of the processed data shows polarization dependent minimum and ma.ximum sa.mple va.lues. In both types of histograms a good similarity of the HV and VH plots is observed indicating that the antenna had similar behavior on transmit and receive. As an example, in figure 4.6 histograms of raw and processed images for DT 46. 1 SRL-1 arc shown.

Range spectra plots show the Fourier spectrum of a chirp corresponding to the specified bandwidth ( 10 or 20 MHz) , and the calibration tone inserted. The SIR-C system uses the in-fiight radio frequency (RF) internal calibration method where the changes in the receiver transfer characteristics are monitared with a single frequency tone generator. The calibration tone ( called caltone) is a continuous tone signal inserted in the receiver. Its power is set to be 12-18 dB lower than the signal power in order to reduce its contribution to receiver saturation. The caltone frequency is selected so that it falls into a discrete FFT bin during signal processing, and its phase is always locked with the radar from pulse to pulse. In this way, when the caltone is extracted from a data block ( e.g. , 3292 samples by 64 lines for DT 46. 1 ) in the frequency domain, a gain in the caltone power level is obtained.

The range spectral plots for DT 46. 1 SRL-1 at C-band a.re shown in figure 4.7. The center frequency of the first spectra.l bin is 0.02197 MHz of the 256th bin is 1 1 . 227 MHz. The caltone can be observed in the center of the system ba.ndwidth as a. peak in bin 129 and has a spectral power of 0 dB. The minimum spectra.l power of each spectrum is annotated on the figure.

The mean caltone gain estimate of the 4 polarization cha.nnels is annotated in the CEOS header and is used to normalize the data. samples a.cquired during a time interval around the processed block of data.

The radiometric correction vector for each channel of the processed image is provided in the CEOS data format. It includes the inverse of the antenna pattern, range attenuation and sine of incidence angle terms [Freeman, 1994] . The radiometric correction vectors of DT 14.2 SRL-1 and SRL-2 as a function of the pixel number in slant range direction of the image are shown in figures 4.8 and 4.9 .

Due to instabilities in the Shuttle orbits a. roll angle may be introduced into the pointing of the SIR-C antenna. The roll angle estimation error causes a displacement of the a.nt.enna pattern. The swath width of DT 14.2 SRL-1 was 1392 pixels with 13.324 m pixel spacing in slant range. The minimum is about 40 pixels away from the ima.ge center which means a roll angle estimation error of 0 .01 o . DT 14.2 SRL-2


RAW SIR-C SRL-1 DT 46.1 C-band

PROCESSED 1 .4 1 .2 1 . 0 0.8 0.6 0.4 0.2

sd=18.4 mean=127.4

0.0 L.......o.�.L...o.........wl!lli!IL....L...JIJ. ,........._ ............ �w 0

1 .4

Q) ·E o.4 � 0 .2

1 .4 :=: 1 .2 >< � 1 .0 � 0.8 :ä 0.6

0

50 1 00 1 50 200 250

w Bins

sd=14.9 mean=127.5

50 1 00 1 50 200 250 Bins

HV sd=8.0 mean=127.5

-�

0.4 l "' öl 0.2 j � o.o L..o....o...... . .J�l ---........LJ

0

1 .4 :=: 1 .2 >< s 1 .0 t 0.8

:.2 0.6

50 1 00 1 50 200 250 Bins

VH sd=8.4 mean=127.4

� 0.4 � o.2 N' ·� 0.0 L.......o.�......._� .... ��l wtL::IW.�..........,�........,

0 50 1 00 1 50 200 250 Bins

2.0 ,...---------------, 8 ,.... >< E

i :2 -� - 0.5 �

HH sd=0.30 mean=0.27

50 1 00 1 50 200 2 50 Bins

2.0 ,......--------------, 0 0 ,.... >< 1 .5 E a .9 1 .0 :ä -� - 0.5 "'

�

w sd=0.31 mean=0.28

0.0 L......� .......... �...l.....�....L........:::.::r::= =J 0 50 1 00 1 50 200 250

Bins 2.0 ,...---------------,

8 HV sd=0.12 � mean=0.13 6 1 .5

t :ä Q) > +I "'

�

0 0 ,....

0.0 L.._-'-'--'c........_........_,� ............. .....=:::::c::::::=J 0 50 1 00 1 50 200 250

Bins 2.0 VH

>< 1 .5 sd=0.12 mean=0.13

E a .9 :ä � ..!!! �

0 50 1 00 1 50 200 250 Bins

40

Figure 4 .6 : Histograms of raw and processed image data of the four polarization channels supplied with the CEOS data format corresponding to DT 46. 1 SRL-1 . For the raw data histograms: min. sample value = 0, max. sample value = 255. For the processed image histograms: min. sample value = 0, max. sample value = 0.918 (corresponding to bin 255) for HH and VV channels, max. sample value = 0.413 (corresponding to bin 255) for HV and VH channels . Standard deviation and mean sample value for each histogram are annotated on the figures.


5

tQ � fjj � ] ! -25

- JO 0

5

tQ 0

� -5 ... <II ;t - 1 0 0 0..

öi .b � 0..

(/) -25 - JO

0

SIR-C SRL-1 DT 46.1 C-band Range spectra

5 HH tQ 0 HV

� - 5 ... <II ;t - 1 0 0

V ·" 0.. - 1 5

] min. spectral power a \ min. spectral power -

& -20

-28.089 [dB] (/) - 25 -33.252 [dB] - JO

50 1 00 1 50 200 250 0 50 1 00 1 50 200 250 Bins Bins

5 VH 0 w tQ �

t ;t 0 0.. - 1 5 iii � - 20 min. spectral power = min. spectral power = 0.. -29.198 (dB] -33.349 (dB] (/) -25

- JO 50 1 00 1 50 200 250 0 50 100 1 50 200 250

Bins Bins

41

Figure 4.7 : Plots of the range spectra of the four polarization channels of SIR-C DT 46 . 1 SRL-1 data with a caltone inserted at bin 129.

SIR-C SRL-1 DT 14.2 C-band Radiometrie correction vector

- 1 4 iil - 1 4 iil ::!:!. ::!:!. r:: HH r:: vv 0 - 1 6 0 - 1 6 '1:1 ·.c "' � � � p. - 1 8 E - 18 0 0 V u

·.6 :6 QJ -20 QJ -20 E E � .S! "1:1 "' -22 � -22 p::: 500 1 000 1500 500 1 000 1500

Range [pixels I Range (pixels]

iil - 1 4 EQ - 14 � ::!:!.

HV r:: 8 0 VH - 1 6 '1:1 - 1 6 ·,p m "' � p. Ei - 1 8 e - 18 0 0 u u u

� 1l QJ -20 e -20 Ei 0 0 :a :a "' -22 � -22 � 500 1000 1 500 0 500 1 000 1 500

Range [pixels] Range [pixels]

Figure 4 .8 : The radiometric correction vector as a function of pixel number in range applied to the polarization channels of DT 14 .2 SRL-1 .


SIR-C SRL-2 DT 14.2 C-band Radiometrie correction vector

- 14 - 1 4 10 10 :!!. :!!. § - 1 6 HH § - 1 6 vv •J:I ·.c ill � c � ..

- 1 8 S' - 1 8 0 8 u ·.S u ·.s - 20 .. .. e e 0 0 :a :a .. -22 &! -22

t:<: 0 500 1 000 1500 0 500 1000 Range [pixels] Range [pixels I

- 1 4 EQ - 1 4 10 :!!. :!!.

HV c c 0 VH 0 - 1 6 :p - 16 ".1:1 � .. Cl) � .. p.. Ei - 1 8 e - 1 8 0 8 u u u ·.s J -20 .. -20 Ei .9

"Cl .. -22 � -22 � 500 1 000 1 500 0 500 1 000

Range [pixels] Range [pixels]

42

1 500

1 500

Figure 4.9 : The radiometric correction vector as a function of pixel number in range applied to the polarization channels of DT 14.2 SRL-2.


has 1332 range pixels with 13.324 m pixel spacing, the minimum of the plot is located 330 pixels away from the image center and the error in the roll angle estimation is 0.9° .

The range correction is applied to the total received power which includes signal and noise power. Thus the noise power in the final image is also range dependent [Freeman, 1994] .

4.2 Calibration equation for X-SAR data

In this study X-SAR SSC and GTC image products were used as complementary information to C- and L-band data. The GTC are derived from MGD data. For both the SSC and MGD data the image pixel intensity is related to 0"0 by [Zink ancl Bamler, 1995]

sin ()P 0 (I) = Kao . (() )

· 0" + KN (R) · Nraw sm P - o:

( 4. 15)

where Kao is the calibration constant, ()P the local incidence angle assuming an ellipsoiclal Earth , o: the local terrain slope (which is given by the DEM) , Nraw the raw clata noise power, KN(R) the processor noise gain. Nraw and KN (R) are annotatecl in the X-SAR CEOS header. The noise equivalent 0"0 (the second term in equation 4. 15) is range clependent , about -40 dB at near range (()p = 20°) and -27 dB at far range (()P = 60°) . This means that the noise effects can be neglected when 0"0 is clerivecl for most natural targets. The absolute calibration was performed with corner reftectors deployed on the test site Oberpfaffenhofen. The processor gain is acljustecl in order to optimize the dynamic range of the image products and finally the absolute calibration factor is:

Kao 200000 for SSC products, and Kao 1000000 for MGD products

The local terrain slope is not taken into account cluring processing. For a ftat terrain o: = 0 ancl 0"0 can be derived from equation 4. 15 . For inclined surfaces (o: =j:. 0) the same correction of the processor incidence angle as for the SIR-C data (section 4 . 1 .2) , sin ()d sin ()P , must be applied. ()i is known from local incidence angle maps, and ()p(R) can be calculated by fitting a spline to near, mid and far range values of ()P annotatecl in the CEOS header. If only small regions are analyzed the average value of ()p(R) may be used for the derivation of 0"0 in this area.

The absolute calibration accuracy is within ± 1 dB, as derived from the reftectors cleployed at Oberpfaffenhafen as well as from the analysis of rain forest data. The relative calibration of SSC and MGD proclucts is within ±0.05 dB.

Because of the high quality of the X-SAR data, demonstrated by the low noise level ancl the calibration accuracy, an investigation of image and data quality based on the corner reftectors deployed in Ötztal is not presented in this thesis. Detailecl information about X-SAR system performance and data quality is provided in [Zink and Bamler, 1995] .

Chapter 5

Field observations during the SIR-C /X-SAR experiments

5.1 Description of the test site

The test site Ötztal is situated in the Austrian Alps at 46.7° N and 10.8° E . The central part (fig. 5 . 1) consists of the accumulation areas of glaciers covering large plateaus with relatively gentle topography and a mean altitude of 3100 m a.s . l . . The main glaciers are Gepatschferner (GEP, 1 7 km2) , Kesselwandferner (KWF, 4.5 km2) , Hintereisferner (HEF, 9 km2 ) , and Vernagtferner (VF, 9 km2) where glaciological research has been carried out during the last decades. The glacier termini descend into the upper parts of the valleys. On the unglaciated areas, above 2100 m, the dominant natural surface types are moraine, bare rock, sedges , grasses and dwarf shrubs. Below 2100 m coniferous forests and cultivated meadows are the main vegetation types. Narrow valleys, steep slopes and large altitude variations are also typical features of the test site, thus providing the possibility to study the impact of topography for SAR applications in high alpine terrain. The highest peaks are vVilclspitze (WS) 3771 m and Weißkugel (WK) 3739 m, the village Vent is situatecl at 1890 m, the lake Gepatschstausee at ca. 1 770 m.

For the SIR-C/X-SAR experiment Ötztal was chosen as a test sites for hydrological applications. Prior to the SRL missions the site had been surveyed by the three-frequency polarimetric AIRSAR from NASA/JPL in two campaigns, June 1989 ancl August 1991 , and by the ERS- 1 satellite. During the SAR overfiights trihedral corncr refiectors were deployed on the glaciers and field measurements were carriecl out .

5.2 Field measurements during SRL- 1

At the time of SRL-1 mission ( 10- 14 April 1994) typical winter conditions were observccl on the glaciers. The air temperature was below - 10°C during the whole periocl. The glaciers were covered with a smooth, dry, fine grained, 2 to 3 m cleep snow layer. The snow depth was measured along transects in the center of the glaciers. Selected properties of the snow pack for different layers are summarized in table 5 . 1 . Information on snow clensity, temperature, grain properties, ancl strati-

44

Chapter 5. Field observations during the SIR-C/X-SAR experiments

N

A • t----1

2 km

•

45

Figure 5 . 1 : Sketch map of the test site Ötztal. The main glaciers and peaks in the test site are: Hintereisferner (HEF) , Kesselwandferner (KWF) , Gepatschferner (GEF) , Vernagtferner (VF) , Wildspitze (WS) , Weisskugel (WK) .


0

0.50

1.00

s ........ 1 .50

l Cl 2.00

2.50

3.00

Hintereisferner at corner reflector Hl (2910 m a.s.l.) 10.04.1994 MET 15:00

0 Temperabure [°C] -5 -10 -15 -20 -25 -30

I I

\ \

l I

\ I

Temperature

Density

0 200 400 600 800 1000 Density [kg m-3 ]

Grain shape

fresh snow

decom-posing crystals

faceted crystals

faceted ayotals

refrozen grains

Grain Snow size liardcrless

[mmJ 0.5-1.0

0 .5-1 .0

1.0-2.0

1 .0-2.0

2.0-5.0

very low

high

medium

medium

very high

Iee crusts +--+---

Figure 5 .2 : Snow pit on Hintereisferner on 10.04. 1994 (SRL-1 ) .

46

fication was obtained from snow pits made on the glaciers HEF and KWF (figures 5.2 and 5.3) .

In the snow pit on HEF on 10 April one ice crust was observed at 2.40 m depth and another with impurities at 2 .50 m depth, the maximum temperature was about -2 .5 oc at 5 cm depth and the minimum about -7°C at 40 cm depth. On KWF on 10 April a fresh snow layer of 0 .50 m was observed, one ice crust with several ice lenses about 0.5 cm thick was located at 0 .51 m depth, metamorphic snow until the next ice lens at 2 .22 m, and below, several ice crusts alternating with very hard snow. On KWF the temperature in the snow pit ranged from -8°C to - 14°C .

On 12 and 13 April snow fell , and the temperatures in the day time increased slowly but decreased again in the night. Thus in the morning of 14 April, at the time of acquisition of DT 78.0 the air temperature was -10°C. The liquid water content of the snow was not measured because the snow temperatures were clearly below 0°C. Although roughness measurements of the snow surfaces were not made, these can be characterized by typical values for fresh snow with r.m.s. of surface height about 1 to 3 mm.


Site

Kesselwandferner close to Brandenburgerhaus near P43 3200 m a.s.l. 10.04.1994 MET 18:00

0

0.50

s 1.00 ........... -:5 0.. QJ

Cl 1.50

2.00

2.50

0 Temperature [0C]

-5 -10 -15 -20 -25 -30 - - , Temperature - - - -'

1 Density --

0 200 400 600 800 1000 Density [kg m.J ]

Grain Grain Snow shape size Hardness

[mm] fresh snow 0.5-1.0

fresh snow 0.5-1.0

decom-posing 0.2-0.5 crystals

faceted 2.0-5.0

crystals

faceted crystals 1.0-2.0 refrozen grains 1.0-2.0

very low

Iow

mediun

medium

mediurr veryhigh

i ce crusts

Figure 5.3: Snow pit on Kesselwandferner on 10 .04. 1994 (SRL-1 ) .

HEF 2600 m HEF 2900 m KWF 3200 m

47

Date/Time 1 1 .4.94 15 :00 10.4.94 15 :00 MET 10.4.94 18 :00 MET ds [cm] 185 320 261 Depth p Ts GS p Ts GS p Ts GS [cm] [kgm-3] [OC] [mm] [kgm-3] [OC] [mm] [kgm-3] [OC] [mm] 0-20 258 -4.2 0.5 193 -4.4 0.5 1 16 - 1 1 .5 0 .2 21-100 401 -5.3 1 .0 350 -7 .1 1 .0 320 - 13.8 0.5 101-ds 386 -3.0 1 . 5 435 -5 .1 1 .5 448 - 1 1 .7 1 . 0

Table 5 . 1 : Snow properties measured on HEF and KWF during SRL-1 : total snow depth on ice or firn (d5 ) , mean density (p) , mean snow temperature (Ts ) , mean grain size (GS) .


Site KWF GEF HEF KWF Date 1 . 10.94 2. 10.94 3 . 10 .94 3 . 10.94 Time [MET] 14:00 13:00 12 :00 14:00 Altitude [m] 3220 3270 3065 3280 ds [cm] 135 196 96 231 Fresh snow [cm] 0 0 2 0 Frazen layer ( cm) 25-50 - - 20-50 p (0-20 cm) [kgm-3] 433 408 473 435 p (20-ds cm) [kgm ·3] 528 532 617 551 GS (0-20 cm) [mm] 1-2 1 0 .5 2 GS (20-ds cm) [mm] 2-3 1-4 2-5 2-4 V w (5 cm) [%voLJ 4 3 2 1 . 5

48

HEF 6.10.94 09: 15 3200 279 2 -

462 496

1 0 .5-5

0

Table 5 . 2 : Snow properties measured on HEF, GEF and KWF during SRL-2: total snow clepth over ice or firn (ds) , mean density (p) and mean grain size (GS) for two layers, snow liquid water content (Vw) at 5 cm depth.

All areas above 2500 m altitude were covered by dry snow. At lower altitudes the snow was partially wet. Snow cover was present at altitudes above about 1200 m. Observations on the unglaciated surfaces, made in Kaunertal (figure 5 . 1 ) , are described in [Mätzler et al . , 1997] . In March'94 the warm weather caused intensive melt metamorphism in the snow pack at altitudes below 2500 m. In April '94 the air temperature decreased significantly and remained low during the SRL-1 mission. This caused the refreezing of the snow pack up to 25-30 cm depth forming a hard crust with densities greater than 400 kg m-3 . Below the crust the snow remained wet with about 2 %vol liquid water content. Fresh snow fell before and during the campaign at altitudes above 1000 m. At sites with shallow snow the snowpack was refrozen and no wetness was observed.

5.3 Field measurements during SRL-2

Snow conclitions on the glaciers during the SRL-2 campaign ( 1-5 October 1994) were quite complex. Heavy snowfall in September covered the glaciers with a fresh snow pack, which melted and disappeared during a warm period at the end of the month at altitudes below about 3000 m.

On 1 and 2 October the snow on the glacier plateau of KWF /GEP and on HEF was wet . In the evening of 2 October the temperature dropped below oac at altitudes above 3000 m. In the snow pit made on 1 October (figure 5 .4) the surface was wet and soft . Due to a cold period in September, an intermediate layer of dry, hard snow was found between about 20 and 50 cm depth. Ice crusts appeared in the wet snow layer at 0 . 13 m, 0.64 m and at 0. 71 m depth. Similar properties were observed in the snow pit made on 2 October on Gepatschferner (table 5 .2) : a moist surface with coarse grains and very low hardness followed by several ice crusts located at different clepths. At the bottarn the snow became hard with faceted crystals and medium to coarse grains. The temperature in the snow pits was not measured.

In the same period the ice in the ablation area of HEF was rough and wet . In


Kesselwandferner close to Brandenburgerhaus near Bl 3200 m a.s.l. 1.10.1994 MET 14:00

Liq. water Grain Grain Snow content shape size Hardness

o --,---------,L------. s �0.50 fr Cl

1 .00

0 200 400 600 800 1000 Density [kg ni3 ]

wet wo>t

dry

drv Clrv

mois

mois mois

fresh snow roun e . e:ralllS

coarse grains

!.

coarse grains and clusters

� coarse grains

[mm] to.s-1.0 very lov.

- very ov.

1.0-2.0 high

2.0-3.0 high 2.0-3.0 very high

2.0-3.0 high

2.0-3.0 very high 1.0-2.0 vo>ry high

Figure 5.4 : Snow pit on Kesselwandferner on 01 . 10. 1994 (SRL-2) .

ice crusts

49

the first two days of the campaign the air temperature at the HEF station at 3000 m was above 0°C even in the morning, e.g. at 1 October 07:41 local time when DT 14.2 was acquired it was +4°C. On 2 October it was cloudy and the same conditions were noticed on the glacier.

A snowfall occurred on HEF in the night from 2 to 3 October which covered the glacier with about 2 cm of dry snow. The air temperature in the morning of 3 October was -0.2°C at the HEF station. The ice below the fresh snow layer was frozen. The snow pit from figure 5 .5 made at noon shows the already moist layer of fresh snow, and below it two hard, wet layers, then at 0.44 m depth an ice crust .

In the same morning on the KWF jGEP plateau the top layer of the snow pack was frozen, while the old firn below it was humid.

On 4 October the temperature continued to decrease, the maximum air temperature at the HEF station rase to +0.2°C. In the morning of 5 October -6°C were measurecl , with a thin cover of new snow and - 10°C was registered at 3200 m on KWF. In the next clays it became colcler and in the morning of 6 October the air temperature measured on HEF was about -12°C.

In April the properties of the snow pack remairred fairly constant during the campaign and were typical for winter conditions, whereas in October a change in the backscattering properties of the site was observed from DT 34.31 to DT 46.0 and to DT 78. 1 as a consequence of the transformations occurring in the snow pack when snow and ice were gradually freezing.

A limited number of surface roughness measurements were made on 2 October on HEF using the laser profilometer. Due to the high liquid water content of the ice and firn areas during SRL-2, the surface dominates the received radar signal , and the geometrical properties of the surfaces are important. In figure 5.6 two surface roughness profiles, for ice and snow, ancl their autocorrelation functions (ACFs) are shown. The measurements were carried out in the upper part of the ablation area where the ice surfaces were comparatively smooth after the September snow had clisappearccl. Generally at least two scales of roughness are observed: a large scale


0

:§: t 0.50

Q) a

Hintereisferner at Teufelseck 3065 m a.s.l. 3.10.1994 MET 12:00

Liq. water Grain Grain Snow content shape size Hardness

[mm]

LL moist es S[lO\\ n - 1 .c; verv low wet

rounaea 0.5-1.0 medium grains

dry rolJ!lded 1.0-2.0 high grams

col!Se

dry grams

2.0-3.0 very

and high l cl�sters s us -

0 200 400 600 800 1000 Density [kg ni3 ]

Figure 5 .5 : Snow pit on Hintereisferner on 03 . 10 . 1994 (SRL-2) .

ice crust �

50

of roughness in the order of decimeters to meters and a small scale in the order of millimeters. The ]arge scale roughness is caused by the drainage patterns in snow and ice. The Iaser profilometer is too short to measure this roughness scale. The calculatecl ACFs in figure 5.6 are obviously dominated by the large scale roughness and differ significantly from the theoretical ACFs. The correlation length calculated for the measured profiles has large variations for the same surface type, e.g. for snow the correlation length ranges between 73 mm and 197 mm. Therefore it seems that this parameter is not a good descriptor of a rough surface. The investigations of [Bellini, 1994] revealed that , if the ACF is calculated from joined roughness profiles measurecl in the same region, the value of the correlation length is more stable but is not necessarily representative for that surface. An other problern when natural surfaces are characterized by statistical parameters results in the fact that , in our case, the measured surfaces are not fully random. Methods for the estimation of the ACF ( or power spectrum) from a limited number of field-measured surface profile clata are cliscussed in [Tsang et al. , 1996] . The problems which occur when the power spectrum is obtained by fitting the measured ACF with an exponential or gaussian ACF are pointed out. Synthetic roughness profiles are generated from the average spectrum of true measured roughness profiles. With this method a better resemblance of the synthetic surfaces to the real-life profiles is observed, as well as a good agreement between the scattering results computed from natural and synthetic surfaces.

The mean values for r.m.s. height (O'h) and correlation length (L) over ice surfaces were 6.8 mm and 109 mm ( averaged over 4 roughness profiles) , respectively; for snow and firn 6 . 1 mm and 90 mm (averaged over 5 roughness profiles) respectively.

5.4 Meteorological observations

Information on air temperature, snow height and precipitation from 8 April to 16 April ancl from 29 September to 7 October was obtained from meteorological stations surrouncling the test site. As an example, in figure 5 .7 the data from the climate


Surface roughness

40. Iee 2.10.1994 HEF

crh= 8.8 mm

I 20.

fo ] O.-:r----I'MI;C7.brDI\r.-;----Hür---w��'----iü-------! 11.1 � 8 -20.

p..

Autocarrelation function 1.0 ..........------------, L = 150 nun 0.5

-0.5

L -1.0 +-r-r+--r-ro---.,--r-,-"--,c-r-r�,....,--l

51

-40. 0 500 1000

20.

-20.

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Horizontal position [nun]

Surface roughness

Displacement [mm]

Autocarrelation function Snow 2.10.1994 HEF

o;, = 5.3 nun 1.0 ......-------------, 0.5

-0.5

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Horizontal position [nun]

L = 93.5 nun

500 1000

Displacement [mm]

Figure 5 .6 : Examples of surface roughness profiles for ice in the upper part of the ablation area and for snow and the corresponding autocorrelation function measured on Hintereisferner on 02 .10 .1994 (SRL-2) . The correlation length L is shown. Theoretical autocorrelation functions are plotted: exponential (- . - . ) , gaussian (- - -) , modified exponential (- - -) .

Chapter 5. Field observations during the SIR-C/X-SAR experiments 52

station of Obergurgl , situated at 1938 m, about 10 km east of Vent, are plotted for the two periods. During the SRL-1 mission the air temperature did not exceed Ü°C until the end of the experiment. Snow fall was observed during most days and a major event was observed on 12 April. In the first days of October the air temperature was above ooc and decreased during the mission, reaching -10°C at the end of the period. Weak rainfall and later on snowfall was observed almost every clay. The unglaciated areas were almost completely free of snow.


Obergurgl 1938 m a.s.l.

SRL-1 April 1994 DT 14 DT 34 DT 78

15 60

E 1o 55 50

QJ 45 � 5 40 35 ""' QJ

P.. 0 30 e 25 QJ E-< -5 20

15 -10 10

5 -15 0

8.4 10.4 12.4 14.4 16.4

........ 250 DT 14 DT 34 DT 78

]. 200 DT 18 DT 46 t 150

QJ "0 100 � 0 50 J3

0 8.4 10.4 12.4 14.4 16.4

SRL-2 October 1994 DT 14 DT 34 DT 78

......., 20 DT 18 DT 46 30 u � QJ 25 ""'

.a 10 � 20 QJ S" 15 � 0 10

5 0

-10 0 0 0 0 29.9 1.10 3.10 5.10 7.10

Figure 5 .7 : Meteorological observations at Obergurgl during SRL-1 arrows show the acquisition time of the various data takes .

53

I c:: 0

:0 .19 ....... P.. ....... u �

p..

I ..._. c:: 0

:p (1:1 -. ...... P.. . ...... u QJ �

and SRL-2. The

Chapter 6

Backscatter modelling of snow covered terrain

[n this cha.pter the interaction of the microwaves with snow is discussed as a basis for the interpretation of the signatures derived from SAR data. In the first part the dielectric properties of dry and wet snow are presented. In the second part the sensitivity of surface and volume scattering models to variations of snow properties is investigated. The different backscattering effects are combined in a one layer model and their contributions to the total backscattering are compared.

6.1 Permittivity function and penetration depth

Snow is a heterogeneaus mixture of air, ice, and, if wet , liquid water. The propagation of the electromagnetic wave is governed by the dielectric constant or permittivity, E = E1 - jE" , since the magnetic permeability of snow can be approximated by that of free space, 1-" � 1-"o · Usually these measures are expressed relative to the vacuum values and will be denoted Er and 1-"r ·

Dry snow. The real part of the relative dielectric constant of dry snow, E�5 , is inclependent of frequency from 1 to 10 GHz [Tiuri et al . , 1984] . It clepends only on the volume fraction of the ice particles. The imaginary part , E�5 , depends on frequency, temperature, density and impurity of the snow.

The following formulae were chosen to calculate the real and imaginary parts of the clielectric constant of dry snow, Eds :

1 . + 1 .5995ps + 1 .861p� for Ps < 0.45g/cm3 [Mätzler, 1996] E��e (0 .52p5 + 0.62p;) [Tiuri et al . , 1984]

(6. 1a) (6. 1b)

where Ps is the snow density in gcm-3 and E��e is the dielectric loss of ice for which the value E�'ce = 8 x 10-4 measurecl at 2 GHz and -20°C was taken . The frequency and temperature dependence of E��e in the range -70°C to Ü°C is given in [Mätzler, 1995] . For frequencies between 1 and 10 GHz E��e increases slightly with the temperature and the given equation provieles reasonable values for E�s ·

Wet snow. The amount of liquid water determines the magnitude of the dielectric constant of wet snow in the microwave range, which is frequency dependent . This is clue to the fact that the dielectric properties and relaxation frequency of water are

54

Chapter 6. Backscatter modelling of snow covered terrain

4 ,------------------------. p5 =0.44 gcrri3

3

1

55

Figure 6 . 1 : The variation of the real and imaginary parts of the dielectric constant of wet snow with liquid water content , Vw , at L- , C- and X-band.

completely different from those of the other components. For the frequency range of 1 GHz to 30 GHz the permittivity of wet snow, Ews , can be estimated with the relation [Mätzler, 1987]

I Ews

II Ews

1 23VwJJ Eds + J6 + J2

11 23Vwfof Eds + J6 + J2

(6.2a)

(6 .2b)

where Vw is the liquid water content by volume, fo = 10 GHz is the relaxation frequency of water f is the frequency in GHz. The variation of E�5 and E�5 with the liquid water content at L- , C- and X-band is shown in figure 6 . 1 .

Volume extinction coefficient and penetration depth. The depth where the average power of a wave propagating downward in an uniform medium is reduced to 1/ e from its initial value is called the penetration depth. For a medium with uniform extinction coefficient, ke , the penetration depth, dp , is given by

dp = k;1

Generally ke is the sum of absorption and scattering coefficients, ke = ka + k5 • For wet snow and dry snow at low microwave frequencies, k5 « ka , so that dp � k;;1 . The absorption coeffi.cient of a homogeneaus medium is defined as [Ulaby et al . , 1982]

k -47T

a - Ao E� [ ( E�2) 1/2 l - 1 + - - 1 2 E12 T

(6 .3)

where >.0 is the wavelength in free space Er = E� - jE� is the relative permittivity of the medium (snow) . If E� « E� the above expression can be simplifiecl and the

( Jhapter 6. Backscatter modelling of snow covered terrain 56

10.00 p5 =0.44 gcm-3

........ s ..........

.rJ' 1 .00 -5 I 0.. I ):..-band Q) I '"0 � \

\ 0 \ '..t:l cd 0.10 "\

b ' '-.

Q) ' � ' '-. Q) '-.

- - - � _C::::_band �

0.01

0 2 6 8

Figure 6 .2 : Penetration depth in wet snow as function of liquid watcr content at L- , Cand X-band.

penetration depth reduces to

d � AoX P - 2rrc" r (6.4)

This approximation is valid for wet snow and dry snow with small grain sizes at frequencies up to 10 GHz. Because the scattering effects can be neglected, the value of dp obtained with equation 6.4 may be considered the maximum penetration clepth at a given frequency ancl wetness. Figure 6.2 shows the penetration depth as a function of Vw , calculatecl with 6.4, at L- , C- and X-band.

6.2 Scattering from snow covered terrain

Generally, the total scatterecl intensity from an inhomogeneaus layer with irregular bounclaries, e.g. snow covered ground, consists of the following main contributions:

(6.5)

where !5 represents the intensity scattered from the top boundary surface, lvt is the transmitted intensity due to the volume scattering within the layer, l9t is the transmitted intensity due to scattering at the lower boundary Ivgt is the surfacevolume interaction term, where (85 , </J5) is the direction of the scattered wave.

The contribution from the upper boundary (air/snow interface) and lower boundary (snowjground interface) are surface scattering terms which depend on the dielectric cantrast of the two media and the roughness of the interface. The ground surface contribution is attenuated by the snow layer by an amount depencling on the optical depth of the layer. l9t becomes unimportant if the loss in the layer


L-band C-band Vw Ps c'

r c" r dp c'

r c" r [%volj [gcm-3 ] [m]

0 0 .30 1 .53 0.0002 236.2 1 .52 0.0002 1 0 .44 2.09 0.0286 1 .931 2.04 0.095 2 0 .44 2 .31 0 .0569 1 .021 2 .22 0 .1906 3 0.44 2 .54 0.0852 0.715 2.40 0 .2858 4 0.44 2.77 0 . 1 135 0.560 2 .58 0 .3809 5 0 .44 2.99 0. 1418 0 .466 2.76 0.4761 6 0 .44 3 .22 0. 1701 0.403 2.94 0.5713

57

X-band dp c'

r c" r dp

[m] [m] 55 . 1 1 .53 0.0002 30.5 0 . 135 1 .98 0. 1 152 0 .061 0 .070 2 .10 0 .2301 0 .031 0 .049 2.22 0 .3450 0 .022 0.038 2.34 0.4599 0 .017 0 .031 2 .46 0.5748 0.014 0 .027 2 .58 0.6897 0 .012

Table 6 .1 : Snow permittivity and penetration depth calculated according to eq . 6 .1 , 6 .2 , and 6.4 for liquid water content (Vw) from 0 to 6 %vol . For dry snow Ps =0.3 gcm-3 represents the mean density measured during SRL-1 , for wet snow Ps =0.44 gcm-3 is the mean density of the upper part of the snow pack measured during SRL-2.

is high or the lower boundary discontinuity is small. The volume scattering term depends on the volume scattering albedo of the layer. If the scattering albedo is small Ivt becomes unimportant . Ivgt represents the interaction between the volume inhomogeneities and the lower boundary of the layer. In many situations this term is smaller than the other three terms in eq. 6 .5 . The conditions under which one or more of these terms will dominate or be negligible is investigated below.

6 . 2 . 1 Snow layer characterisation

The top and bottom boundaries of the snow layer are random rough surfaces which can be characterized by the r.m.s. surface height, ah , and the correlation length, L. This is a reasonable approximation if the surface does not have a preferred orientation (which is often observed for glacier ice) . For snow and ice surfaces on the glaciers ah and L were determined from roughness profiles measured with a laser profile-meter (see chapter 5) . The dry snow surfaces in April were very smooth, with mean ah of 2 to 3 mm. The average ah measured in October 1994 was about 7 mm. The correlation length derived from the October measured profiles varies between 48 and 197 mm. In backscattering models the normalized roughness parameters are kah and kL, with k , the incident wave number, as a normalization factor. The values for k used below correspond to the three frequencies of SIR-C/X-SAR: 26.2 m-1 , 1 1 1 .0 m-1 and 201.0 m-1 for L- , C- and X-band respectively. The incidence angle values, ei , used in backscattering simulations vary between 20° and 70° .

The relative permittivity of the snow layer varies with the snow wetness and frequency. In table 6 . 1 permittivity values and penetration depths corresponding to the mean density and snow wetness measured during the SRL-1 and SRL-2 campaigns (tables 5 . 1 and 5 .2) are shown. For the permittivity of dry snow eq. 6 . 1 was used, for wet snow eq. 6 .2 , and for the maximum penetration depth eq. 6.4 may be considered a good approximation. In practice, the penetration depth for dry snow is significantly lower because of internal boundaries and volume scattering.


6.2 .2 Model for surface scattering

58

The backscattering by a random rough interface between two media can be described with the Integral Equation Model (IEM) [F\.mg, 1994] . The range of validity of the IEM is wider than for the small perturbation model (SPM) or the Kirchhoff model (KM) . The surface scattering coefficient consists of two terms: single scattering, rJ8, and multiple scattering , rJM, backscattering coefficients. Most natural surfaces have a small r .m.s. slope, m. Hence, if m < 0 .4 , the single scattering term dominates.

For small and medium roughness (krJh < 1 . 5) the single scattering backscattering coefficients for like polarization are given by

(6 .6)

where kx = k cos Bi, kz = k sin Bi , pp = vv or hh,

(6.7)

and wn( -2kx , 0) is the Fourier transform of the nth power of the surface correlation function. The Kirchhoff and complementary field coefficients for hh and vv polarizations are

fvv

Fhh( -kx , 0) + Fhh(kx , 0) =

where R11 and R1_ are the Fresnel refiection coefficients at vertical and horizontal polarizations, respectively. These expressions are valid under the additional assumption that the local incidence angle in the Fresnel refiection coefficients in !PP can be replaced by the incidence angle ei . The range of validity of this assumption varies with the type of surface correlation function and the value of the dielectric constant. For a modified exponential correlation function the condition is (krJh) (kL) < 1 .6-JEr, while for a gaussian surface it is more restrictive: (krJh) (kL) < 1 .2-JEr or kL < 5. For a surface with an exponential correlation function a similar condition is not given, therefore in this case the following two restrictions of eq. 6.6 will be considered, for roughness and for the evaluation of Fresnel coefficients at ei , respectively:

krJh < 1 . 5 and (krJh) (kL) < 1 .6-JEr (6.8)

These conditions become very restrictive at high frequencies, e.g. at X-band, and small er , e.g. for snow, as illustrated in figure 6.3. However, the exact range of validity of eq. 6.6 is not known [Fung, 1994] .

Starting from the expressions given for the elements of the Stokes matrix for single scattering the HHVV complex correlation coefficient (see definition 3 . 16) can


4,---------------------,

3

1

5

IEM validity range kcrh<1.5

kcrhkL < 1.6 ..Je,

10 15 kL

20 25

59

Figure 6.3 : Regions of validity of the IEM model in kah - kL space, for an exponential correlation function. In the case of snow (er about 2 .) the restriction is much stronger than for wet soil (er about 16 . ) . At X-band few natural surfaces are expected to fulfil these conditions.

be calculated as

2::=1 a�n IJ:h 1:;: ;b wn (-2kx , 0) Phhvv = -------2 --=::..:..::::..:...._...;,:__:.:;,;:....1-7-:/2

�-__:_--__:_-2

_______ 1,....",./2 [2::=1 (J�n I I,�h l *' wn( -2kx , 0)] [2::=1 O'�n I I�v l *' wn( -2kx , 0)] (6.9)

For cross-polarization the single scattering backscattering coefficient is zero. Gontributions must come from multiple scattering terms which can be calculated by

k2 oo oo (k2 2t+m - exp (-2k2a2 ) """' """' z ah (6 . 10) 167T' z h L L n!m!

n=1 m=1 j [ IFqp (u, v) l 2 + Fqp(u, v)F;p ( -u, -v)] wn(u - kx , v )Wm(u + kx , v)dudv

The complementary field coeffi.cients for hv or vh polarization in the backscattering clirection are given by [Fung, 1994] (page 210)

Fhv (u, v) = uv

{2 [ 1 - R _ 1 + R] ( 1 _ R) + 2 [ 1 + R _ 1 - R] ( 1 + R) k cos ei q qt q qt _ [ 1 - R _

( 1 + R)J.Lr ] ( 1 + R) _ [ 1 + R _ (1 - R)cr ] ( 1 _ R) q qt q qt - - - - (1 + R) - - - - (1 - R) } [ 1 - R 1 + R] [ 1 + R 1 - R l

q qtEr q qtJ.Lr where R = (Ru - Rj_)/2, q = (k2 - u2 - v2) 112 , qt = (kt - u2 - v2) 112 and kt = k.,JE;. The interaction between different frequency components of the surface is shown through the u, v integration in eq. 6 . 10.

Chapter 6. Backscatter modelling of snow covered terrain 60

The model depends on the type of surface correlation function, roughness scales, and the clielectric constant . Different correlation functions and their effect on surface scattering are presented in [Fu.ng, 1994] . For this study the exponential correlation function was used

p(�, () = exp( - ( 1� 1 + 1 ( 1 ) / L) (6. 1 1 )

and its roughness spectrum

(6 . 12)

where L is the correlation length. This correlation function is not differentiable at the origin, thus the surface it characterizes does not have a r .m.s. slope (m = ah #(0)) and the surface does not allow theoretical analysis. Despite this disadvantage this correlation function is often used in practice and shows good agreement between measurements and theoretical calculations.

IEM model behavior. To illustrate the IEM model behavior the dependence of its a�h ' a�v ' a�v ' and I Phhvv l on the normalized r.m.s. height, kah , correlation length, kL, permittivity, En and frequency have been considered. The input parameters and the range of ()i are chosen to be close to the values obtained through field measurements during SRL-1 and SRL-2. The reference values for the sensitivity study of IEM are: C-band for frequency, about 4 mm for ah , surface permittivity corresponcling to 4%vol for snow wetness, 80 mm and 30 mm for L.

First the effects of kL variation on the IEM model results when kah is fixed at 0.4, are shown in figure 6.4. Although a correlation length of 20 mm is too small for snow it was chosen to demonstrate the effect of L on a0 . An increase of kL causes a decrease of co- and cross-polarized a0 values and a weak decrease of the difference between vertical and horizontal polarizations. The co-polarized backscattering coefficients clrop off faster as kL increases. The effect is more pronounced when kah and Er are larger. The decrease in the level with increasing kL is faster at crosspolarization than like polarization. The change of the magnitude of the correlation coefficient with the correlation length is shown in figure 6 .5 . A strong dependence on the inciclence angle and a weak decrease with increasing kL are observed.

In figure 6.6 the dependence of the backscattering coefficients on the roughness parameter kah is shown. The parameter kL is chosen to be 3.3 . The angular dependence is less pronounced when the surface becomes rougher and the difference between a�h and a�v decreases, indicating that they are approaching the Kirchhoff model . The magnitude of the correlation coefficient is very close to 1 (figure 6.7) when the surface is smooth and has a weak incidence angle dependence. As the surface becomes rougher the level of I Phhvv l decreases and becomes more ()i dependent.

In figure 6.8 the dependence of the backscattering coefficients on the dielectric constant of the snow surface is illustrated. The three chosen permittivity values corresponcl to a liquid water content of the snow of 0 %vol , 2 %vol , and 4 %vol respectively. The magnitude of the backscattering coefficient increases with an increase of the dielectric constant. The change of Er affects a�h and a�v in different ways, e.g. at large incidence angles the change of a�h is smaller than for a�v ' and a difference between them is observed. The magnitude of the HHVV correlation coefficient clepends slightly on the permittivity as observed in figure 6.9 .

llapter 6. Backscatter modelling of snow covered terrain

-10

-20

� �

··+ ··· W (kL=2.2) ... x. ... ffii (kL=2.2) -+-- VV (kL=9.0)

IEM kcr=0.4 e,=2.58-j0.3809

-40 --8-- ffii (kL=9.0) -- VV (kL=20.)

20

ffii (kL=20.)

40 60

ei [deg] 80

-30 r---------------,

-40

IEM ko=0.4 e,=2.58-j0.3809

� �-so 0 b

-60 .... .... HV (kL=2.2)

--t:r-- HV (kL=9.0) -e- HV (kL=20.)

-70 '---''---'�-'--'---'---'---'---'--'--'---'--'---" 20 40 60 80

8i [deg]

61

Figure 6.4: Surface backscattering coefficients for different values o f kL. C:r corresponds to the permittivity of snow with Vw = 4%vol wetness at C-band .

1.0

0.8

� :E 0.6 .Q..

0.4

0.2

0.0

•

kcr=0.4 e,=2.58-j0.3809

20 40

ei [deg]

IEM .. + .. kL=2.2

-+-- kL=9.0

+. -- kL=20.

"': +

60 80

Figure 6.5: Effects of kL variation on surface contribution of IPhhvv J. C:r corresponds to the permittivity of snow with Vw = 4%vol wetness at C-band.

( }hapter 6. Backscatter modelling of snow covered terrain

-30 IEM IEM

kL=3.3 -10

&�'�$>._

kL=3.3 e,=2 .58-j0.3809 E, =2.58-j0.3809

E0'-20 � 0 b

-30

)!(._ &. '1$>._ ··-t.-�:-: :: :: � . a ·x. '+-.. +. a 15>.. x .. ·+ .. '�- a �

x.. '>t &. X. ··7 EI + ... W (kcr=0.22) X. X · · · · HH (ka=0.22)

- �- w (kcr=0.44)

X '+

)(

-40 --B-- I-rn (ka=0.44) X --------- w (kcr=0.66) -6-- HH (kaa .66

60 20 40 80

ei [deg]

-40

-60 .. *.. HV (kcr=0.22) --&-- HV (ka=0.44)

--e- HV (ka=0.66)

20 40 60

ei [deg] 80

62

Figure 6.6 : Surface backscattering coefficients for different values of kcrh. er corresponds to the permittivity of snow with Vw = 4%vol wetness at C-band.

1.0

0.8

� J 0.6

0.4

0.2 kL=3.3

··�·+·+. + � ',+

� �

�

E, =2.58-j0.3809

0.0 20 40 60

ei [deg]

\ �

IEM

+

80

·+ ... ka=0.22

- 4-- - ka=0.44

--------- ka=0.66.

Figure 6.7 : Effects of kO" variation on surface contribution of IPhhvvl· er corresponds to the permittivity of snow with Vw = 4%vol wetness at C-band.


-10 -40 IEM

kcr=0.3 kL=9.0

-20 -50 ,......., ,......., � '"0 � ........... '"0 t,-30 - ...........

0-60 b

IEM kcr=0.3 kL=9.0

&.. & t>

*· ·Ä· ·Ä· ·Ä· ·)i<i ·Ä· ·Ä· ·)i<i· ·)i<i· ·)i<i. '*

-40

-50 20 40 60

ei [deg] + W (e,=1.53-j0.0002) x HH (e,=l.53-j0.0002)

-4--- W (e,=2.22-j0.1906) - tl- - HH (e,=2.22-j0.1906) -- w (&,=2.58-j0.3809) ____._ HH (t,=2.58-j0.3809)

-70

-80 80 20 40 60

ei [deg] .... *. HV (&,=1.53-j0.0002)

--&-- HV (c,=2.22-j0.1906)

--e- HV (e,=2.58-j0.3809)

80

63

Figure 6.8: Surface backscattering coefficients for different values of Er. The values of Er corresponcl to permittivity of snow with Vw = O%vol, 2%vol, and 4%vol wetness at C-band .

1.0

0.8

� :§ 0.6 ..Q...

0.4

0.2 kcr=0.3 kL=9.0

0.0 20 40 60

ei [deg]

IEM

80

. +.. &,=1.53-j0.0002

- 4--- &,=2.22-j0.1906

----- &,=2.58-j0.3809.

Figure 6 .9 : Effects of Er variation on surface contribution of IPhhvvl· The values of Er corresponcl to permittivity of snow with Vw = 0, 2, and 4%vol wetness at C-band.


-10 -40 IEM

cr=3 mm L=80mm

IEM cr=3 mm L=80mm

-20 Vw=4%vol -50 �%�

� fr-� fr-

�-- &.. "d �

b -30

-40

-50

. · .;: : '1-.

X. '1-. . ''1-. X. X.

'+.

X. ... +-.... VV (L-band) ··X·--· HH (L-band)

-+-- VV (C-band) --fl--- HH (C-band) ------ VV (X-band) ____._..,_ HH X-b d

20 40 ei [deg]

'+-._EJ X. '+ X.

X

X

60

"d ........ 0 -60 b

-70

-80 80

&-.&-.

*··)1( .. )1(._*··)1(. ')II(. ')I(. ... * HV (L-band)

')I(_ ---l>-- HV (C-band) ---e- HV (X-band)

20 40 ei [deg]

')!(.

60

"' ""

*·

*

80

64

Figure 6 .10 : Effects of frequency variation on surface backscattering coefficients illustrated at L-, C- and X-band. The values of kL, kCJ and Er change with frequency. The snow wetness is Vw = 4%vol.

In figure 6 . 10 the backscattering coefficients are calculated for fixed surface parameters at L-, C- and X-band. The frequency change causes a variation of kCJh from 0.08 to 0.6 , of kL from 2. to 16. , and of permittivity as shown in table 6 . 1 . All the backscattering coefficients increase with frequency. The separation between (J'�h and ()�V increases with ei but decreases with increasing frequency. At X-band less than 1 dB difference is observed even at large incidence angles. The correlation coefficient (figure 6.11) increases with the frequency because the infiuence of kiJ'h dominates relative to changes of kL and Er· The incidence angle dependence is very weak at 1-band and becomes very pronounced at X-band.

For the situations presented above the calculated cross-polarized backscattering coefficients were very low. The parameter which determined the values for CJ�v was kiJ'h indicating that the depolarization is important only when the surface is rough.

6.2 .3 Volume scattering model

The volume scattering contribution from the snow layer is calculated with a simple first order model which assumes coherent transmission into the lower medium and, after scattering by the volume inhomogeneities, coherent transmission back into the upper medium. The volume backscattering coefficient and the HHVV complex correlation coefficient are given by [Fung, 1994]

vol Phhvv

0.5wTi cos ei [1 - exp ( -2ked/ cos ßt)] Ppp( cos et,- cos et, ?T) (tht�)2 Phhvv

(6. 13)

(6. 14)

where p is either h or v, w is the volume scattering albedo, d is the layer depth, Tp the Fresnel power transmission coefficient for a plane interface, tp the Fresnel

( .'/wpter 6. Backscatter modelling of snow covered terrain

1.0

0.8

� :E 0.6 .9...

0.4

0.2

0.0

cr=3 mm L=80mm Vw=4%vol

20

IEM ··t--:·+--+--+- +--+--+--+ ()>.__()>.__ � �

�

40 60

ei [deg]

\ \

80

65

--+ -- L-band

- 4--- - C-band

----------- X-band.

Figure 6 .1 1 : Effects of signal frequency variation on surface contribution of IPhhvvl at 1-, C- , ancl X-band .

transmission coefficient , related through

Ppp is the pp element of the volume scattering phase function. For scatterers small compared with the wavelength the Rayleigh phase function can be used and the polarization components in case of backscattering are Pvv ( cos Bt , - cos Bt , 7r) = Phh( cos Bt, - cos Bt , 7r) = -Phhvv( cos Bt, - cos Bt, 7r) = 1.5. The backscattering within the volume is polarization independent. Differences between a�lt01 and a��ol appear only because transmission across the layer interfaces differs for vertical and horizontal polarization. The first order cross-polarized components Phv and Pvh are zero in the backscattering case, thus second order calculations are needed to simulate a�v .

The albcdo w = k5/ke, and the optical depth T = ked are the parameters which characterize the layer in the Rayleigh approximation. Assuming randomly orientated spherical ice grains the scattering coefficient k5 is independent of the polarization and depends on snow density, wetness, and particle size:

(6.15)

whcre N is the number of scatterers per volume unit, N = F / (�7ra3), a is the particle radius, c5 is the scatterer permittivity (ice) Eb is the hast permittivity (air) . F is the volume fraction of ice related to the snow density Psw , the ice density Pice and the water density Pw through F = Psw-VwPw.

Pu.e Model behavior. The dependcnce of the volume scattering term on the various

input parameters is shown below. The co-polarized coefficients are calculated at incidence angles between 20° and 70° as functions of particle size, layer depth, ice volume fraction, wetness and frequency. The correlation coefficient I PK!!vv I as given in equation. 6.14 depends only on incidence angle and on the relative permittivity


-10 ,---------------,

;:ö' �

-15

0 -20 b

-25

Rayleigh

li(· ·:l(· '*' '* * ' ==:lc::k::;t.+.

20

·x. ·+. ·x.· ·+

·x

40 60 ei [deg]

80

· ·-+- · VV (a=0.5 mm) · · ··X· · · HH (a=0.5 mm)

- 4--- VV (a=0.75 mm)

- -8-- - HH (a=0.75 mm)

-------- VV (a=l.O mm) ___.__ HH (a=1.0 mm)

C-band P,w=0.3 gcm·3 d=2.5 m &,=1.53-j0.0002 V w =0%

66

Figure 6 . 12 : Dependence of volume backscattering coefficients on particle radius variation.

of the snow, thus the angular dependence is expected to change only with the snow density when the snow is dry and also with frequency and wetness when the snow is wet.

The snow layer reference parameters are chosen according to the field measurements in April 1994. The snow on the glaciers was dry, about 2.5 m deep, with mean density 0.3 gcm-3 grain radius about 0.5 mm. The reference frequency is C-band.

In figure 6 . 12 the change in the volume backscattering contribution with increasing scatterer size is illustrated. According to equation 6 .15 the scattering coefficient ks has a strong dependence on the particle dimension. This causes an increase of the albedo w from 0 . 1 corresponding to a = 0.5 mm to 0 .3 for a = 0.75 mm and 0.5 for a = 1 .0 mm. Although the extinction coefficient ke is dominated by the absorption coefficient ka the weak increase of ke with ks causes an increase in the optical depth T from 0.05 to 0.09 at a constant layer depth of 2 .5 m. As expected, the backscattering coefficients are increasing with total change in level of about 10 dB for the given parameters. Their angular pattern does not change and a��ol is always slightly higher than a�h_01•

The effect of the snow layer depth is shown in figure 6 .13 . A deeper snow layer results in a larger optical depth and the backscattering coefficients increase because more scatterers become available . The optical depths corresponding to the values in figure 6. 13 are T = 0.01 for d = 0.5 m, T = 0.02 for d = 1 .0 m, T = 0 .05 for d = 2 .5 m. The total increase of the backscattering coefficients is about 8 dB and the same shape of the angular dependence is observed in the three situations.

The effect of the ice volume fraction F as predicted by the Rayleigh model is omittecl. Even for low snow density the volume fraction of ice is appreciable (F � 0.2) and snow must be consiclerecl a dense medium [.Tin, 1993] . Aspects of scattering in a clense medium are discussed in the last part of this section.

The presence of 2%vol of liquid water in the snow layer causes a decrease of about 20 clB of the volume backscattering coefficients as observed in figure 6. 1 4. The absorption increases and the albedo decreases from 0 . 1 to 13 x 10-5 and 7 x 10-5

C'hapter 6. Backscatter modelling of snow covered terrain

-20

-22

-24 ,........, � -26 � 0 b -28

-30

-32

-34

Rayleigh

Q-��§:: �& � El� !SI_�

)l( .. *"* '*' :)\: · ·± 'lSl. � -x: :;t :�: "+. b

20

·x.·�.

40 60 ei [deg]

··x.··+

80

·-+ ·· W (d=O.S m) · · · ·X· · · HH (d=O.S m)

- 4--- W (d=l.O m) --&- HH (d=l.O m)

----+--- W (d=2.5 m) ____..__ HH (d=2.5 m)

C-band p.,.=0.3 gcm.J a=O.S mm e, =1.53-j0.0002 V w =0%

Figure 6 .13 : Dependence of volume backscattering coeffi.cients on layer depth.

67

for Vw = O%vol , 2%vol , and 4%vol . At large optical depth (7 = 38.4 and 70.2 for Vw = 2 ancl 4%vol respectively) there is little clifference in backscattering because the term 1 - exp( -27/ cos Bt) reaches saturation. The separation between HH ancl VV backscattering coeffi.cients increases with increasing permittivity at large incidence angles due to the Brewster angle effect [F\.mg, 1994] . The magnitucle of the HHVV correlation coeffi.cient shows a slight decrease with wetness (figure 6. 15) .

Finally the effect of the signal frequency is shown in figure 6. 16. With increasing frequency both albedo and optical depth increase. At L-band w = 0.0016 ancl 7 = 0.01 , at C-band w = 0 . 1 1 and 7 = 0.05 at X-band w = 0.42 and 7 = 0. 14. The volume scattering contribution increases with the frequency from about -46 dB at L-band to -21 dB at C-band and - 1 1 dB at X-band. Because the snow is dry and the snow density constant the permittivity has the same value at the three frequencies and the angular patterns are very similar.

The volume scattering model presented above is based on the conventional radiative transfer (CRT) theory with the assumption of indepenclent randomly distributecl spherical scatterers ancl no correlation between the fields scattered by the different particles. Snow has a large volume fraction of the scatterers, F, of 0 . 1 to 0 .4 in April and the dielectric constant of ice is much larger than that of the background medium. Thus snow is a dense nontenuous medium. Experiments and theoretical stuclies demonstrate that the assumption of independent scattering is not valid for clense nontenuous media [Wen et al . , 1990] . An improvement of the theory is given by the dense medium radiative transfer (DMRT) equations derived from wave theory. The DMRT theory takes into account the interference between scattered field contributions from neighboring particles, the correlation between particle positions ( through the pair distribution function in the Percus-Yevick approximation) , and the effective propagation constant of a dense medium. The form of the equations is the same as the CRT equations, the differences appear in the calculation of the extinction coeffi.cient ke and the volume scattering albedo w. The phase matrix used in DMRT is identical to the phase matrix of Rayleigh scatterers used above. In CRT

Chapter 6. Backscatter modelling of snow covcrccl terrain

-15

-20

-25 ,......., � � -30 0 b -35

-40

-45

-50

Rayleigh

*• ·:1(· "*• ·:I:·'*''*''*' =:1<: :;!(: · + . · x .. + ·x

20 40 60 ei [deg]

80

··+-·· W (Vw=Q%) · · · ·X··· · HH (Vw=Q%)

- +- - W (Vw=2%) - -8-- HH (Vw=2%)

___....__ W (Vw=4%) ____"._ HH (V w =4%)

C-band p..,=0.3 gc m-3 a=O.S mm

d=2.5 m

68

Figurc 6 .14: Effects of the liquid water content of the snow layer on volume backscattering.

1.0

0.8

� J 0.6

0.4

0.2

0.0 20

Rayleigh

'+ �:-- +.

�--+. �··'+. \ ·. � '+ \ · .. <.\ '+ \ ·.

8-\. \ 0

40 60 80 ei [deg]

C-band p.,.=0.3 gc m-3 a=O.Sm m

d=2.5 m

Figure 6 . 15: The dependence of the magnitude of the HHVV correlation coefficient on snow layer wetness predicted by first order Rayleigh volume scattering model.


-10 ,--------------::::----:-:----:-� �leigh -20

;:o "d .......... 0 -30 b

-40

·-+ ·· VV (L-band) .... x.... HH (L-band)

--o-- W (C-band)

- B- - HH (C-band)

------- W (X-band) -- HH (X-band)

d=2.5m p.,.=0.3 gern., a=O.S mm e, =1.53-j0.0002 V w =0%

69

Figure 6. 16: Effects of signal frequency on the volume backscattering coeffi.cients of dry snow.

ke is preclicted as a linear function of the volume fraction F. But the experimental clata revealed an initial strong increase of ke with the volume fraction of the particles until a maximum is reached followed by a decrease when F increased further [Wen et al . , 1990] . The results of DMRT are in agreement with the experimental data. In the limit of a small volume fraction F « 1 , the extinction coeffi.cient and the albedo reduce to that of independent scattering as in the conventional radiative theory. The backscattering coeffi.cients computed using DMRT are lower than those computed with CRT. The reason is that in the case of small particles compared to the wavelength the interference between the scattered fields is destructive and the scattering is lower than for independent particles.

As observed in figure 6 . 12 the level of backscattering is strongly dependent on the particle size . Instead of single sizes, particle size distributions may be used to calculate ke. The Rayleigh probability density function with tail truncation (so that the Rayleigh scattering criterion is fulfilled, ka :::; 1) and mode equal to the size of the particles used in the single size case was applied in numerical simulations at C-band [West et al . , 1993] . The backscattering values calculated using a size distribution were about 10 dB higher than in the case of single sized particles. This effect occurs because larger snow grains in a snow volume are more effective scatterers than smaller snow grains, even if their concentration is low.

6.2 .4 Effects of layering and crusts

Snow packs are slightly anisotropic because clusters of grains are formed in the vertical direction and the layering of the snow cover is usually aligned horizontally ( crusts ancl layers of different densi ties) [ Chang et al . , 1996] . The presence of these structures infl.uences the magnitude of the backscattering coeffi.cients and of the correlation coeffi.cient. Alternation between melting and refreezing produces crusts at the snow surface. Later, due to accumulation, the crusts can be found inside the


snow pack.

70

Active measurements of snow crusts were performed with ground-based scatterometers at various frequencies. [Strozzi et al . , 1997] observed at 5 .3 GHz a slightly high er backscattering coefficient 1 ( rJ0 = 1 cos Bi) for wet snow with a refrozen crust than for wet snow with smooth surface. The increase is due to the diffuse scattering by the large snow grains which are formed in the case of wet snow metamorphism. The co-polarized coefficients lhh and lvv are almost identical and at 40° incidence angle about 5 dB higher for the snowpack with a crust . The difference in the backscattering is more pronounced at large incidence angles. The cross-polarized 1 coefficient is about 2 to 3 dB higher for the surface with a crust .

The interaction of radar signals at 10 .4 GHz with snow crusts was investigated by [Reber et al . , 1987] . An increase from about -23 dB to -15 dB of the HH/VV mean backscattering coefficient was measured during observation of a growing crust. The clifference between the lhh and lvv coefficients was less than 3 dB, therefore only the mean values were reported. For modelling purposes the snow crust can be considered an optically thin random medium with E1 = 3 . 15 or a laycr without losses and with indepenclent spherical ice grains. Backscattering coefficients calculated using the first order Born approximation [Reber et al . , 1987] and the Rayleigh approximation [Mätzler, 1987] are in good agreement with the measurements.

At 35 GHz when the crust thickness was larger than 10 cm [Strozzi et al . , 1 997] reportecl more than 10 dB increase of the co-polarized 1 coefficients and more than 15 clB increase for the cross-polarized I· The large grains of refrozen crusts are efficient Rayleigh scatterers at high frequencies.

At microwave frequencies interference effects may occur between the refiected components of the two interfaces of the thin crust. Interference patterns were observecl by [Mätzler, 1987] with radiometric measurements at horizontal polarization for brightness temperatures at 4.9 and 10 .4 GHz.

Often the snow cover contains parallel, horizontally aligned layers characterized by different densities and wetnesses. At the interface between two layers refraction, interference, and polarization effects may occur [Mätzler, 1987] . The layering is more important for backscattering from dry snow packs because in wet snow the penetration depth is low. Investigations of the stratification of Antarctic firn were made by [West et al . , 1996] who developed a model assuming homogeneaus layers with planar interfaces. The calculated emission was in good agreement with radiometric measurements by [Rott et al . , 1993] . When the layering of the snow pack was not considered, differences of 40 K to 50 K in the predicted and measured brightness temperatures were found.

The anisotropy of the snow pack determines differences in the propagation and scattering of the horizontally and vertically polarized transmitted wave. The HHVV correlation coefficient is therefore a complex number with phase and magnitude clepending on the snow conditions. Variations of IPhhvv l and cphhvv were observed by [Chang et al . , 1996] at 95 GHz during an 8 day experiment with melt-freeze cycles of snow. IPhhvv l increased with increasing water content while cphhvv decreased . During the periocl with snowfall iPhhvv l decreased to near zero while the phase cphhvv exhibited a general increase.


6.2.5 Combined surface and volume scattering effects

71

The total backscattering from a snow layer covering the ground is calculated with a simple moclel in which only the significant terms in equation 6.5 are retained . The surface-volume interaction term is neglected. In terms of backscattering coefficients the contributions are incoherently added as follows:

cos B· a

Otot. = a

Oas (B.) +

a

Ov

ol + __ t y_as y_sa exp ( _ 2k dl COS e ) aOs_q (B ) PP PP t PP B P P e , t pp t cos t (6. 16)

where ar;;/ represents the surface backscattering at the airlsnow interface, a�ol the snow volume backscattering coefficient , and ar;;p the backscattering contribution from the snow I ground interface. The attenuation due to propagation loss through the layer and crossing of the airlsnow boundary are taken into account . For a�5

and ag;g eq. 6.6 was used with parameters corresponding to each interface and an exponential surface correlation function. Assuming independent spherical scatterers a��ol is calculated from eq. 6 . 13 using the Rayleigh phase function. The following simplifying assumptions are made:

1 . only single scattering is important, thus only co-polarized backscattering coefficients are calculated.

2. by using the Fresnel transmission coefficients only the coherently transmitted power across the airlsnow interface is accounted for.

The model is applied for snow covered terrain in two cases, dry and wet snow. The snow pack and ground parameters used in both simulations are shown in table 6 .2 . The total and each of the individual contributing terms are shown to illustrate the range where they are dominant or ncgligible. In figure 6 . 17 the backscattering from terrain covered with dry snow is shown at L-, C- and X-band. The top interface is smooth and the dielectric discontinuity between air and dry snow is small . The scattering at this boundary is significantly lower than at the snow I ground interface. The volume scattering contribution (figure 6. 16) is important only at X-band because at C- and L-band the albedo and optical depth are low. The dominant term at C- ancl L-band originates from the snow I ground interface where the dielectric cliscontinuity is large. At L-band this term represents 99% of a�;ot at all incidence angles, at C-band it decreases to 83% and the volume contribution increases while at X-band the volume backscattering dominates above 45° (figure 6. 18) . When the snow cover is slightly wet (Vw = 1%) the total backscattering is much lower at C- and X-band (figure 6. 19) . Due to the large penetration depth at L-band the snow I ground interface is still the main factor in the total backscattering. At lower frequencies this interface is not seen by the incoming radiation because the optical clepth is very large. At C- and X-band at low incidence angles the airlsnow surface provieles the main contribution to backscattering, at larger incidence angles the volume scattering becomes dominant (figure 6.20) . The lack of separation between HH and VV backscattering coefficients is due to the volume scattering contributions from the snow cover.

Clwpter 6. Backscatter modelling of snow covered terrain

Parameters Units Dry snow Wet snow (fig. 6. 17) (fig 6. 19)

Snow pack properties

d [m] 2 .5 0 .3 Psw [gcm-3] 0.3 0.3 F 0.33 0.32 a [mm] 0.5 0.75 Vw [%volj 0 1

1 . 98 - j0.0568 (L) Esnow 1 . 53 - j0.0002 1 .71- j0.0953 (C)

1 .65- j0. 1 151 (X) a as [mm] 4. 4 . las [mm] 1 10 1 10

Ground properties

1 1- j2.0 (L) 1 1 - j2.0 (L) Eground 10- jl .9 (C) 10- jl .9 (C)

9- j2 .5 (X) 9- j2.5 (X) a sg [mm] 8.0 8.0 lsg [mm] 80 80

72

Table 6 .2 : Parameters for dry and wet snow pack used as input to the backscattering model of snow covered terrain. cground was determined using the empirical relation of [Hallikainen et al., 1985]

Cbapter 6. Backscatter modelling of snow covered terrain

0

-5

� -10

0 b -15

Total backscattering

'!\ + 'a: x:-. ·.+

X. +. X.

'+.

X.

-25

-30 S' ::2. 0 -35 b

-40

-45

-50

� '&& '$..

lSI.'<Sl., '+. !SI.'$..

+. Ol,'& +. 0 X. ...

'!<�. "!;t.

'l!i. :l::. 'e, 'ä, ·:\!::. ·:+ �� X::-+

20

'x>+. x·'�-. X. ....

X.

40

ei [deg]

.... X,

0

-5

� -10 0 b

-15

� e. '[!

+. X. '+,

+ x.

60 80

Snow I ground interface

+ x·· '

·.''+-.

x. '+ ·x ·+ . . . +

X. T

·x.

20 40 60

ei [deg]

· + VV (L-band) · .. x... ffi:I (L-band)

-4-- VV (C-band)

--B-- HH (C-band)

- VV (X-band) ____...._ ffi:I (X-band)

d=2.5m p.,.=0.3 gern"' a=0.5 mm E,=1.53-j0.0002 V w =0%

73

80

Figure 6. 17: Scattering terms of soil covered by dry snow at L-, C- and X-band (for parameters in table 6.2) .

�100 100 �nd

00 � SG L-band X-band � BO BO BO

bO V ·� .,

60 60 60 :t: .. � u .. .0 Ci

40 40 40 SG

!: 0 � 20 20 20 ::l

] �� !: V AS AS 8 0 0 0

20 40 60 BO 20 40 60 BO 20 40 60 BO

a;[deg) a;[deg) 8;[deg)

Figure 6 .18 : Gontributions of each scattering term to the total backscattering in the case of a dry snow covered terrain, at 1-, C- and X-band. AS . . . scattering from the air/snow interface, V . . . volume scattering of snow pack (independent Rayleigh scatterers) , SG scattering from snow j ground boundary after attenuation by the snow layer.


-10 x-. ··:+. x . .. .... -15 X ...

X � -20 j:Q � 0 -25 b

-30

-35

-40 20

-20

Total backscattering

'"· '1-. ·x. '1-. ·;.. x.

40

ei [deg]

'1-. ... "+

X

60

-15

-20

� -25 j:Q �

0 -30 b -35

-40

-45 80 20

-10

Air/snowinterface

40

ei [deg]

x. "+ ">< 60

Snowvolume x:-. Snow/ ground interface ·.+.

-30

'iQ' � 0 -40 b

-50

-60

� lll-li!-fÖl-"'=i!!o�

)1(--)1( . . :1( .. ;1:

20

��a$.. El.� El

'*' '*' :)1:: :;!(: :t :�: "+

40

ei [deg]

·x

60

-15

'iQ' -20 � 0 b

-25

-30

-35 80

+- VV (L-band)

· · ·X · HH (L-band)

-+-- VV (C-band)

--El-- HH (C-band)

'x . . ·. +. x. "+.

x.

s. \

'Q. '9.

20

"+ +.

x. '"· .. X.

X X

\ 40

ei [deg]

--- VV (X-band) ----4-- HH (X-band)

+. "+

+ x.

x. ·x

60

74

80

80

Figure 6.19: As figure 6.17, but for soil covered with 30 cm of slightly wet snow (Vw = 1%v01). The input model parameters are given in table 6.2.

�100 � L-band

j 80 � bO c ·c "'

60 :t:: .. � u .. � 40 "" 0 c A5 0

'.tl 20 � ::1 �

'B c V 0

u 0 20 40 60 80

ei[deg]

100

80

AS 60

40

20

0 20 40

ei[deg]

C-band 100

80

60

40

20

0 60 80

X-band

V

5G

20 40 60 80

ei[deg]

Figure 6.20: As figure 6.18, but for slightly wet snow case (Vw = 1 %vol) (parameters in

table 6.2).

C:hapter 6. Backscatter modelling of snow covered terrain 75

6.3 Conclusions for the interpretation of SIR-C j X-SAR measurements

In this section some comments on the backscattering predictions directly related to l.he signatures derived from SAR data are made. Due to the Brewster angle effect, l.heory predicts large Separation between O'Rh and a�v at large incidence angles for smooth surfaces. This difference is visible, especially at L-band. The SAR data do not confirm this prediction since the observed difference between the co-polarized hackscattering coeffi.cients is about ±1 dB.

According to the field observations ( chapter 5) in April'94 the glaciers were covered with dry snow, 2 to 3 m deep. The signatures derived from SAR data (sections 7.3 and 7.6) differ from the calculations with the single layer model presented in section 6 .2 .5 for dry snow. The snow pits showed that real snow cover is not homogeneaus and randomly distributed ice crusts are present . At a given incidence angle their infiuence on the backscattering coeffi.cients differs with their thickness and the signal wavelength (section 6.2.4) . The thickness of the crusts is of the order of centimeters therefore they will infiuence the backscattering coeffi.cients in particular at C- and X-band.

Another difference relative to the model presented in the previous section is that the firn or ice below snow should be treated as infinitely deep covers. Due to the small clielectric contrast, a large amount of the incoming radiation penetrates these media. The contributions of volume scattering in dry firn and of scattering at layers and crusts present in snow and firn should be added to the one layer model . Therefore at L-bancl the real backscattering coeffi.cients on the accumulation areas in April are up to 10 dB larger than theoretical values. At X-band at incidence angles above 50° theory and observed values of 0'�: agree within ±1 dB because, as shown in figure 6 . 18 , scattering in the snow volume has an important contribution which becomes dominant at incidence angles above 45° . On the ablation areas the backscattering coeffi.cients derived from SAR data are lower than on the accumulation areas. This may be explained by the reduced volume scattering in ice compared to firn.

Multiple scattering effects are not taken into account in the single layer model . Since the effects of volume scattering and loss through the dry snow cover are negligible, multiple scattering at the snow/firn (snow/ice) and firn (ice) volume are responsible for the relatively high cross-polarized backscattering coeffi.cients on the glaciers.

Model predictions compared with ERS- 1 SAR and ship based radar measurements over multiyear sea ice were published by [Beaven et al . , 1997] . The singlelayer volume scattering model is based on the modified radiative transfer theory with phase matrix for closely packed Mie scatterers. The rough boundaries are modelled using the IEM with an exponential correlation function. For frozen sea-ice covered with dry snow the model predicts C-band co-polarized a0 between -8 dB and -18 dB and cross-polarized 0'0 between -26 dB and -36 dB for incidence angles from 20° to 70° . These values are close to the SIR-C backscattering coeffi.cients clerived from the ice site (section 7.6) at like-polarization and about 8 dB lower at cross-polarization.

In April'94 the unglaciated areas were covered with up to 1 . 5 m snow. The depth


0

-5

� -10

0 b -15

-20

-25 20

Moraine

40

ei [deg] +· VV (L-band)

---<>-- W (C-band) --- W (X-band)

0 SRL-1

-5 � ' �

.......... � -10

0 + b -15 '1-

+ X + c

"+ L '+ -20

-25 60 80 20

d=0.5 m Vw=O% crh=10 mm (bottom)

m"=0.2

Cultivated meadow

'G..

+

'Q_� c"�S>..

'\So.. �� + c

"+. L·"+.

'1-.4

40

ei [deg]

c

t. 60

d=0.5 m Vw=O%

�

"+

crh =5 mm (bottom) m"=0.2

76

SRL-1

'o

80

Figure 6 .21 : Comparison between SAR backscattering coefficients at X- , C- and L-band on snow covered ice-free sites in April'94 (SRL-1 ) and model predictions.

and wetness of the snow cover varied locally and with altitude. SAR signatures for cultivatecl meadow and moraine (section 7.7) are compared with model calculations for 0.5 m deep dry snow (figure 6.2 1 ) . Differences between moraine and meadow backscattering coefficients may be caused by different ground surface roughnesses. Therefore for model predictions the r.m.s. surface height at the bottarn interface was assumed to be 10 mm for moraine and 5 mm for meadow, and the same soil maisture mv = 0 .2 was considered. According to field measurements carried out in the NW part of the test site at low altitudes, where vegetation is dominant, slightly wet snow was present above the ground [Mätzler et al . , 1997] . This may be another reason for the low backscattering coefficients at the meadow site.

In October'94 the snow on the accumulation areas was slightly wet and the air/snow interface rougher than in April'94. Modeled and SAR signatures are illustrated in figure 6 .22. The scattering mechanisms are explained in section 6 .2 .5 . Although the SAR (}0 values differ by several dB from those predicted by the model , the largest backscattering coefficients are observed at 1-band due to the contribution at the snow/firn interface and at X-band due to the large roughness of the air/snow interface. The ablation areas were snow free during the second experiment and very rough. Because glacier ice is a very complex medium, simple models cannot predict the backscattering behavior of this surface and volume and no comparisons with SAR data are shown here.

The unglaciated areas were snow free in October and dominated by surface scattering. Therefore only the IEM was used to model (}0 values. The soil maisture measured during the experiment in the east of the test site was about mv = 0.3 the surface roughness was considered to be the same as for the SRL-1 data. The backscattering coefficients at C-band are larger than at X- and 1-band for the moraine site


:CO � 0 l?

-10------------------------� + 't

-15 + '+.

\ -20 (\

� �

-25

-30

-35 20

Snow L

c · .+ X };.

'{. ·'1-.L C

'!-X '!-c �

'<SI.. � >SI.. �

40 60

ei [deg]

SRL-2

� c '+

� '0

80

· · · ·+ W (L-band) - -o- - VV (C-band) - VV (X-band)

d=0.3 m P.w=0.3 gcm-3 a=0.75mm Vw=l%

crh =7 mm (top)

77

Figure 6.22: Comparison between SAR backscattering coefficients at X- , C- and L-band on the accumulation area in October'94 (SRL-2) and one layer model predictions.

clue to the large roughness, as shown by the trend in the model. In reality these surfaces can be rougher than consiclered here and Kirchhoff scattering models shoulcl be used. Often at alpine surfaces two scales of roughness are observecl, roughness of the meter scale related to individual rocks, ancl the smaller scale roughness of the soil . For meadow surfaces the measurecl alt varies between 4.6 mm ancl 28.6 mm, the correlation length, L, between 50 mm ancl 200 mm [Öhreneder, 1995] .

For all surface types the magnitucles of the correlation coefficients derived from SIR-C clata are lower than unity at small inciclence angles, and the angular patterns are significantly different from those preclictecl by first order scattering theory. Surface ancl volume multiple scattering effects, which were not considerecl in the simple models cliscussed above, may contribute also to decorrelation between the HH ancl VV elements of the scattering matrix.


0 Mo raine SRL-2 0 Cultivated meadow

-5

� -10

0 b -15

-2 0

-2 5

'\ c

+ +

'+.

'+-. ... . . ... .

+. + '+

'+

··+- VV (L-band) cr"=10 mrn ---o-- VV (C-band) m"=0.3 - VV (X-band)

2 0 40

ei [deg] 60

c iS

80

-5 � \ '\ ,......... "'-� -10

0 b + -15 +. '+

-2 0 crh=5mm

-2 5 m.=0.3

2 0

�

'+

� ��

L � '1-.

+. L X . .... ...... c

40

ei [deg]

!!;.

60

�

'+

78

SRL-2

\:>

80

Figure 6.23: Comparison between SAR backscattering coefficients at X-, C- and L-band on ice-free sites in October'94 (SRL-2) and IEM model predictions.

Chapter 7

Microwave polarimetric signatures of alpine terrain

The elements of the covariance matrix ( defined in chapter 3) predicted by theoretical models depend on several parameters : the incidence angle, frequency, polarization and physical properties of the target . The complexity of the acquired data and the range of natural conditions in the test site during the two SRL missions enable the experimental study of these dependences including the impacts of seasonal and short term changes.

In this chapter results concerning the influence of the local incidence angle and of target properties on the elements of the covariance matrix are presented. The mean values of the elements of the covariance matrix were calculated at incidence angles from 20° to 70° for steps of 5° for each surface type. The local incidence angle dependences of the co- and cross-polarized backscattering coefficients, and of the magnitude of the HHVV complex correlation coefficient were studied at C- and Lband, and of the VV backscattering coefficient at X-band. The seasonal differences of the backscattered signal were analyzed, as well as the variations between the different data takes within each experiment.

Due to the different radar imaging geometries of the five data takes one must take the following into account when the signatures are interpreted:

1. identical points in the scene are viewed at five different incidence angles,

2. for a specified value of the incidence angle different areas (pixels) of thc specified target type are imaged by the five data takes. Within a given target class the physical properties usually show some variability, which may depend on altitude, slope inclination or sun exposure.

7.1 Auxiliary data for signature analysis

For signature analysis thematic and topographic information from various sources was used . Thematic information includes the surface classes in the scene, topographic information consists of the local incidence angle map, layover and shadow zones.

79

Chapter 7. Microwave polarimetric signatures of alpine terrain

Figure 7. 1: Geocoded Landsat TM channel 3 image of the test site Ötztal acquired on 1( August 1992.

The extent of the main classes of natural surfaces in the Ötztal scene was delim ited basecl on a classification algorithm applied to Landsat TM data [Nagler, 1996 acquired two years earlier, on 16 August 1992 (figure 7. 1 ) .

Using Shuttle orbit parameters the image was transformed into the slant rang( geometry of each clata take and afterwards the transformed images were classified Obviously, when geocoded SAR data are used this transformation is not necessar3 and the classification is performed directly on the geocoded Landsat image. How ever, signature studies are preferably carried out in radar geometry in order tc preserve signal statistics. Masks for snow, firn, glacier ice and unglaciated surface1 were obtained in radar geometry and in map projection (figure 7 .2) .

Using complementary information from field observations and aerial photograph1 taken during the two campaigns, the principal types of natural areas present ir October 1994 were mapped:

on the glaciers

• snow accumulated during the last year (the accumulation area),

• glacier ice (the ablation area) ,

on the ice free areas

• vegetation, consisting of alpine grass and shrubs,

• bare soil, including rocks and moraines.

Chapter 7. !vficrowave polarimetric signatures of alpine tenain

Figure 7 .2 : Classification of the Landsat TM image from 16 August 1992: white . . . snow, black . . . firn, grey . . .ice, dark grey . . . rocks and moraines, light grey . . . vegetation.

The firn or neve, snow from former years, although it is sometimes observed on the glaciers in late summ er, did not cover significant areas in Ötztal at the end of the summer 1994, as a consequence of the negative mass balance in the previous hydrological years1 and of the heavy snowfall in September (see chapter 5) . Forest and man made targets are outside the high resolution DEM, therefore no local terrain corrections could be performed on the backscattering coeffi.cients of these targets .

When the relief is very complicated the angle between the radar beam and the imaged surface may cover values between oo and 90° depending on its orientation relative to thc incident radar beam. For X-SAR geocoded (GTC products) data local incidence angle maps are available as GIM products (section 2 .2) . For SIR-C and X-SAR data in slant range geometry local incidence angle maps were derived from a DEM with 25 m resolution and Shuttle orbit parameters using the same polynomials as for transforming the Landsat scene. Shadow and layover areas, the corner reftectors and their side lobes, and heavily crevassed zones are subtracted from the initial calculated masks. The incidence angle correction was applied pixel by pixel by multiplying the uncorrectcd backscattering coeffi.cients with the ratio sin ed sin eP, where ei is the local incidence angle derived from the DEM and eP the processor incidence angle (for details sec section 4. 1 .2) .

Fully polarimetric data allow the retrieval of polarimetric parameters such as the complex correlation coeffi.cient between the two like-polarized measurements ( defined in chapter 3) . The width of the probability density function (PDF) of the phase cphhvv is controlled by the magnitude, IPhhvv J , of the correlation coeffi.-

1 In the Alps the hydrological year is the period between 1 October and 30 September of the following ycar.

Chapter 7. Microwave polarimetric signatures of alpine terrain 82

cient [Quegan et al . , 1994] . In addition, it was shown that the mean and standard deviation of the phase are not good descriptors of the representative rphhvv for a clistributed target . Because I Phhvv l can be corrected for first and second order noise effects its behavior will be discussed below. I Phhvv l is a polarimetric measure based on the ratio of two measurements, hence it is less affected by radiometric calibration errors than the backscattering coefficients [Souyris et al. , 1995] .

7.2 Review of published results on polarimetric

signatures

Previous work on the analysis of polarimetric SAR data over snow and alpine targets is mainly based on AIRSAR data and on ground based scatterometer systems. Over the test site Ötztal there were two AIRSAR overflights, in August 1989 and June 1991 . The polarimetric properties of the main targets (snow, glacier ice, and snow-free surfaces) were investigated by [Rott, 1992] and [Rott and Davis , 1 993] at a given incidence angle with the aim of finding suitable parameters for classification. [Shi and Dozier, 1992] plotted the backscattering coefficients, the correlation coefficient and the coefficient of variation as function of the incidence angle for high ancl low wetness snow in order to find a relationship between the backscattered signal and snow wetness.

Backscattering coefficients measured by AIRSAR over the test site Mammoth, California in March 1991 for ground covered by dry snow were published by [Shi et al . , 1993] . AIRS AR data collected over the Greenland ice sheet were analyzed as reported by [Jezek et al . , 1993] and [Rignot et al . , 1993] .

[Strozzi, 1996] measured VV, HH, HV and VH backscattering coefficients, /, with two Network-Analyzer based scatterometers at 5 .3 and 35 GHz, from different snow covered grass fields. The measurements were performed for incidence angles ranging from oo to 70° , but only the data between 20° and 60° were analyzed. From the four test sites, two are situated at altitudes below 1000 m, in the Swiss Central Plain, and two above 2000 m, in the Alps, revealing two distinct snow cover situations. The experiments were carried out during several periods from December 1993 to January 1996. Tagether with ground information, the seasonal variations of the backscattering coefficients are used to identify signatures of object classes. The relationships between the backscattering coefficients and snow parameters like height , liquid water content , water equivalent and thickness of refrozen crusts were investigatcd.

One set of these measurements was carried out during the SRL-1 experiment on a snow covered alpine grass site in Kaunertal [Mätzler et al . , 1997] . During SRL-2 , ground based microwave scatterometer/radiometer measurements at C- and X-band were performed on a snow free meadow in Obergurgl [Öhreneder, 1995] . The SAR derived backscattering coefficients are always higher than the comparative ground measurements (2 to 6 dB for the like-polarized and about 10 dB for the crosspolarized signal) because the SAR signal is influenced by large point scatterers (rocks) usually present in the resolution cell, whereas for the experiments at the test site rocks and irregularities at the surface were avoided.

Similar to the analysis presented in this chapter [Luckman and Baker, 1995]

( !hapter 7. Microwave polarimetric signatures of alpine terrain 83

made a study of the infl.uence of topography on radar backscattering from conif< � rous forest and upland pasture based on AIRSAR data. Additional data, such as high resolution DEM and forest management data (plantation stocking maps) , were 1 1sed to mask the two types of vegetation. The variation of 0'0 with local incidence angle was calculated for co- and cross polarizations at all three AIRSAR frequencies.

A detailed investigation of the dependence of the SAR polarimetric parameters on the local incidence angle at different frequencies over targets in alpine terrain has uot yet been reported.

7.3 The angular dependence of backscattering

The dependence of co- and cross-polarization backscattering coefficients and of I Phhvv l on the incidence angle was studied with SSC data at X-, C- and L-band. In this section the results obtained by analyzing DT 46, with an incidence angle <Lt the image center of 50° , are discussed. In appendix A the corresponding plots derived from DT 14, 18 and 78 are shown.

The seasonal differences between the conditions (see chapter 5) in April'94 (SRL-1) and October'94 (SRL-2) proviele the possibility to study the signatures of natural targets in at least two distinct situations. For winter conditions (April'94) , volume scattering from the dry snow cover and surface scattering from the snow-ground interface dominate the backscattered signal. In October 1994, representative for late summer conditions, the unglaciated areas were snow free, the glacier ice was exposed and the snow on the accumulation area was wet . Therefore the surface scattering at the air-ground interface and air-snow interface as well as volume scattering may be important. Generally, surface scattering dominates at small incidence angles and volume scattering becomes important as the incidence angle increases. Theoretical models predict a strong angular dependence of the co-polarized backscattering coefficients of a target with surface scattering as the dominant mechanism (wet snow) at incidence angles between 5° and 50°, and a weaker angular dependence for a target with an important volume scattering contribution (dry snow) [F\mg, 1994] . The correlation coefficient , IPhhvv l , decreases with increasing incidence angle and depends on the dominant scattering mechanisms of the target. When only single surface scattering or volume scattering dominates, the correlation coefficient is close to 1 , when more scattering mechanisms are involved, the correlation coefficient decreases [Shi and Dozier, 1992] .

7.3. 1 Incidence angle dependence of backscattering April'94

0

lll

As described in chapter 5 a 2 to 3 m deep, dry snow layer covered the glaciers in April'94. Up to 1 . 5 m snow was observed on the unglaciated areas. In the case of dry snow the air-snow surface interaction results only in a small contribution to the backscattered signal. The penetration depth in the dry snow pack can reach several hundred wavelengths ( chapter 6) and the interaction at the snow-ground interface is important, as well as the volume scattering from the snow layer. Large co- and cross-polarized backscattering coefficients and small correlation between HH and VV

( ,'IJapter 7. Microwave polarimetric signatures of alpine terrain

5

0

-5

Cii' :!::!. 0 -1 0

b -1 5

-20

-25

5

0

-5

Cii' :!::!. 0 -1 0

b -1 5

-20

-25

ACCUMULATION AREA

SRL-1 DT 46

W X-band(+) W C-band(6) HV C-band(O) w L-band(e) HV L-band(O)

20 40 60

ei (deg)

ROCKS (snow covered)

� 20 40 60

ei [deg]

80

80

5

0

-5

Cii' :!::!. 0 - 1 0

b - 1 5

-20

-25

5

0

-5

Cii' :!::!. 0 - 1 0

b - 1 5

-20

-25

GLACIER ICE (snow covered)

20 40 60

ei [deg) 80

VEGETATION (snow covered)

20 40 60

ei [deg] 80

84

Figure 7 .3 : Incidence angle dependence of co- and cross-polarized backscattering coefficicnts at X- , C- and 1-band for different types of natural surfaces derived from SRL-1 DT 46. The dass " rocks" includes all unvegetated ice-free surfaces.

scattering matrix elements are expected, especially on the glaciated areas, if volume scattering dominates.

The co- and cross-polarized backscattering coefficients. Figure 7.3 shows the angular dependence of the VV backscattering coefficients at X- C- and L-band, and the cross-polarized backscattering coefficients at C- and L-band, of the four surface classes described in section 7. 1 .

The difference between a�;; and a�� is less than 1 dB. At L-band the refl.ecting interfaces are less rough in relation to the wavelength, and the volume scattering albedo ancl optical clepth are smaller than in the other frequency bancls. Therefore a�v at L-bancl is 2 to 4 dB lower than at C- and X-band. At C- and L-band a�h is at most 1 dB smaller than a�v . The cross-polarized backscattering coefficients are 2 to 4 dB lower at L-band than at C-band, because volume scattering and internal layers are more effective scatterers at smaller wavelengths.


On the glaciers the main contribution to the backscattered signal originates from the refiection at the snowfice interface and firn or ice volume. At X-band the volume scattering in the snow layer is dominant at incidence angles above 45° (section 6.2 .5) . The refrozen firn is a more effective scatterer than the ice and this results in higher backscattering coefficients for the accumulation than for the glacier ice areas. On the accumulation area high cross-polarized backscattering coefficients are observcd: aR� are between -7 dB and - 15 dB, a�� between -9 dB and - 17 dB.

In comparison with the glaciated areas, the snow covered rocks and vegetation areas reveal a different shape of the angular dependence. The co- and cross-polarized a0 of the unvegetated surfaces is only about 1-2 dB higher than for the vegetated areas. These differences are so small because the vegetation was dormant and also because the roughness of thc vegetated surfaces in Alpine terrain is high. Part of the differences between vegetated and unvegetated surfaces may result from differences in the snow conditions, because at lower elevations, with predominantly vegetated surfaces, the snow pack was partly wet (see chapter 5) .

The magnitude of the HHVV correlation coefficient. Due to the large penetration clepth of the radiation in dry snow its structure strongly infiuences the strength of the correlation between the co-polarized elements of the scattering matrix. For dry snowpacks (high volume scattering contribution) low magnitudes of the correlation coefficient , I Phhvv l , are expected [Shi and Dozier, 1992] .

Figure 7.4 shows the angular depenclence of I Phhvv l at C- and L-band for the four surface classes derived from DT 46 SRL-1 .

I n April the correlation coeffi.cient at C-band was equal to or lower than that at L-bancl the difference being more pronounced for the glaciated areas. The behavior of the correlation coefficient is infiuenced by surface and volume scattering. According to first order surface scattering models (section 6 .2 .2) I Phhvv l increases with the wavelength. A first order Rayleigh volume scattering model does not preclict a significant change of IPhhvv l with the frequency if the snow is dry (section 6 .2 .3) . Multiple scattering effects in the snow cover and internal structures may also contribute to decorrelation, especially at small wavelengths.

On glaciers, ice areas show larger correlation coefficients at C- and L-band than accumulation areas. I Phhvv l is larger on ice-free surfaces than on the glaciers because the snow layer is less deep. At Bi = 20° lPhhvv l is about 0.5 for glaciated areas while for unglaciated areas I Phhvv l it is about 0 .7 to 0.8 .

The correlation coefficient of vegetation is slightly higher than of the rocky areas. This may be an effect of differences in surface roughness, as found by [Borgeaucl and Noll, 1994], who analyzed L-band airborne polarimetric data of agricultural fields with different roughness. They found a high negative correlation between I Phhvv l and the roughness of the bare soil fields. Moreover, comparing IPhhvv l from tall vegetation (forest, crops) with bare soil they observed that a medium with strong multiple scattering effects reduces the HHVV correlation coeffi.cient to a greater degree than a very rough surface. However, the decorrelation effects of the sparse vegetation above the tree line are less important than surface roughness effects.

This may also explain the difference of I Phhvv l for accumulation and glacier ice areas. For glacier ice the interface between the transparent, dry snow and the rough ice surface is the main contributor to the backscattered signal. In the accumulation


1 .0

0.8

� 0.6

..c ..c ..Q...

0.4 -

0.2 -

0.0

1 .0

0.8

� 0.6

..c ..c a..

0.4 -

0.2

0.0

ACCUMULATION AREA

C-band(.6.) SRL-1 DT 46

L-band (e)

20 40 60

ei [deg]


20 40 60

ei [deg]

80

80

1 .0

0.8

� 0.6

..c

st 0.4

0.2

0.0

1 .0

0.8 -

� 0.6

..c ..c .Q... 0.4

0.2

0.0


20 40

ei [deg] 60 80


20 40

ei [deg] 60 80

8G

Figure 7 .4 : Incidence angle dependence of the magnitude of the HHVV complex correlation coefficients at C- and L-band for different types of natural surfaces derived from SRL-1 DT 46.


area the grainy firn below the winter snow pack causcs in addition a significant volume contribution to the backscatterecl signal . Decorrclation is more effective at C-bancl clue to the grain size dependence of the volurne scattc�ri ng.

7.3.2 Incidence angle dependence of backscattering in October'94

In early October'94 the glacier ice and the unglaciated areas were almost cornplctcly free of snow. On the accumulation area of the glaciers the snow surfaces were wet. These are called late summer conditions.

When the snow surface is wet the absorption of the medium increases, the albedo clecreases and the volume scattering is weak. The cross-polarized backscattering coefficients are very low due to the small penetration depth being reduced to the order of centimeters (chapter 6) . The co-polarized backscattering coefficients are expected to be low because the dielectric lasses are high and the snow surfaces are comparatively smooth. On the unglaciated areas surface scattering is the dominant scattering mechanism, the permittivity of wet soil is relative high and almost all the racliation is refl.ected at the air / soil interface.

The co- and cross-polarized backscattering coefficients. In the October data a weak frequency dependence of the co- and cross-polarized backscattering coefficients for each surface type is observed, as shown in figure 7 .5 . On the accumulation areas the cross-polarized backscattering coefficients are higher at L-band than at C-band. For all surfaces the differences between a�h and a�v are less than 1 dB at C- and L-band.

For accumulation areas a�v shows a significant decrease for incidence angles up to 45° . vVhen the incidence angle exceeds 45° the curve becomes less steep as an effect of the increasing volume scattering contribution. a�v ranges between -4 and -16 dB, is lower than for glacier ice areas and reveals stronger angular dependence. a�� is about 2 dB higher than a�� because the volume scattering term increases with the penetration depth. When DT 46 was acquired the accumulation areas included snow with a wet surface layer as well as, at high elevations, snow with refrozen crusts.

For the glacier ice high er co- and cross-polarized backscattering coefficients and weaker angular dependence are observed because the ice surface is rougher than the snow surfaces, and multiple scattering terms become more important.

Unglaciated surfaces show a�� between -2.0 and -12 .0 dB for incidence angles between 20° and 70° . a�� is up to 2 dB higher than a�t and a�;; . The crosspolarizecl backscattering coefficients vary between - 13.3 and -21 dB, and are similar for vegetated and unvegetated surfaces. The angular patterns for these two surface types are almost identical.

The magnitude of the HHVV correlation coefficient. In figure 7.6 the average values of I Phhvv l at different incidence angles are shown.

Unlike in April, for all surfaces the correlation coefficient at C-band is higher than at L-band . The frequency dependent penetration depth is important for the strength of the correlation between the co-polarized scattering matrix elements.

Due to the high liquid water content of the glacier surfaces and the small volume


5

0

-5

CD :!:!. 0 - 10 b

-15

-20

-25

5

0

-5

CD :!:!. 0 -10 b

-15

-20

-25

ACCUMULATION AREA

SRL-2 DT 46

20

20

40

W X-band(+) W C-band(6) W L-band(e) HV C-band(<>) HV L-band (0)

60

ei [deg]

ROCKS

40 60 ei [deg]

5

0

-5

CD :!:!. 0 - 10 b

- 15

-20

-25 80

5

0

-5

CD :!:!. 0 - 10 b

- 15

-20

-25 80

20

20

GLACIER ICE

40 60

ei [deg]

VEGETATION

40 60

ei [deg]

80

80

Figure 7 .5 : Incidence angle dependence of co- and cross-polarized backscattering coefficients at X-, C- and L-band for different types of natural surfaces derived from SRL-2 DT 46.


ACCUMULATION AREA GLACIER ICE 1 .0 1 .0

SRL-2 DT 46

0.8 0.8

~ � 0.6 � 0.6

� ..c ..c ..c � .Q.._

0.4 0.4

0.2 C-band (.t..) 0.2 L-band ( e )

0.0 0.0 20 40 60 80 20 40 60

e i [deg] ei [deg]

ROCKS VEGETATION 1 .0 1 .0

0.8

~ 0.8

- 0.6 � 0.6 � ..c ..c

..c ..c

a.. 0.4 .Q.. 0.4

0.2 0.2

0.0 0.0 20 40 60 80 20 40 60

ei [deg] ei [deg]

89

80

80

Figure 7.6 : Incidence angle dependence of the magnitude of the HHVV complex correlation coefficients at C- and L-band for different types of natural surfaces derived from SRL-2 DT 46.

C'hapter 7. Microwave polarimetric signatures of alpine terrain !)( )

scattering contribution , large correlation coefficients are observed on the accumulal.ion and ice areas: about 0.8 at C-band and about 0.7 at L-band at ei = 20° .

For the unglaciated areas, the correlation coefficients are larger than over the g;laciers, ranging from 0.8 to 0.7 at C-band, and from 0.8 to 0 .6 at L-band.

7.4 Seasonal variations of the backscattered sig

nal

This section addresses variations of the backscattered signal between winter and late summer. The co- and cross-polarized backscattering coefficients and the magnitudes of the HHVV correlation coefficient in the two seasons are compared.

Seasonal variations of the backscattering coefficients. The angular dependence of a0 in April and October, as shown in figure 7.7, reveals temporal variations in hackscattering for all surface classes . a�; shows a very similar behavior with a�� in hoth seasons , with the exception of SRL-2 for unglaciated areas when it was similar to a�� ·

For the accumulation area the co- and cross- polarized backscattering coefficients in April are higher than in October. Significant changes are observed at X- and Cband, where the co-polarized backscattering coefficients are about 8 dB higher in April, whereas at L-band about a 5 dB difference is observed. Between SRL-1 and SRL-2 a�� increases by about 12 dB a�� by about 5 dB, respectively. For the accumulation area the largest seasonal changes are observed for a�� hence it may be a valuable parameter for wet snow mapping.

For glacier ice areas seasonal differences less than 2 dB are observed for all backscattering coefficients except a�� which is about 5 dB !arger in April than in October.

The unvegetated areas have very similar angular patterns in the two seasons. For the " rocks" class a0x has no seasonal variations O'oc a0L and a0L are 1 to 2 vv ' vv ' vv hv clB higher in October than in April while 0'�� shows a decrease of about 2 dB.

For snow covered versus snow free vegetation the following characteristics are observecl :

• a�� and a�� are up to 4 dB higher in October than in April

• a�;; and a�� have almost no seasonal changes

• a�� is about 2 to 3 dB higher in October.

Seasonal variations of the magnitude of the HHVV correlation coefficient. The magnitude of the correlation coefficient shows !arge seasonal variations for the accumulation and glacier ice areas (figure 7.8) .

In figures 7. 9 and 7. 10 color tables of the HHVV correlation coefficient at SRL-1 and SRL-2 over the glaciers are shown. I Phhvv l is more affected by the seasonal changes at C-band than at L-band.

For the unglaciated areas an increase of the C-band correlation coefficient and almost constant values at L-band are observed.


co :2. 0 b

co :2. 0 b

ACCUMULATION AREA 5

0

-5

- 1 0 [ - 1 5 -

-2o r -25

20 40 60 ei [deg] 80

ROCKS 5

0

-5 -

-1 0

-1 5

[ -20 r -25

20 40 60 ei [deg] 80

SRL-1 DT 46 W C-band(Ä) HV C-band(+) VV L-band(•l HV L-band(e)

co :2. 0 b

co :2. 0 b

GLACIER ICE 5

0

-5

-1 0

- 1 5

-20 -

-25 20 40 60 80

ei [deg] V EGETATION

5

0

-5

- 1 0

- 1 5

-20

-25 20 40 60 80

ei [deg]

SRL-2 DT 46 W C-band(L'.) HV C-band(O) W L-band(O) HV L-band (0)

91

Figure 7 .7 : Seasonal changes of the backscattering coefficients at C- and L-band derived from DT 46. a2; is not shown because it was similar to a2� in both seasons, with the cxception of SRL-2 for unglaciated areas when it was similar to a2� .


ACCUMULATION AREA 1 .0 ,-------------,

SRL-1 DT 46 SRL-2 DT 46

0.8

� 0.6

.c

� 0.4

0.2 .

20

C-band�) C-band (6) L-band (e) L band (0 )

40 60

ei [deg]

ROCKS

80

1 .0 ,--------------,

0.8 -

� 0.6 -

.c .c

a.. 0.4 -

0.2

20 40 60 ei [deg]

80

GLACIER ICE 1 .0 ,-

-------------,

0.8

� 0.6

.c

� 0.4

0.2

20 40 60

ei [deg]

VEGETATION

80

1 .0 ,--------------,

0.8

� 0.6

.c .c

..Q... 0.4

0.2

20 40 60

ei [deg] 80

92

Figure 7.8: Seasonal changes of the magnitude of the HHVV complex correlation coefficient at C- and L-band calculated from DT 46 .

Clwpter 7. Aiicrowave polnrinwtric siguat r m�s of alpine terraiu

I Phhvvl 0.0 0.2 0.3 0.4 0 .5 0 .6 0 .7 0 .8 0 .9 1 .0

93

Fignrc 7. 9 : Corrclat ion cocffkient irnagcs of glaciCrs at C-bancl clerived from DT 46 in (a) April and (b) Octobcr 1994.

Clwp tor 7. 1\Jicrownvc polarinwtric si�nat rm�s of alpine terrain

I Phhvvl 0.0 0 .2 0.3 0 .4 0 .5 0 .6 0.7 0 .8 0.9 1 .0

94

Fignrc 7 . 10: Corrclation codficicnt irnagcs of glacicrs at L-band clcrivcd frorn DT 46 in (a) April and (b) October 1994.


Explanation for the seasonal differences. The accumulation areas on the glaciers show pronounced seasonal variations. In winter high a�v and a�v values on the accumulation areas are due to scattering in the frozen firn below the fine-grained dry snowpack Iee lenses and ice pipes in the frozen firn are effective volume scatterers. In October, when the snow was wet and relatively smooth, low a�v and a�v are observed. The volume scattering contribution is considerably reduced and the scattering at the air/snow interface becomes important at C- and X-band. At L-bancl the scattering at the snow/firn interface is important if the snow layer is not too deep (e.g. , 0.5 m snow depth was measured during the SRL-2 campaign) .

On the glacier ice a0 is determined by its roughness and by the dielectric properties of the glacier ice. In winter the backscattering at the smooth snow /ice interface and in the ice volume are dominant. In October the ice surfaces are rougher than in April and wet. Thus very weak seasonal differences are observed for co-polarized a0 . The strong decrease of a�� from SRL-1 to SRL-2 is an indicator of the reduced volume scattering contribution of the glacier ice in October at this frequency. a�� backscattering coeffi.cients are less sensitive to seasonal changes than a�� .

For the ice-free surfaces, assuming an approximately constant roughness, the seasonal differences are due to variations in the permittivity of bare soil, c�oil and E�oil · In winter the upper part of the snow covered soil is frozen and E�ail = 3 .9 , E�ail = 0.5 at 4.6 GHz and volumetric maisture mv = 0.05 [Wegmüller, 1990] . When the soil is wet , e .g. for mv = 0.38, E�ail increases to 21 , E�ail to 7.4. High values of E�ail lead to high co-polarized backscattering coeffi.cients in the SRL-2 relative to SRL-1 data. The seasonal change is more pronounced at lower frequencies [Wegmüller, 1990] . The cross-polarized backscattering coeffi.cients are also positively correlated with the wetness for frequencies between 1 and 12 GHz [Wang et al. , 1997] .

The magnitude of the HHVV correlation coefficient is lower in April for all surface types, because the dry, transparent snow which covered these surfaces increases the clecorrelation between HH and VV backscattering. In October when all the surfaces were wet, the penetration depth is significantly reduced ancl the correlation coeffi.cients are higher than in April.

Figure 7. 1 1 shows a false color representation of DT 46 com posed of co-polarized backscattering coeffi.cients in three frequencies. The firn areas appear in blue because of high backscattering at C-band in April while the red to purple color in the ablation areas is an effect of large a�;; due to roughness in October. The sub-Alpine forests appear in cyan ( e.g. near lake Vernagt) as an effect of comparatively high copolarized backscattering coeffi.cient at L-band in October. a�X instead a�t was chosen to emphasize the forcsts because of the visible Brewster angle effect.

7.5 Short term variations of the backscattered sig

nal

The aim of this section is to cliscuss the changes of a0 ancl I Phhvv l within several hours or days. The short term variation in the backscattered signal were analyzed in two different ways:

Cilap tor 7. 1Uicrowave pol�:trinwtric signat 1 1res of alpine torrain

forest

snow ice moraine

96

Figurc 7. 1 1 : Tlu·cc-frequcncy Sin-C/X-SATI multitemporal imagc of DT 46. Thc false color composite is bascd on amplitudes of co-polari�cd backscattcring cocfficicnts: (red) , CJ�k (grccn) acquirecl in Octobcr'94, and CJ��7 (blue) acquircd in April'94. polarimctric signaturcs of thc sclccted sitcs arc discussed in scctions 7.6 and 7.7.

ox (J'V'V Thc

( :llapter 70 Microwave polarimetric signatures of alpine terrain 97

1 0 the angular patterns of the elements of the covariance matrix are discussed a:-; in the previous sections for the available data takeso

20 to visualize the short term changes, ratios of backscattering coeflicient:-; a08RL-2 / a08RL-l belanging to repeat pass data takes are calculatecl, tlm:-; re

clucing the local inciclence angle clependence of 0"0 0

7.5 . 1 Changes in the backscattering coeffi.cients during SRL-1

During SRL-1 the snow properties at elevations above 2500 m were stable with t.he cxccption of some dry snow fall ( chapter 5) 0 Therefore no difference among the signatures corresponding to the same surface class at different data takes is expectedo [n figure 70 12 the incidence angle dependence of a�; derived from X-SAR GTC data during SRL-1 for all data takes for each type of surface is showno

Over all surface classes the plots show comparatively small differences for DT 18, 34, 46 and 78, only for DT 14 a0 is higher. A source of errors in the calculations can be the inaccuracies in the DEM and orbit parameters o DT 14 is in particular a.ffectecl by these errors because it has the steepest look angle (36°) 0 lf the DEM cloes not perfectly match the topography of the imaged seeneo inaccuracics result for geococling and other image coordinate transformationso At transitions from foreslope to backslope and vice-versa ( eogo at ridges and valleys) , the errors of the backscattering coefficients due to the wrong calculation of the surface resolution element can reach ±705 dB if the uncertainty in calculation of the local incidence angle is 90° 0 In the case of DT 14 a much smaller amount of pixels is imaged at high incidence angles than the other clata takes, which results in large variability of the data pointso Therefore, for DT 14 the signatures were calculated only up to (}i = 45° 0

For glacier ice the difference among the three ascending data takes is about 2 dB at incidence angles less than 50° , at large incidence angles it becomes larger ( 4 dB or more) o For the clescending data takes the O"�; coefficients are up to 2 dB lower than for DT 46 and 78 for the accumulation area, but all are in good agreement over glacier ice areao

For unglaciated areas comparatively good agreement between the data takes is observed; the backscattering coefficients corresponding to DT 14 are about 2 dB larger at small inciclence angles o

There is a general trend that a0 of the descending data takes is smaller than of the ascencling data takeso This may be due to calculation errors and to effects of variations of target properties with the azimutho The orientation of the slope very likely plays also a role, in particular in the accumulation areaso

Reasonable agreement between the data takes were observed also for C- and L-bancl signatures derived from SIR-C data shown in appendix Ao

7.5 .2 Changes in the correlation coeffi.cients during SRL-1

As discussed in the previous section the magnitude of the correlation coefficient in winter is related to the volume scattering contributions of the snow pack and to

( :hapter 7. Microwave polarimetric signatures of alpine terrain

5

0

-5 iii' :!:!.

0 5: -10 ö

-1 5

-20

-25

5

0

-5 iii' :!:!.

o$: -10 b

-1 5

-20

-25

ACCUMULATION AREA

X-band SRL-1

DT 14 (0) DT 18 (+) DT 34 (0 ) DT 46 (..&.) DT 78 ( e )

20 40 60 ei [deg]


20 40 60 ei [deg)

80

80

5

0

-5 iii' :!:!.

0 $: -10 b

- 1 5

-20 -

-25

5

0

-5 iii' :!:!.

o S:: - 10 b

-1 5

-20

-25


20 40 60 ei [deg]

80


20 40 60 ei [deg)

80

Figure 7 . 12 : Incidence angle dependence of a�v backscattering coefficient for natural targets during SRL-1 derived from X-SAR GTC data. The dass " rocks" includes all unvegetatecl ice-free surfaces.

Chapter 7.

1 .0

0.8

� 0.6

.J:: .J:: .Q...

0.4

0.2

0.0

1 .0

0.8

� 0.6

.J:: cf

0.4

0.2

0.0

!vficrowave polarimetric signatures of alpine terrain

ACCUMULATION AREA

SRL-1 DT 1 4 DT 46

C-band ( 6. ) C-band (.&) L-band ( 0 ) L-band (e )

20

20

40 60

ei [deg]


40 60

ei [deg)

� .J::

�

80

� .J:: .J:: .Q...

80

1 .0

0.8

0.6

0.4

0.2

0.0

1 .0

0.8

0.6

0.4

0.2

0.0


20 40 60

ei [deg]

VEG ETATION (snow covered)

80

20 40 60 80

ei [deg)

99

Figure 7 . 13 : The magnitude of the HHVV complex correlation coeffi.cient as function of incidence angle calculated from DT 14 ancl 46 SRL-1 .

t.he roughness of the surface below it. For SRL-1 only two fully polarimetric data t.akes are available, DT 14 and 46, with 36° and 50° incidence angle at image center respectively. Thereforc identical points of the same target dass are observed at different incidence angles. This may explain, at least partly, the differences in the correlation coefficients (figure 7. 13) and in backscattering coefficients (figure 7. 12) because the structure of the snow pack as well as of ice-free surfaces has significant spatial variations depending on the orientation of the slope, elevation, and solar illumination.

Errors in the slope correction, primarily due to small-scale errors in the DEM, and calibration errors are probably also partly responsible for the temporal variability of the signatures within SRL-1 . In addition, comparatively small changes of snowpack conditions during SRL-1 cannot be excluded as one of the reasons for the signature variations.

Chapter 7.

5

0 -

-5

ii5' � 0 5: -1 0 -ö

-1 5

-20

-25

5

0

-5

ii5' � 0 � - 1 0 b

-1 5

-20 -

-25

lvlicrowave polarimetric signatures of alpine terrain

ACCUMULATION AREA

SRL-2 X-band DT 1 4 (D)

20

20

40

DT 1 8 (+ ) DT 34 (0) DT 46 (.&) DT 78 ( e )

60 80

ei (deg]

ROCKS

40 60

ei (deg] 80

5

0

-5 -

ii5' � 0 � - 1 0 b

- 1 5

-20

-25

5

0

-5

ii5' � 0 � -10 b

-1 5

-20

-25

20

20

GLACIER ICE

40 60

ei [deg]

VEGETATION

40 60

ei (deg]

100

80

80

Figure 7 . 14 : Incidence angle dependence of O"�v for natural targets during SRL-2 derived from X-SAR GTC data.

7.5.3 Changes in the backscattering coefficients during SRL-2

Before and during the SRL-2 mission, in the first days of October '94, the air temperature dropped significantly and changes in the properties of snow and ice were o bserved ( chapter 5).

At X- and C-band the data are sensitive to changes of snow wetness in the upper part of the cover. In figure 7.14 the incidence angle dependence of O"�;; is shown over the accumulation, glacier ice, rocks and vegetation areas for geocoded X-SAR data of SRL-2.

On the accumulation area O"�;; backscattering coefficients of ascending passes differ only at incidence angles above 25°. The data acquired during descending passes (DT 18 and 34) , at noon ( chapter 2) , reveal lower backscattering coeffi.cients than the data acquired during ascending swaths at all incidence angles. For example, at 40° inciclence angle O"�;; is -12.9 dB, -16.7 dB, -14.5 dB, -11.1 dB and -7.5 dB for


DT 14, 18, 34, 46 and 78, respectively. During the acquisition of the first 3 data takes air temperatures above ooc were measured on the glaciers even in the morning. On the KWF IGEP glacier plateau 4%vol liquid water content was measured at 5 cm clepth in the snow area on 1 October at noon. Towards the end of the experiment the air temperature decreases below Ü°C. On the accumulation area the upper part of the snow pack started to freeze and a�; increases. On 3 October (DT 46) the snow liquid water content measured in the same area and same layer decreased to 1 . 5%vol . Looking at the seasonal changes (figures 7 .12 and 7. 14) , on the accumulation area at ()i = 35° during DT 14 a�; is about 8 dB higher in winter than in summer while for DT 78 only about 3 dB.

For glacier ice small changes are observed from DT 14 to DT 78 at X-band. During DT 14 a�; for glacier icc is higher than for wet snow areas, for DT 46 the clifference between the backscattering coefficients of the two classes becomes smaller , and for DT 78 up to ()i = 45° a�; is higher for the accumulation area.

Bare soil and vegetation reveal similar angular dependences. For DT 14 vegetation 1:>hows lower a�; than bare soil areas. The weak decrease in the average backscattering coefficients from DT 14 to DT 78 is of the same order as that observed in the SRL-1 data. Therefore one reason for the decrease may be errors in terrairr correction.

At C- and L-band the angular dependences of the a�f , a�?t , and a�� backscattering coefficients were derived from multilooked SSC data and are presented in appendix A (figures A.6, A.7 and A.8) .

The variation of the C-band cross-polarized backscattering coefficient , a?t� , during SRL-2 is shown in figure 7. 15) .

At angles below 45° the a�� backscattering coefficients increase from DT 18 to DT 78 due to the refreezing of the snow pack. Similar but less pronounced changes are observecl for the glacier ice areas. For the bare soiljrocks areas a clear difference is observecl between the data takes of 1 October ancl the data takes acquired during the cold period at the end of SRL-2. For the vegetation areas and bare soil surfaces a decrease of the a?t� coefficients is observed from DT 14 to DT 78 which may be explained by the freezing of the soil at the end of the experiment.

The average signatures of the glacier surface classes show the capability to detect short term changes in the properties of the wet snow in the accumulation area using X- and C-band backscattering coefficients. But they are less sensitive to freezing of glacier ice.

The short term evolution of the scattering properties of the targets can be visualized through ratios between backscattering coefficients at the same frequency ancl polarization, corresponding to repeat pass data takes. Compared to a0, the a08RL-2 1 a08RL-1 ratios have a reduced local incidence angle dependence. In agreement with the field observations, it is assumed that the target properties were approximately constant during SRL-1 . Thus, if the ratios differ from DT 14 to DT 78 this is clue to backscattering changes during SRL-2. The ratio images show the spatial variability of the short term changes.

The backscattering coefficient images were low pass filtered, then a�; SRL-2 I aoXSRL- 1 and aocsRL-21a008RL-1 ratio images were calculated for DT 14 46 and vv hv hv ' 78 in slant range geometry. After geocoding, thresholds were applied to the ratio images: -7 dB for X-band and -10 dB for C-band data. The retreat of the wet


ACCUMUL..ATION AREA ·5,_----------------------,

· 10

co �

0 � ·1 5 b

·20

SRL·2 C·band

20 40 60 ei [deg]

ROCKS

DT 1 4 (0) DT 1 8 (+) DT 46 (A) DT 78 (e)

80

·5 ,-----------------------�

· 10

co :!:!.

0 � ·15 b

·20

20 40 60 ei [deg]

80

GLACIER ICE .s ,-----------------------,

- 10 -

co :!:!.

0 �·15 b

·20 -

20 40 60 ei [deg]

VEGETATION

80

·5,-----------------------�

·10

co :!:!. Y- ·15

·20

20 40 60 ei [deg]

80

102

Figure 7. 1G : Incidence dependence of the cross-polarized backscattering coefficient at Cbancl clerivecl from DT 14, 18, 46 a.nd 78 SRL-2.

Clwptor 7. 1\Jicrowave pola.rinwtric sip,11at ures of alpine torrain 103

Figure 7. 16: Monitaring the extent of the wet snow eover in Oetober 1994 with X-SAR a��RL-2/a�;;rn-1 ratio imagcs thresholded to -7 dß. In red appear the areal changes of wet snow from DT 14 (1 Oet . 6:41 UTC) to DT 46 (3 Oet. , 6:02 UTC) . In grecn the change from DT 46 to DT 78, in bluc the areas with rat ios up to -7 dß for DT 78 ( 5 Oct . . 5 :23 UTC) . Background: C-band HH DT 78 SRL-2 gcocodcd imagc.

Figure 7. 17: ?vlonitoring thc extent of the wct snow covcr in Oetober 1994 with C-band SID c OS'JU,-2/ OS'/U,-1 t "

. th h 1 1 l t 1() lß ß k I . I . .n- a hv a hu ra IO Images rcs o c cc o - c . ac: g:rounc Image anc sig-

nificancc of thc colors arc as in figurc 7. 16.


snow areas from 1 to 5 October can be observed from thresholded X-band ratio images (figure 7. 16) . The areas corresponding to ratio values up to -7 dB differ from DT 14 to DT 46 (red) and from DT 46 to DT 78 (green) . For DT 78 only a limited number of pixels (blue) is a.c;;signed to wet snow. At C-band the threshold applied to the ratio images was -10 dB and the resulting bitmaps are shown in figure 7. 17. Layover and shadow zones corresponding to each data take were masked out. In both figures seasonal variations of this order (-7 respectively -10 dB) are observed almost solely on the accumulation areas of the glaciers . At X-band a significant decrease of the number of pixels with backscattering ratio below the corresponding threshold is observed from DT 14 to DT 78. The C-band crosspolarized backscattering coefficient is more stable to short term variations in the snow conditions. The decrease of thc seasonal differences from DT 14 to DT 78 is a consequence of the increasing backscattering coefficients during SRL-2. The differences between X- and L-band are due to different sensitivity to the frozen crust which developed during SRL-2.

The changes of the backscattering properties of the glaciers during SRL-2 are outlined with color coded SRL-2/SRL-1 ratio images scaled in dB corresponding to co- and cross-polarized o-0 at X-, C- and L-band. The interpretation of these images is based on the following ideas:

• for a specified frequency and polarization, increasing ratios (from -15 dB to + 10 dB) are related to increasing backscattering coefficients during SRL-2, if rr0 during SRL-1 is assumed to be approximately constant.

• ratios close to 0 dB should not be interpreted as significant because of the calibration uncertainty.

At X-band (figure 7. 18) ratio values between -15 and -7.5 dB dominate on the accumulation areas in DT 14. A clear increase of the ratio is observed towards the end of the period; for DT 78 ratios between -10 and -5 dB are observed for the same surfaces . On the ablation areas the X-band ratios do not show a significant change during SRL-2.

The C-band ratio images o-��SRL-2 jo-��SRL-1 (figure 7. 19) are very similar for DT 14 and DT 46 but larger ratios are observed for DT 78 on the accumulation area. The glacier termini show relatively high ratios up to + 7.5 dB at DT 14 which decrease cluring SRL-2. Similarities between C- and X-band ratio images corresponding to DT 14 (figures 7. 18 (a) and 7. 19 (a) ) are observed for the accumulation areas.

The C-band ratio images of cross-polarized backscattering coefficients, rr��SRL-2 / rr��SRL- I , are shown in figure 7.20. Ratios lower than -10 dB are dominant on the accumulation areas while ratios close to 0 dB are observed on the glacier ice areas. Towards the end of the experiment the ratios on the accumulation areas change more at X-band than at C-band.

At L-band values of rr�tSRL-2 / rr�tSRL-1 up to + 10 dB are observed on the glacier ice areas for DT 14 (figure 7.21 ) , decreasing in time during SRL-2. On the accumulation areas ratios between -7.5 dB and -2.5 dB dominate, being significantly closer to 0 dB than at X- and C-band. The values of the backscattering coefficients at L-band on the accumulation areas in October are mainly determined by the scattcring at internal boundaries and at the snow/firn interface. At C- and X-band the

Clwpter 7. J\Jicrow<:J.VC polnrimetric signat l !res of alpine tcrrain

- 1 5 - 1 0 -7 .5 -5 -2.5 0 2.5 5 7.5 1 0 [dB]

105

Figurc 7. 1tl: natio imag;cs bctw<�cn a?,J in Octobcr and April 1994 on thc glacicrs corrcsponding to: (a) DT 14. (h) DT 4G aud (c) DT 7tl.

Clwptcr 7. 1\Jicrowave polarinwtric sigmtt ures of alpine tcrrain

- 1 5 - 1 0 -7 .5 -5 -2 .5 0 2.5 5 7 .5 1 0 [dB]

I ( )( )

Figmc 7. 19 : Rat io imagcs bctwcen a?,};' in Octobcr and Apri l 1994 c:orrcsponding to: (a) DT 14 , (b) DT 46 and (c : ) DT 7� .

Clwpter 7. Aiicrowave polminwtric signat l !res of alpine terrain

- 1 5 - 1 0 -7.5 -5 -2 .5 0 2.5 5 7 .5 1 0 [dB]

107

Figme 7. 20: Rat io irnages betwcen er��;' in October and April 1994 corrcsponding to: (a) DT 14 , (b) DT 46 and (c) DT 78.

Chapter 7. Microwave polarimetric signatures of alpine terrain lOH

signal is absorbed in the wet snow layer and scattering contributions come from the air/snow interface and from the top layer of the snow volume. This is consistent with theoretical models (section 6 .2 .5) which predict larger a0 at L-band than at Cand X-band when the snow layer is slightly wet, smooth, and its thickness does not exceed 1 . 5 m.

Tl OLSRL-2/ OLSRL - 1 t ' · h · fi 7 22 'J'} . . 1e ahv O'hv ra 10 Images are s own m gurc . . . w vanai. Ion from DT 14 to DT 78 is similar to the co-polarized ratio images but h�ss ol >vious.

7.5.4 Changes in the correlation coeffi.cients during SRL-2

The variation of the HHVV correlation coefficient over the glaciers from DT 14 to DT 46 is shown in figure 7.23 and for the unglaciated areas in figure 7.24. Within only several hours, from 06:41 UTC (DT 14) to 12 :50 UTC (DT 18) , IPhhvv l at Cband decreases on the accumulation and glacier ice areas, then increases at DT 46. On the unglaciated areas the correlation coefficients are similar for DT 14 and DT 18, for DT 46 they are higher.

In chapter 6 first order surface and volume scattering models for the correlation coeffi.cient are discussed. The surface contribution of IPhhvv l derived with the IEM increases with wetness, whereas the volume contribution of IPhhvv l decreases with increasing wetness. Although not modelled, multiple order scattering may infiuence the correlation coefficient in slightly wet snow conditions. The variation of I Phhvv I on the glaciers is determined by several factors and therefore no Straightforward explanation is possible. In addition the effects of imaging geometry and calibration errors may affect the magnitude of the correlation coefficient . Low correlation coefficients are observed for DT 18 also on the ice-free areas, therefore the possibility of an artefad of calibration should not be excluded.

The short term changes of the C-band correlation coefficient confirm the Observation in section 7.4 regarding the sensitivity of this polarimetric parameter to variations in the properties of the snow pack.

7.6 Polarimetrie signatures derived for selected

snow and ice sites

The short term and seasonal variation of the elements of the covariance matrix cluring the SRL-2 experiment was also analyzed through the signatures collected over data windows which have approximately the same location in all scenes. At Xband the backscattering coefficients are derived from X-SAR GTC or SSC products, at C- and L-band the polarimetric measures are derived directly from the averaged Stokes matrix over the data window.

On the glaciers two data windows were chosen, with positions illustrated in figure 7. 1 1 :

• the window ,, glacier ice" contains 750 pixels in X-SAR geocoded images and about 100 pixels in the SIR-C images in slant range projection, and is situated on the ablation area of Hintereisferner, north of the corner refiector H2.

Clwpter 7. 1\Jicrowave polariwctric �ignat l ll'C� of alpine terra.in

- 1 5 -1 0 -7.5 -5 -2.5 0 2.5 5 7 .5 1 0 [dB]

109

Figure 7. 2 1 : Tiat io Images between a?,�; in October and Apri l 1994 corresponding to: (a) DT 14 , (b) DT 46 and (c) DT 78.

Clwptcr 7. 1\Jicrowavc polarinwtric si�mlt r rrcs of alpine tcrmin

- 1 5 - 1 0 -7.5 -5 -2.5 0 2.5 5 7.5 1 0 [dB]

I I 0

Fi�nre 7.22 : nat io irna�cs bctween a?,�: in Oetober and April 1994 correspondin� to: (a) DT 14 . (b) DT 46 ancl (c) DT 7H.

Chapter 7. Microwave polarimetric signatures of alpine termin

1.0

0.8

� 0.6

..c ..c .Q..

0.4

0.2

0.0

1 .0

0.8

� 0.6

..c ..c a..

0.4

0.2

0.0

ACCUMULATION AREA

C-band

SRL-2 DT 1 4 (D) DT 1 8 (+)

DT 46 (.&.)

20 40 60

ei [deg) ACCUMULATION AREA

20 40

L-band

ei [deg) 60

1 .0

0.8

� 0.6

..c

!i: 0.4

0.2

0.0 80

1 .0

0.8

� 0.6

..c ..c .Q.. 0.4

0.2

0.0 80

20

20

GLACIER ICE

40

C-band

ei [deg) 60

GLACIER ICE

40

L-band

ei [deg) 60

I I I

80

80

Figure 7.23: Variation of the magnitude of the HHVV complex correlation coefficient at C- and L- band in the period 1 to 3 October 1994 over glacier areas.



C-band C-band

0.8 0.8 � � 0.6 � 0.6

..c: ..c:

� 0.4

� 0.4

SRL-2

0.2 DT 1 4 (D) 0.2 DT 1 8 (+) DT 46 (.&)

0.0 0.0 20 40 60 80 20 40 60

ei [deg] ei [deg]


l-band l-band

0.8 - 0.8

� 0.6 � 0.6

..c: ..c: ..c: ..c:

0... 0.4

.Q... 0.4

0.2 0.2

0.0 0.0 20 40 60 80 20 40 60

ei [deg) ei [deg]

1 1 2

80

80

Figure 7 .24: Variation of the magnitude of the HHVV complex correlation coefficient at C- and 1- band in the period 1 to 3 October 1994 over unglaciated areas.

Chapter 7. Microwave polarimetric signatures of alpine tcrrain

(Bi) [0] DT 14 18 34 46 78

snow 42.6 45 .4 52.4 56.4 64.2 glacier ice 36.6 50.5 57.6 50.0 58.0

I 1 : 1

Table 7. 1 : The average local incidence angle (Bi) in degrees, corresponding to the selected snow and ice sites in the imaging geometry of each data take (DT) .

• the window "snow" belongs to the accumulation area of Kesselwanclferner, west of the corner refl.ector K1 , and contains 990 pixels in X-SAR GTC and about 100 pixels in SIR-C SSC data.

The areas are approximately horizontal and homogeneous; the average incidence angle at each data take is given in table 7. 1 .

I n figure 7 .25 the co-polarized backscattering coefficients derived from the data windows at different data takes are shown. The length of the vertical bars is proportional to the seasonal change in backscattering, which is calculated as CJOSRL-2 / a08RL-l ratio. Assuming that snow conditions did not change during SRL-1 the variations in seasonal differences illustrate short term changes of the backscattering coefficients during SRL-2.

At X-band the seasonal difference for the snow area is about - 14 dB for the first 3 data takes. At the end of SRL-2 a�; increases and the SRL-2/SRL-1 ratio is reduced to about -7.5 dB. This confirms the observations presented in section 7.5 about the sensitivity of X-band radiation to short term changes of the snow surface properties. For glacier ice the variations of a�; are low, ±1 dB; this variability is within the calibration precision. Although volume scattering in the snow layer above the ice provides an important contribution to backscattering during SRL- 1 , seasonal differences for this surface class cannot be detected at X-band. The overall seasonal changes on the glaciers at X-band for DT 14, 46 and 78 can be observed in figure 7 . 18 . In October the backscattering coefficients on the snow site are significantly lower than on the ice site. The volume scattering in the slightly wet snow cover and the scattering at the smooth air/snow interface are weaker than the scattering at the rough air /ice interface and in the ice volume.

At C-band a decrease of about 10 dB of a?tX' from April to October is observed for the snow area. The difference is approximately constant for all data takes. During DT 14 and 46 aRX' of the ice site is about 3 dB higher in October than in April . The snow covered ice surface is smoother than the bare ice and the dielectric contrasts at the snow/frozen ice and air/melting ice interfaces differ in the two seasons. The increase of a?tX' over ice areas was not as clearly observed in the seasonal change of the averaged signatures described in section 7.4. For DT 78 the difference is only 0 .8 clB. The behavior of the C-band seasonal ratios on the glaciers is shown in figure 7 . 19 . Similarly to X-band, the scattering mechanisms at C-bancl (section 6 .2 .5) cause low backscattering coefficients for slightly wet snow relative to glacier ice.

On the snow area the seasonal changes at L-band are less pronounced than at C-band; ratios of -4 dB to -6 dB are observed for the three data takes. For the glacier ice site the seasonal difference is 6 .5 , 3 .2 and 2 .5 dB in DT 14, 46 and 78,


SNOW ON THE ACCUMULATION AREA 0

VV X-band SRL-1 (O)

·5 Q SRL-2 (e)

Q i:D -10 � Q

I I 0 b

-15

0

-20

4 -25 '-------'------'---'--------'-----'------'

1 4 18 34 46 78 Dala Iake

o ,-------------.

HH C-band

-5

i:D -10 � 0 b

-1 5

-20

• -25 '------'-----'----------'----'----'

1 4 1 8 34 46 78 Dala Iake

o ,-------------.

HH L-band

-5

i:D -10 � I I I

0 b

-15

• -20 •

-25 '------'-----'-----'--------'-----'------' 1 4 1 8 34 46

Dala take 78

GLACIER ICE o ,-------------.

-5

i:D - 1 0 � 0 b

- 1 5

-20

VV X-band

•

-25 '------'-----'------'-------'-----'------' 1 4 1 8 34 46

Dala Iake 78

o ,-------------.

-5

i:D - 1 0 � 0 b

- 1 5

-20

HH C-band

I •

•

-25 '-------'-----'----'-------'-----'------' 14 1 8 34 46 78

Dala taka

0 ,-------------.

-5

i:D - 1 0 � 0 b

- 1 5

-20

HH L-band

I I I • •

-25 '-------'-----'----'-------'-----'--___J 1 4 1 8 34 46

Dala Iake 78

I I II

Figure 7.25: The co-polarized backscattering coefficients at different data takes derived from SRL-1 and SRL-2 data, and their ratios (the vertical bars) for data windows belanging to the accumulation and glacier ice areas.

Cllapter 7. Microwave polarimetric signatures of alpine terrain 1 15

SNOW ON THE ACCUMULATION AREA GLACIER ICE -10 Q ·10

HV C-band SRL-1 (0) HV C-band

-15 SRL-2 (e) -15 Q

10 -20 - 10 -20 � :!:!. :!:!.

� 0 0 • � b b -25 -� -25 • • 4t

-30 •• -30

• -35 -35

1 4 18 34 46 78 14 1 8 34 46 78 Dala Iake Dala Iake

-1 0 ·10

HV L-band HV L·band

-15 -1 5

� -20 I I

CD' -20 :!:!.

6 0

I 0

b b • -25 -25 � • • �

-30 • -30

-35 -35 14 18 34 46 78 14 18 34 46 78 Dala Iake Dala Iake

Figure 7.26: The cross-polarized backscattering coefficients at different data takes derived from SRL-1 and SRL-2 data and their ratios (the vertical bars) for data windows belanging to the accumulation and glacier ice areas.

respectively. This decrease of the ratio at L-band is observed in figure 7 .21 also on other glaciers and indicates that changes occurred in the dielectric properties of the glacier ice during SRL-2. On the snow site the co-polarized backscattering coefficients in SRL-2 are higher than at C-band and, for DT 14, also higher than at X-band. This situation was anticipated by the one layer model in section 6 .2 .5 . The slightly wet snow on the accumulation areas in October is partly penetrated by the L-band radiation. At C- and X-band the signal is absorbed in the wet snow layer and scattering contributions come from the air/snow interface and from the snow volume. According to the model calculations (f:igure 6 . 19) 0'0 at L-band is larger than at C- and X-band when the snow layer is slightly wet, smooth, and its thickness does not exceed 1 .5 m.

The cross-polarized backscattering coefficients of the two areas are shown in figure 7 .26.

Large seasonal variation is observed for 0'�� over the snow data window. For DT


snow SRL-1 SRL-2 SRL-1 SRL-2

DT ( ()i ) I Phhvv l IPhhvv l IPhhvv l IPhhvv l [0] C-band 1-band

14 42.6 0.318 0.488 0.638 0.340 18 45 .5 - 0.539 - 0.548 46 56.4 0.072 0.566 0.446 0.310

1 16

Table 7 .2 : The magnitude of the HHVV complex correlation coefficient for 11 snow11 data window at C and L-band derived from quad-pol SIR-C data.

ice SRL-1 SRL-2 SR1- 1 SRL-2

DT ( ()i ) I Phhvv l IPhhvv l IPhhvv l IPhhvv l [0] C-band 1-band

14 36.6 0.635 0.867 0.600 0.823 18 50.8 - 0.670 - 0.453 46 50 .0 0.444 0.817 0.560 0.715

Table 7.3 : The magnitude of the HHVV complex correlation coefficient for 1 1 ice 11 data window at C and L-band derived from quad-pol SIR-C data.

14 and 46 the ratio is about -15 dB, and for DT 78 - 1 1 .5 dB. For ice the ratio is less than -2 dB and approximately constant for the 3 data takes. The higher 0'�� coefficients in winter compared to summer on both sites are due to volume scattering effects in the dry snow cover and possible also in the ice. For the snow area 0'�� clecreases by about 5 dB from April to October'94, and increases by about 2 dB over the ice surface. These observations agree well with the results obtained over the entire accumulation and glacier ice areas of the Ötztal scene shown in figures 7.20 and 7.22.

At all three frequencies the values of the backscattering coefficients corresponding to DT 18 and DT 34 are low relative to those corresponding to the ascending swaths. These difFerences may be due to azimuthal dependence of e1° but calibration errors probably also play a role.

Tables 7 .2 and 7.3 show the values of IPhhvv l over the two targets . In agreement with the average signatures presented in section 7.4, the correlation coefficients are lower in April than in October, except for the snow site at L-band. At C-band the seasonal clifference is more pronounced than at 1-band.

7. 7 Polarimetrie signatures derived for selected

moraine, meadow and forest sites

In figures 7.27, 7.28 ancl 7.29 the backscattering coefficients of data windows bclonging to the unglaciated areas are shown. The selected data windows are (see locations in figure 7. 1 1 ) :

• morairre at the terminus of the Hintereisferner at about 2380 m a.s.l . ,


o �--------------�

-5

äi' :!:!. - 1 0 0 b

- 1 5

-20

W X-band

• � � i

� SRL-1 (o ) SRL-2 (e )

-25 '---�---'---'----'---..J..._----' 1 4 1 8 34 46 78

Dala Iake

- 1 0

Mo raine 0 0

• HH C-band -5 -5

� • äi' äi' :!:!. -10 � :!:!. -1 0

0 I 0 b -15 b -15

-20 -20

-25 -25

1 4 1 8 34 46 78 Dala Iake

- 1 0 • HV C-band • HV L-band

-15 - I äi' :!:!. -20

0 b -25

-15 •

� I •

I äi' :!:!. -20 � 0 b -25

-30 -30

-35 -35

I 1 4

I

1 4 1 8 34 46 78 1 4 1 8 34 46 78 Dala Iake Dala Iake

1 17

HH L-band

• •

� I

1 8 34 46 78 Dala Iake

Figure 7. 27: The co- and cross-polarized backscattering coefficients at different data takes clerived from SRL-1 and SRL-2 data and their ratios for glacier morairre at the terminus of Hintereisferner data window.

• cultivated meadow at Kurzras in the SE part of the test site at 2150 m,

• coniferous forest at 1900 m near the village Vent .

The meadow site is not included in the high resolution DEM therefore no topographic corrections of the backscattering coefficients can be performed. The backscattering coefficients presented below for meadow are calculated assuming a simple curved earth geometry, but because the area is approximately horizontal they should not differ very much from topographic corrected a0. The local incidence angles for moraine and forest sites and the incidence angle at far range for the meadow site are specified in table 7.4.

Generally, an increase up to 5 dB of the co- and cross-polarized backscattering coefficients from April to October is observed, but the seasonal changes are less significant than on the glaciers. The field measurements on snow covered ground in Kaunertal during SRL-1 revealed the presence of an old , slightly wet , about 60 cm deep snow cover below the dry fresh snow, at 2100 m altitude. At X- and C-band this may be the layer where the refiection occurs.

In October the backscattering coefficients are higher than in April because the ground was wet and c' higher than in winter when the ground was frozen. This is in good agreement with the backscattering coefficients of a cultivated meadow at 1200 m altitucle clerivecl from ERS-1 data, described in [Nagler, 1996] .

In October the forest has relative high a�� and a�k compared to the other targets. Theoretical studies based on ground truth measured in the Freiburg coniferous forest


Meadow

1 18

o .----------------. o .----------------. o .-----------------, W X-band HH C-band HH L-band

-5 ·5 ·5

iii' iii' iii' :!2. ·10 :!:!. -10 :!:!. -10 0 0 0 b ·15 b -15 b -15

-20 -2o SRL-1 (o) SRL-2 (e)

-25 '--------'-----'------'-----'---'--__j -25 '-------'-----'-----'----'-----'----'

14 1 8 34 46 78 Data take

14 1 8 34 46 78

Data Iake 1 4 1 8 34 46 78

Dala Iake

- 1 0 ,------------------,

HV C-band ·15

iii' :!2. ·20 0 b -25 I .

-30

-35 '------'----'-----'----'-------'-----' 1 4 18 34 46 78

Dala Iake

-10 ,------------------, HV L-band

-15 iii' :!2. -20

0 b -25

-30

-35 '------'----'----'----''-------'----' 14 18 34 46 78

Data Iake

Figure 7. 28: The co- and cross-polarized backscattering coefficients at different data takes derived from SRL-1 and SRL-2 data and their ratios for the meadow data window.

Forest

W X-band o r-----------------. 0 ,------------------,

HH C-band

e r----------------. HH L-band

-5 -5 -5

• iii' :!2. - 1 0 iii' :!:!. -1 0

� -10 I 0 0 0 b -15 b -15 b -15

-20 -20 -2o SRL-1 (0) _25

SRL-2 (e) ·25 '--------'-----'-------'----'------'----' -25 '------'----'------'-----'----'-----'

1 4 1 8 34 46 78

Dala Iake 1 4 1 8 34 46 78

Dala Iake

1 4 1 8 34 46 78

Data Iake

-5 .------------------,

-10

iii' • :!2. -15 C) 0 b -20

·25

HV C-band

•

-30 '--------'----'-------'----'------'-______J 1 4 1 8 34 46 78

Data take

-5 ,_-----------------,

-10

iii' :!2. -15 0 b -20

-25

HV L-band

I -30 '-------'-----'-----'----''-------'----'

1 4 1 8 34 46 78

Data Iake

Figure 7.29: The co- and cross-polarized backscattering coefficients at different data takes derived from SRL-1 and SRL-2 data and their ratios for the forest data window.

Chapter 7. !v!icrowave polarimetric signatmes of ;i/fJiiH ! /. t 'ITn i l l

(Bi) [0] DT 14 18 34 46 78

moraine 53.9 45.8 52.0 65 .2 74.4 meadow 39.0 - - 52.0 58.0

forest 57.4 - - 67.0 72.5

1 1 ! 1

Table 7.4: The average local incidence angle (ei) in degrees, corresponding to the selected moraine ancl forest sites in the imaging geometry of each data take (DT) . For the meadow site which is not included in the DEM the inciclence angle at far range is given.

moraine SRL-1 SRL-2 SRL-1 SRL-2

DT (Bi) I Phhvv l IPhhvv l IPhhvv l IPhhvv l [0] C-band L-band

14 53.9 0.459 0.528 0.598 0.612 18 45 .8 0 .676 0.764 46 65 .2 0.535 0 .617 0.646 0.559

Table 7 .5 : The magnitude of the HHVV complex correlation coefficient for the moraine clata winclow at C- and L-band derived from quad-pol SIR-C data.

[Fung, 1994] showed that at L-band two scattering terms are important: volume scattering, dominated by the large branches, and volume-ground interaction. At C-band contributions are coming from needles and branches of different sizes, at X-band mainly from the layer close to the top of the trees. The contribution of the ground alone is not considered in the model . The contribution from the trunkground interaction to the backscattering coefficients depends heavily on soil moisture [Karam et al . , 1993] . Thus, the seasonal variation of the L-band backscattering coefficients of forested areas is probably caused by changes of the properties of the underlying ground.

The magnitudes of the HHVV correlation coefficient for the three surface classes are shown in tables 7.5 and 7.6. The vegetation areas have lower correlation coefficients in October than in April.


meadow

SRL-1 SRL-2 SRL-1 SRL-2 DT e frg IPhhvv l iPhhvv l iPhhvv l IPhhvv l

[0] C-band L-band 14 39 .0 0 .645 0.627 0.657 0.478 46 52.0 0.608 0.534 0.523 0.395

forest SRL-1 SRL-2 SRL-1 SRL-2

DT (ei) iPhhvv l iPhhvv l iPhhvv l iPhhvv l [0] C-band L-band

14 57.4 0.571 0.416 0.484 0.231 46 67.0 0.499 0 .350 0.538 0.218

120

Table 7.6: The magnitude of the HHVV complex correlation coefficient for the meadow ancl forest clata windows at C- ancl L-band derived from quad-pol SIR-C data. () frg - the incidence angle at far range.

Chapter 8

Target classification in the high alpine test site

Target classification in alpine regions is important for hydrological and glaciological applications. Glaciers are of interest as sensitive indicators of climate change and as water resources. The annual mass balance, a key glacier parameter for climate research and hydrology, is closely related to the ratio of accumulation area to ablation area at the end of the mass balance year. This ratio can be estimated from spaceborne imagery. On the ice-free areas the discrimination of surfaces according to different hydrological characteristics is of interest .

In this chapter different classification procedures for various surface types in the test site Ötztal are presented. After the selection of the feature vector and definition of surface classes, target separability is investigated. The classifi.cation algorithms applied are based on statistics of the image pixels (Maximum Likelihood Classifier) , segmentation of multitemporal ratios, and hierarchical classifiers .

8.1 Review of snow mapping procedures with SAR

Almost all of the existing SAR based snow mapping algorithms classify only wet snow. In chapter 7 it was shown that the signatures of glacier ice and unglaciated areas were very similar in April and October'94 data. This is additional evidence of the difficulty of discriminating between dry snow covered and snow free surfaces at X- , C- or L-band. These results are confirmed by observations with C- and X-band single polarization radar [Rott and Nagler, 1992] and [Fily et al . , 1995] .

The clifference can be explained by the scattering mechanisms of dry and wet snow. In dry snow the penetration depth of the microwave radiation can reach tens of meters and the main contribution to the backscattering comes from the snow /soil interface, snow and soil volume. When the snow starts to melt even a small liquid water content of the snow pack results in a strong reduction of the penetration clepth and the main contribution to the backscattering comes from the air-snow interface and the snow volume. The backscattering coefficients are relatively low at all 3 frequencies and classification of wet snow versus dry snow covered or snow free surfaces is possible [Nagler, 1996] .

The infiuence of the local incidence angle on the backscattering coefficients can

121

Chapter 8. Target classification in the high alpine test site 122

not be neglected in mountainous terrain. Several methods were proposed to reduce it. The correction of the topographic effects is part of each classification algorithm in scenes with complicated topography. The effect of the local incidence angle on the backscattering coefficients is usually reduced by normalizing 0'0 with backscattering functions or calculating ratios between backscattering coefficients.

8 .1 . 1 Snow classification algorithms based on C-band po-larimetric parameters

8 .1 . 1 . 1 The classification procedure proposed by H. Rott (1994)

The first attempt to make a classification using fully polarimetric data is described in [Rott, 1994] . The algorithm is based on AIRSAR data acquired on 18 August 1989 over the test site Ötztal and is an extension of a procedure which uses a single channel SAR [Rott et al . , 1988] . At the time of the overflight rainfall was observed up to altitudes of 3300 m, the snow on the accumulation area of the glaciers was wet and rough, glacier ice and unglaciated areas were snow free.

The first part of the algorithm uses only the C-band HH amplitude image which is geocoded, low pass filtered and corrected for additive noise. A simulated image is created by applying an empirical backscatter function to the local incidence angle map. A threshold was applied to the ratio between the SAR amplitude image and the simulated image and three classes were separated: snow- and ice-free, glacier ice and snow. Shadow and layover zones were not included in the classification. The classification rules are:

if 3 .5 < ( � - �)/ � then snow

if 1 .8 < ( � - �)/ � < 3.5 then glacier ice

if 1 .8 > (� - �)/� then snow- and ice-free

where aRf, is the HH backscattering coefficient at C-band, a��ise is the noise equivalent backscattering coefficient at C-band a�im is the simulated backscattering coefficient. In order to improve the classification results the following condition for the depolarization ratio was used:

if (a�� - O'��se) / (a�� - a��ise) < 0.04 then snow

The results of the classification derived from AIRSAR data were compared with those derived from a Landsat TM image acquired 6 days later. Classification errors are due to the range dependence of the system noise in the AIRSAR data, which is assumed to be constant in the algorithm described above.

In addition, the spectral backscattering ratios were investigated: the L-band/Cband power ratio indicated good discrimination of snow and ice surfaces versus unglaciated areas, but poor separation of snow versus ice; the P-band/C-band power ratio shows good discrimination of the 3 classes snow, glacier ice and unglaciated areas.

Chapter 8. Target classification in the high alpine test sitc 123

The algorithm uses a simulated image to reduce the topographic effects on the a�?t backscattering coefficient. To generate the backscatter function accurate additional information is needed: the sensor location, a high resolution DEM, and precise modeling of the angular backscattering behavior for snow-free and snow-covered terrain. . These may be sources of errors in the classification.

8 .1 . 1 .2 The classification procedure of Shi et al. (1994)

An attempt to find a general technique of mapping snow that does not require cletailed topographic information is presented in [Shi et al. , 1994] . The data used are C-band AIRSAR data from 18 August 1989 and 25 June 199 1 acquired over the test site Ötztal.

In order to reduce the effect of the local incidence angle the backscattering coefficients, a0 , should be normalized with functions, f (Bi) , which represent the angular clependence of the backscattering coefficients of a given target , depending on the dominant scattering mechanism:

The normalized backscattering coefficient , a� , is related to the target backscattering properties. The intensity measurements, a0 , from 1989 AIRSAR data were normalized with functions f(Bi ) = cosn Bi , where n = 2 .2 for VV and n = 1 . 9 for HV polarization. The localizations of the training sites were selected from a Landsat TM image acquired six days later. Mean backscattering coefficients were derived for each class from lOxlO pixels2 data windows. As a measure of the texture of the SAR image the standard deviation of the normalized backscattering coefficients was chosen. lt was shown that the local incidence effect was reduced and the ability to separate snow, ice and rock classes can be improved using this parameter.

The separability of the classes and the angular dependencc were evaluated for three polarization measurements: the degree of polarization for the incident wave in vertical polarization, the depolarization factor a�� I a�� , and the VV backscattering coefficient normalized by the total power, a�� I a�� ( see defini tions in chapter 3) . The advantage of these measures is that they show comparatively small depenclence on the illuminated area, therefore no topographic information is required for classification.

After calculating the separability1 between all pairs of classes it was found that:

• the clepolarization factor provieles the best discrimination between all classpairs,

• the clegree of polarization of the vertically incident wave is a good discriminator between wet snow and unglaciated surfaces but poorly separates glacier ice from other surfaces,

1 The separability between classes j and k is defined by S3· k = IJ.Lj +-1-Lk l , where J.L and s are mean I s, Sk

values ancl standard deviations of the features.

Chapter 8. Target classiflcation in the lligh alpine test site 124

• the normalized VV backscattering coefficient is a good discriminator between wet snow or glacier ice with other targets, but does not discriminate well between these two targets.

The AIRSAR and Landsat TM images from August 1989 were both classified with a supervised Bayes classifier and the results were compared, taking the Landsat classification as reference. From AIRSAR data the following input data were chosen: C-band VV polarization only, polarization measurements only, and both intensity and polarization measurements. The wet snow and ice-free regions were well classified by SAR, but the glacier ice was poorly classified. This is due to the fact that the rough, partly rock-covered ice surfaces near the glacier margins and the pro-glacial morairre have similar SAR backscattering signatures.

In the AIRSAR data from 1991 the separability between snow and ice free classes was better because the wet snow surface was smoother than in 1989. The classification result was qualitatively in good agreement with the field observations.

8 .1 .2 Classification with repeat pass SAR data developed by T. Nagler (1996)

In [Nagler, 1 996] a classification algorithm based on ERS-1 AMI SAR data over the test site Ötztal is presented. The procedure requires as input:

• a SAR reference image (dry snow conditions or snow free) and a SAR snow image (wet snow conditions) from crossing orbits (ascending and descending pass)

• high precision DEM

• sensor and orbit parameters, ground control points

• calibration information

After preprocessing of each SAR image ( calibration, coregistration, speckle filtering) , the ratio between the snow and reference image is calculated in radar geometry on a pixel by pixel basis according to equation 8. 1 and then geocoded.

ao A 0 _ 10 l ( ws,pass ) ua - og1o o (J ref,pass

(8. 1 )

The indices ws, ref, and pass refer to the wet snow image, the reference image and the ascending or descending pass respectively. A threshold is applied to the ratio image and, because these steps are applied separately for ascending and descending passes, two snow maps are obtained. Additionally, two binary masks for areas with layover, shadow and excluded incidence angles are calculated.

Because of the steep incidence angle of ERS-1 (23°) a lot of information is lost due to the layover. Therefore the next step in the procedure is pixel by pixel combination of the ascending and descending snow maps in order to reduce this effect. The combination rule is described in detail in [Nagler, 1996] . It results in the reduction


of the layover and shadow regions from 36% and 41% in the ascending and descending image, respectively, to 8% in the combined image.

Problems and source of errors in the procedure indude:

• in the microwave domain it is impossible to discriminate snow and firn which are therefore treated as one dass. This is of relevance for mass balance studies in certain years.

• errors may result from different snow conditions during the day and night passes.

• dry snow cannot be detected, hence, if the snow at high altitudes does not melt, it will be dassified as snow free.

8 .1 .3 Classification with polarimetric multi-frequency SAR data

8 .1 .3 .1 Classification algorithms of J. Shi and J. Dozier (1997)

Two dassification algorithms based on SIR-C/X-SAR data were developed by [Shi and Dozier, 1997] for the test site Mammoth Mountain in Sierra Nevada. The scene is characterized by complicated topography but, unlike Ötztal, it does not have glaciers. During SRL-1 the site was partly covered by snow which was wet at lower eievatians and dry at high altitudes. Six dasses were selected: dry snow, wet snow, lake, bare surface, short vegetation and forest. As input data the following measured parameters were used:

• intensity measurements or backscattering coefficients and average power (span) radiometrically corrected with sin Bi/ sin ()0 and normalized with f ( ei) = cosn ei ·

• polarization properties ( the degree of polarization for H and V incidence, depolarization factors, polarization ratio, the backscattering coeffi.cients normalized by span power, HHVV correlation coefficient and the coeffi.cient of variation) and ratios between backscattering coeffi.cients at different frequencies.

• threc synthesized images (C-band, C-/1-band and 1-/C-band) to enhance the cantrast between short vegctation and dry snow.

There are 39 different measurements to dassify the surface covcr from which 18 were selected. The following selection criteria were considered: the dependence on the local incidence angle, thc separability of each dass pair , and the correlation between the measurements. The tasks carried out in this study were:

1 . mapping dry snow cover,

2. discriminating dry and wet snow,

3. mapping wet snow without using a DEM.


Three classification procedures based on decision tree classifiers (DTC) are proposed. The first one uses intensity measurements, polarization properties and frequency ratios and is able to map dry snow and to discriminate dry and wet snow. The second and the third are based only on polarization properties and backscattering ratios between different frequencies. They are designed to achieve the third task, mapping wet snow when no topographic information is available. The difference between the second and the third algorithms is that one uses only C- and L-band data, the other needs the 3 frequency data as input. It was tested if the availability of X-band data improves the classification accuracy. The ratios of C- or L-band backscattering coefficients to X-band VV improves the separation of forests from bare soil and short vegetation. The ratio L-band VV to X-band VV improves the separability between wet snow and short vegetation.

The acquisition of a cloud free Landsat TM image between the SIR-CIX-SAR data takes allowed the verification of the classification results. The TM data were classified in two ways: a binary classification into snow lsnow-free, and a map of the fractional snow cover in each pixel.

The SAR classifications are about 77% as accurate as the binary TM classification, but , compared with the snow fraction classification, all underestimate the snow covered arcas in regions of mixed pixels. This happens in forested regions. The first classifier was tested for dry snow conditions (data takes 51 . 1 and 67. 1 , acquired in the early morning) , and for wet snow conditions (data take 136.2 , acquired in the afternoon) . Some problems appear when vegetation is misclassified as dry snow at low elevations.

The parameters of the classifiers were determined with training data over the test site Mammoth Mountain. The classification trees may look different in another site because the backscattering properties of the targets may differ. The method to reduce the infiuence of the local incidence angle on the intensity is unfortunately empirical and it is not applicable to other sites, where the n coefficients are not available. The algorithms are adjusted to the test site Mammoth and could not be successfully applied for Ötztal.

8.1 .3 .2 Decision tree classification algorithm developed by Forster e t al. (1996)

A classifier using SIR-CIX-SAR data was proposed by [Fm·ster et al. , 1996] to delineate glacier zones in the South Patagonian Icefield (SPI) . They correlated the radar defined zones to estimated physical and geometrical properties of the glacier without any ground-truth.

The decision tree is based on SIR-C data from SRL-1 and SRL-2 and uses the following ratios between backscattering coefficients: a�� O"�X , 0'�� I 0'��, 0'�� I O"�X and averaged backscattering coefficients: ( 0'�� + a�7t) 12, ( 0'�� + 0'��) 12. The decision rules are based on backscattering characteristics which correspond to four defined radar zones. In the A zone, situated at high elevation, the C-band HH and HV backscattering coefficients are larger than at L-band. It is interpreted as a region with drier snow. Two regions of wet snow were classified. Zone B where the L-band backscattering coefficient is large, zone C with low 0'0 values at both C- and L-band, and largc polarization ratio at C-band. The glacier ice and the heavily crevassed


parts belang to zone D characterized by high and similar backscattering coefficients at C- and L-band. The X-band VV backscattering coefficient was not used for the classification because it was found to have the same spatial variability as the Cband VV backscattering. The L-band polarization ratio is not useful because of its insignificant spatial variation. Unfortunately the radar defined zones were chosen by visual impression and neither ground based data nor other verification was made. The algorithm was found not to be of relevance for glaciers in alpine regions.

8.2 Feature analysis and target separability for

the Ötztal data

8 .2 .1 Selection of the feature vector

Polarimetrie multifrequency data offer the possibility to calculate several parameters for each pixel. Commonly used polarimetric measures, like the elements of the covariance matrix ( co- and cross-polarized backscattering coefficients, the phase and the magnitude of the correlation between the HH and VV scattering matrix elements) , total power (span) at C- and L-band, and the X-band VV backscattering coefficient, were calculated. In figures 8. 1 and 8 .2 the magnitude and phase images of lZwv and Pkhvv are shown for DT 46 in April and October'94. Prior to the image generation the real and imaginary parts of the complex correlation coefficient, Re{phhvv } and Im{phhvv } , were low pass filtered with a 3x3 pixels2 window. The HHVV phase difference images are noisy, especially during SRL-2, and show less differences between the various targets than the magnitude images. The behavior of the phase is not consistent with theory which predicts less spread of qyhhvv when the magnitude, IPhhvv l , is high [Quegan et al. , 1994] .

Before choosing the components of the feature vector the correlations between intensity and correlation coefficient images at the three frequencies were investigated. The backscattering coefficients are radiometrically corrected pixel by pixel by multiplication with the factor sin ed sin ep , where ei is the local incidence angle, eP the processor incidence angle (for details see section 4. 1 .2) . A 3x3 pixels2 low pass filter is applied to the intensity images. The correlation matrix, corresponding to the multi-channel image derived from DT 46 SRL-1 data, is shown in table 8 . 1 . Shadow and layover areas were not used for the calculations. The correlations may be grouped as follows:

• correlation coefficients larger than 0.96 between pairs of co-polarized backscattering coefficients and total power at the same frequency band ( C- and 1-band) . The total power is dominated by the co-polarized backscattering coefficients , thus it behaves very similar.

• correlations of about 0.9 between C- and L-band co-polarized and total power intensity images, and between co-polarized and total power intensity images at C-band and a�; .

• correlations of about 0.8 between co-polarized or total power intensity images

Chapter 8. Target classilication in t;}w high alpine f;est site

tFlight dir. N � � Look dir. " DT 46 SRL-1

128

Figure 8. 1 : Magnitude and phase of Phhvv images in radar geometry derived from SRL-1 DT 4G quad-pol data at C- and 1-band. In thc IPhhvv l images black correspond to IPhhvvl = 0. and white to IPhhvvl = 1 . Phase images are formed with absolute values: (hhvv = 0 are shown in black and i (,bhhvvl = 7r in white.

Chapter 8. Target classification in the high alpine test site

tFlight dir. N � Look dir. � DT 46 SRL-2

Figure 8.2 : As figure 8 .1 but for SRL-2 DT 46.

129


Chan. Bi I Pthvv l I Pkhvv I a��· (Jou vv a��· (J�; Bi 1 .00 �P�wv l - .421 1 .00

Phhvv - .344 .646 1 .00 oc - .597 .296 .248 1 .00 (Jhh

(Joc vv - . 590 .287 . 237 .968 1 .00 oc (Jhv - .496 - .009 - .002 .794 .788 1 .00

(Joc - . 597 .239 . 199 .983 .984 .869 1 .00 �L -. 594 .298 .255 .898 .891 .752 .901 (Jhh

(JOL vv - .584 .308 .257 .894 .890 .740 .896 OL (Jhv - .492 .044 -.039 .746 .738 .863 .799

(JOL - .593 .262 .206 .901 .895 .799 .914 tp (Jox - .607 .319 .281 .906 .892 .726 .899 vv

130

(J�x (JOL vv a�� (J�;

1 .00

.966 1 .00

.800 .797 1 .00

.984 .984 .871 1 .00

.813 .823 .647 .815

Table 8. 1 : The correlation matrix of the multi-channel SIR-C/X-SAR image derived from DT 46 SRL-1 . The image channels are: local incidence angle map, magnitude of the HHVV correlation coefficient at C- and L-band and intensity images at C- , L- and X-band.

and cross-polarized backscattering coefficients at the same frequency; also between the L-band co-polarized a0 and total power versus CJ�J .

• correlations of about 0.75 of co-polarized a0 and total power versus crosspolarized a0 at different frequencies; also for ag� and a�J .

• correlations lower than 0. 7 between (Jg� and CJeJ channels and between I pfhvv I and I Pkhvv l ·

• correlations between the image channels and the local incidence angle are, as expected, negative and are lower than 0.6.

• correlations between I pfhvv I or I Pkhvv I and the intensity channels are lower than 0.4.

A simple method to reduce the incidence angle dependence and the redundant information in the intensity channels is to calculate ratio images. 2 1 ratios between the 9 backscattering coefficients from table 8. 1 were calculated. Among them only the depolarization ratios and spectral ratios show reasonable spatial variability. As components of the multidimensional feature vector the following (images) were cho-sen: OC OL OL OL OC OL OL

I c

I I L

I ahv (Jhv (Jhv ahh ahv (Jhh (Jhv

Phhvv ' Phhvv ' OC ' --oi ' OC ' OC ' OX ' OX ' OX (Jtp (Jtp (Jhv ahh avv (Jvv avv (8.2)

The recalculated correlation matrix of the multi-channel SIR-C/X-SAR image is shown in table 8.2.

The correlations with the local incidence angle map and between the ratio channels are considerably reduced. Because the co-polarized backscattering coeffi.cients and the total power channels are strongly correlated, the depolarization ratios at C b d OCj OC OCj OC d OCj OC d t L b d OLj OL OLj OL d - an ' CJ hv CJ tp ' CJ hv CJ hh ' an a hv CJ vv ' an a - an ' CJ hv a tp ' CJ hv (J hh ' an CJ��(J�t , have correlations larger than 0 .96, so practically any may be chosen as

aOX vv

1 .00


Chan.

ei �P�wv l Phhvv oc I oc (Jhv (Jtp

OL I OL (Jhv (Jtp OL I oc

(Jhv (Jhv OL I oc (Jhh (Jhh aoc laox hv vv OL I OX (Jhh (Jvv OL I OX (Jhv (J vv

ei J pfhvv l 1 .00

- .421 1 .00

- .344 .646

.373 -.645

.291 - .549

- . 1 15 . 184

- .048 .055

. 199 - .490

- . 148 . 1 16

.084 - .287

I Pkhvv I � � a'!l.J

� a t" at"

1 .00

- .534 1 .00

- .701 .626 1 .00

- .020 - .319 . 182 1 .00

. 128 . 103 - . 189 .493

- .405 . 732 .451 - . 195

. 161 -.051 - .228 .434

- .364 .394 .515 .544

1 : 1 1

a\'{' ah� axt a?,t � aOX aOX aOX

v v v v vv

1 .00

. 188 1 .00

.757 .451 1 .00

.507 .713 .692 1 .00

Table 8.2 : The correlation matrix of the multi-channel SIR-C/X-SAR image derived from DT 46 SRL-1 . The image channels are: local incidence angle map, magnitude of the HHVV correlation coefficient at C- and L-band and ratios between X-, C- and L-band intensity images.

a component of the feature vector. The ratios between the C-band co-polarized backscattering coeffi.cients and a�;; do not contain useful information for target classification in the Alpine test site, as evident from the signature study in chapter 7.

The correlation matrix for image channels derived from DT 46 SRL-2 data (table 8.3) reveals similar characteristics to the correlation matrix for SRL-1 data.

8.2 .2 Selection of the surface classes

Because the signatures of glacier ice and rocks are similar in SAR data, optical imagery is preferably used to separate glaciers from unglaciated areas. In this study the glacier mask is derived from Landsat TM data acquired on 16 August 1992, the last cloud free image before October 1994. Because the glacier boundaries did not change rapidly it can be considered a good approximation for the situation in 1 994. The discriminant analysis and the classification algorithms presented in this chapter are always applied separately to glacier-eavered and glacier-free areas.

On the glaciers two classes should be discriminated for mass balance studies:

• the accumulation area,

• the ablation area, which often (but not necessarily) is identical to the glacier ice area.

The ability of a SAR classifier to detect these classes is based on differences in their roughness, wetness and structure. Errors in the classification may occur if the glaciers are covered by wet snow as happened in October. The classified wet snow areas are identical with the accumulation areas only when the glacier ice is snow free. Another problern for mass balance studies may be the presence of firn. Because of their similar properties at radar wavelength snow and firn usually cannot be separated. Firn is snow which accumulated in previous mass balance years and


Chan. ()i j pfhvv l i Pkhvv l � � � (J tp (J tp C1f.v ()i 1.00 �P�wv l -.458 1 .00

Phhvv -.437 .594 1 .00 oc I oc .389 -.653 - .531 1 .00 ()hv ()tp OL I OL . 290 - .388 - .606 .533 1.00 ()hv ()tp OL I oc - .263 . 103 - . 107 - .182 .358 1 .00 ()hv ()hv OL I oc ()hh ()hh - .218 - . 123 -.057 . 190 - .004 .646 oc I ox ()hv () vv . 344 -.421 - . 258 .635 .277 -.299 OLl OX ()hh ()vv - . 157 - .013 .097 .027 - . 127 .448 OLl OX ()hv ()vv .053 - .256 -.305 .364 .537 .619

132

(J�� a/:� a/:f; aY.t � aOX aOX aOX v v v v v v

1 .00

.027 1 .00

.731 .432 1 .00

.581 .561 .742 1 .00

Table 8.3: The correlation matrix of the multi-channel SIR-C/X-SAR image derived from DT 46 SRL-2. The image channels are: local incidence angle map, magnitude of the HHVV correlation coefficient at C- and L-band and ratios between X- , C- and L-band intensity images.

therefore, from a glaciological point of view, firn belongs to the ablation area. In October 1994 the firn did not cover significant areas on the test site. Photographs taken on the accumulation and ablation areas on 1 October 1994 are shown in figures 8.3 and 8.4. The differences in roughness between the dasses can be dearly observed. In April 1994 these areas were covered with about 3 m of dry snow.

On the unglaciated areas of Ötztal the main dasses which are of interest for hydrological or environmental investigations are:

• unvegetated dasses, mainly rocks and moraines.

• sparse vegetation, grasses and sedges mixed with scattered rocks. This dass corresponds approximately to surfaces with Normalized Differential Vegetation Index 0 . 15 < N DV I < 0.4. N D V I was derived from the surface albedo in bands 3 and 4 of Landsat TM, with N DV I = ��t��.

• dense short vegetation (meadow and small shrubs) . This dass corresponds approximately to N DV I > 0.4.

• coniferous forests. These have high N DV I thus cannot be dearly separated from meadows using visible and IR imagery, but can be detected in SAR images because they have high (]"�� backscattering coeffi.cient.

SAR and optical sensors are sensitive to different target properties. In the case of vegetation, scattering of the incident microwave radiation is related to the geometry and dielectric properties of the vegetation type, whereas in optical and IR domain the plant vigor determines the amount of refiected solar illumination. Therefore one cannot expect that SAR dassification corresponds to N DV I.

In figures 8.5 , 8.6 and 8.7 the dasses on the ice-free areas are illustrated. On the glacier termini the ice is partially covered with moraines. In the pro-glacial valley and on the slopes morairres and rocks mixed with grass can be seen. These dasses have similar roughness and different optical properties. Therefore their separation is

Chapter 8. Target classification in tlw higl1 alpine l;est; site lJJ

Figure 8 .3 : The ferner j Gepatschferner snowfall in September.

accumulation area on 1 October 1994.

on the glacier plateau KesselwanclThe surface was smooth due to the

Figure 8.4: The ablation area of Hintereisferner on 1 October 1994. The complicated structure of bare ice surfaces is obvious.


Figure 8.5 : Rocks and moraines at the terminus of Hintereisferner in summer. In the lower right corner is sparse vegetation mixed with rocks.

difficult in the microwave domain, but Straightforward in the visible and IR dornain. At lower altitudes coniferous forests and meadows are dominant. Both classes are photosynthetically active and separation based on N DV I is not possible in summer images. Differences in the structure of the two vegetation types enable Separation with radar data.

8.2.3 Incidence angle dependence of the components of the feature vector

The incidence angle dependence of the polarimetric measures selected as components of the feature vector defined in section 8.2 , is shown in figures 8.8 to 8. 15 . The procedure applied to derive the angular pattern is described in chapter 7. The Landsat TM derived masks correspond to the surface classes presented in 8 .2 .2 with the exception of forest. This class is not included in this analysis because of its reduced number of pixels which cannot cover the whole range of incidence angles.

For the depolarization and spectral ratios the incidence angle dependence is smaller than for the backscattering coeffi.cients but is not cornpletely eliminated. This would be possible if the backscattering coeffi.cients would have identical angular patterns. But, as observed from the analysis presented in chapter 7 and as predicted by backscattering models, the angular patterns of a0 depend on polarization, frequency and target properties.

The average signatures give a first indication about which ratios and decision rules are suited for a hierarchical classification in order to discriminate among the classes. For SRL-1 data the best features for dass separation are the C-band correlation coeffi.cient, I pfhvv I , and the ratios (JR� I (J�; and (JR� I (J�; on both glaciated and

Chapter 8. Target classification in tlw high alpine test site 135

Figurc 8.6 : High alpine grass surface. In the background thc tcrminus of Hintercisfcrncr .

Figurc 8. 7: Coniferous forest near Vent, at about 1900 m altitudc. Shrubs and, on the lcft siele of thc vallcy, meadow can be observcd.

Chapter 8. Target classiflcation in the high alpine test site 136

ice-free areas. The signatures of dense and sparse vegetation classes are overlapping but differences are observed compared to the rocks class. The possible ratios able to separate between accumulation and glacier ice areas in SRL-2 data are aR�/aR� )

OLj OC OLj OX OLj OX Q th . f .

·1 •t• b d b a hh a hh ) a hh a vv ) a hv a vv . n e 1ce- ree areas Slml an 1es are o serve etween rocks and sparse vegetation which may be separated in SRL-2 from dense vegetation

·th 1 c 1 oc; oc oc; ox d OLj ox Wl Phhvv ) ahv atp ) ahv avv ) an ahv avv .

8.2.4 Discriminant analysis

Because the dynamic range of the components of the feature vector is only a few dB (section 8.2.3) and the differences for the various surfaces are small) the separability of the surface classes was assessed. The correlation coeffi.cient and ratio images were represented as channels in an image database. Thus standard digital image processing techniques could be applied on the multi-channel image.

8.2.4.1 Spectral signature data generation

About 1000 training pixels are selected for each surface class defined in section 8 .2.2 . The desirable number of training samples per spectral class is 100N where N is the climension of the feature space [Richarcls) 1986] . For each surface class spectral signature clata are createcl by sampling the components of the feature vector in the training areas. The signature data include statistical values) like mean and standard cleviation) correlation matrix) covariance matrix) the determinant of the covariance matrix) inverse covariance matrix) ancl triangular inverse covariance matrix of each selectecl channel. These signatures are usecl below for calculating separability among classes ancl in supervised classification.

8.2.4.2 Separability measures for two or more spectral classes

One commonly used measure of the separability is the Transformed Divergence (TD) measure) which is defined as [Richards) 1986] :

d� = 2(1 - exp( -dij /8)) (8.3)

where

is the divergence of the pair of spectral classes wi ancl Wj) assumed normally distributed with vectors of mean values and covariance matrices given by mi, Li ancl mj, Lj) respectively. Tr{} is the trace of the matrix) -l derrotes inverse and t transpose. The first term in the expression 8.4 is the square of the normalizecl distance between the means of the distributions whereas the second term involves only covariances.

The TD measure cannot exceed 2.0; so that d'[j = 2 indicates a complete separation between the classes ancl woulcl imply a classification with 100 % accuracy of the pixels into those classes. The larger the separability values) the better the classification results will be. Based on the relation between d'[j ancl the probability


1.0

0.8

� 0.6 ..c

if 0.4

0.2

0.0

0

-2

1D :!:!. Üc.

-4 o-

b -Ü> -6 O.J::

b

-8

-10

0

-2

1D :!:!.

Ü> -4

o.J:: b

-...J > -6 O.J::

b

-8

-10

SRL-1 DT 46 C-band

accumulation area (*) glacier ice (.6)

20

20

20

40

ei [deg] 60

SRL-1 DT 46

40 60

ei[deg]

SRL-1 DT 46

40

ei [deg) 60

80

80

80

1.0

0.8

� 0.6 ..c

� 0.4

0.2

0.0

0

-2

ffi' �

...J -4 o.S

b -...J> -6 O.J::

b

-8

-10

0

-2

1D :!:!.

Ü.J:: -4

O.J:: b

-...J .J::-6 o.J::

b

-8

-10

20

20

20

SRL-1 DT 46

40

ei [deg]

L-band

60

SRL-1 DT 46

40

ei [deg) 60

SRL-1 DT 46

40 60

ei [deg)

t:n

80

80

80

Figure 8.8: Incidence angle dependence of the components of the feature vector derived from DT 46 SRL-1 for accumulation and glacier ice areas.


0

-2

iii' :E.

�� -4

b -Ü> -6 o..r:::::

b

-8

-1 0

SRL-1 DT 46

accumulation area 0+0 glacier ice�)

20 40 60 ei [deg]

80

0

-2

iii' :E.

�� -4

b --' ..r::::: -6 o..r:::::

b

-8

-10 20

SRL-1 DT 46

40 60 80 ei [deg]

0 .----------------------,

-2

iii' :E.

�� -4

b --'> -6 o..r:::::

b

-8

-10 20

SRL-1 DT 46

40 60 80 ei [deg]

Figure 8.9: Figure 8.8 continued.

138

Chapter 8. Target classification in the high alpine test sitc

1.0 1.0

SRL-1 DT 46 0.8 C-band 0.8

� 0.6 � 0.6 .s:::. .s:::. .s:::. .s:::.

..Q... ..Q... 0.4 0.4

rocks(L)

0.2 sparse Vegetation( e) 0.2

dense vegetation( o) 0.0 0.0

20 40 60 80 20 ei[deg]

0 0

SRL-1 DT 46 -2 -2

iii' iii' :!:!. :!:!. Üa.

-4 c!.e- -4 o-

b b - -

Ü> -6 ....1> -6 o..r::: o..r:::

b b

-8 -8

-10 -10 20 40 60 80 20

ej[deg] 0 0

SRL-1 DT 46

40 60 ei [deg]

SRL-1 DT 46

40 ei [deg)

60

-2

~ -2 E2:b�

iii' iii' :!:!. :!:!. Ü> -4 ü..r::: -4 o..r::: o..r:::

b b - -....1> -6 SRL-1 DT 46 ....1 ..r::: -6 SRL-1 DT 46 o..r::: o..r:::

b b

-8 -8

-10 -10 20 40 60 80 20 40 60

ei[deg] ei [deg]

139

80

80

80

Figure 8.10: Incidence angle dependence of the components of the feature vector derived from DT 46 SRL-1 for unglaciated areas.

Chapter 8. Target classification in tlw lligl1 alpine t;cst; sit.e

0 0 SRL-1 DT 46

-2 rocks(.6) -2 1ii' sparse vegetation(e) :!:!.

dense vegetation( o) ��

-4 b

-Ü> -6 o..c

b

1ii' !!. -4 ��

b - -6 -l..c o..c

b -B -B

·10 20 BO 20

SRL-1 DT 46

40 60 ei [deg]

,-----------------------�

-6

��-10 -

-l> o..c

b ·12

-14

20

SRL-1 DT 46

40 60 ej[deg]

Figure 8.1 1: Figure 8 . 10 continued.

BO

140

80

Chapter 8. Target classifi.cation in the high alpine test site

1.0 1.0

SRL-2 DT46

0.8 0.8

� 0.6 � 0.6 ..c: ..c:

Jf. ..c: ..Q...

0.4 0.4

0.2 accumulation area (*) 0.2 glacier ice ({'>)

0.0 0.0 20 40 60 80 20

9; [deg]

0 0

SRL-2 DT 46

-2 -2

m m 32.

-4 :!:!.

-4 Üc. �.9-o .. b b

- -

Ü> -6 ....1> -6 o.s:::. o..c:

b b

-8 -8

·10 -10 20 40 60 80 20 9; [deg]

4 4

m 2 m 2 :!:!. :!:!.

Ü> ü.s::. o..c: 0 o.s:::. 0 b b

- -....1> ....I..C: o..c: o..c:

b ·2 b -2 SRL-2 DT 46

-4 -4

20 40 60 80 20 9; [deg]

141

SRL-2 DT 46

L-band

40 60 80 9; [deg]

SRL-2 DT 46

40 60 80 9; [deg]

SRL-2 DT 46

40 60 80 9; [deg]

Figure 8 . 12 : Incidence angle dependence of the components of the feature vector derived from DT 46 SRL-2 for accumulation and glacier ice areas .

Chapter 8. Target classification in the higl1 alpine (;esl; sit.n

-6

iii' -8 :!:!.

�� b -10

-Ü> o..r:.

b -12

-14

SRL-2 DT 46

accumulation area (*) glacier ice (6)

20 40 60 ei [deg)

0

-2

iii' :!:!.

�� -4

b ---l> -6 o..c::

b

-8

-10 20

80

4

iii' 2 :!:!.

�� b 0

-...J..c:: o..c::

b -2

-4

20

SRL-2 DT 46

40

ei [deg) 60

Figure 8 . 13: Figure 8.12 continued.

SRL-2 DT 46

40

80

ei [deg) 60

I I ·.�

80

Chapter 8. Target classification in tlw high alpine test site

1.0,----------------,

0.8

0.4

0.2

SRL-2 DT 46

rocks(6.)

sparse vegetation(e)

dense vegetation( <>)

20 40 60 ei [deg)

80

o .-----------------,

-2

0 a. -4 o ... b

-Ü> o.r:.

b -6

-8

4

m 2 �

Ü> o.r:.

b -....J> o.r:.

b

0

-2

-4

20

20

SRL-2 DT 46

40 60 ei [deg]

SRL-2 DT 46

40 60 ei [deg)

80

80

1.o.-----------------,

0.8

0.4

0.2

20

SRL-2 DT 46 L-band

40 60 ei [deg)

80

o.-------------.

m �

-2

c5 .9- -4 b

-....J> o.r:. -6 b

-8

4

m 2 �

Ü.r:. o.r:.

b 0 -....J.r:. o.r:.

b -2

-4

20

20

SRL-2 DT 46

408·1

60 [deg]

SRL-2 DT 46

40 60 ei [deg)

80

80

143

Figure 8 . 14: Incidence angle dependence of the components of the feature vector derived from DT 46 SRL-2 for unglaciated areas.


0

·2 iD :!:!.

�� -4

b -Ü> ·6 o..c

b

·8

SRL-2 DT 46 4 SRL-2 DT 46

rocks(t-.) iD :!:!. 2 sparse Vegetation( e)

�� dense vegetation(O) b 0

20

-....J..c o..c

b ·2

·4

� � 00 � � � ei [deg] ei [deg]

-6

iD -8 :!:!.

�� b -10

-....J> o..c

b ·12

·14

.------------------------,

20

SRL-2 DT 46

40 60 ei [deg]

80

Figure 8 . 15 : Figure 8 . 14 continued.

144

80

Chapter 8. Target classification in the high alpine test sitc

of correct classification [Richards, 1986] the following rulcs arc suggested for tlt<� separability values:

• 0.0< d'f; < 1 .0 (very poor separability)

• 1.0 < d'f; < 1 . 9 (poor separability)

• 1 . 9 < d'f; < 2.0 (good separability)

Very poor separability indicates that the two signatures are statistically very close to each other and they should be merged, poor separability indicates that the two signatures are separable, to some extent. In practice, no distinction between the signatures with very poor and poor separability was observed. The classcs with good separability between signatures (d'J; � 1 .99) could be well classified and for lower values of the distance measure the classification results were poor.

8.2.4.3 Separability of alpine targets based on the components of the

feature vector

The separability between each pair of the 6 spectral signatures was calculated using equation 8.3 . The transformed divergence values are shown in tables 8.4 and 8.5 for DT 46 SRL-1 and SRL-2 for the pair of signatures on the glaciers and the pairs on glacier-free areas. According to the rules described abovc, good separability is obtained between the accumulation areas (respectively wet snow in October) and ice in both seasons. The classes on the unglaciated areas, with the exception of forests, have poor and very poor separability in SRL-1 data while forest shows very good separability from the other classes in SRL-2 data. This situation is more or less expected from the signatures analyzed in chapter 7. The complete separability of forest is valid only for the selected training data in which areas with mixed pixels are avoided.

In both seasons significant differences in backscattering are observed on the glaciers between the two classes. In April the refrozen firn exhibits larger backscattering coefficients than the glacier ice, in October the slightly wet snow on the accumulation area has lower backscattering than the rough, exposed ice.

The surfaces covered by sparse vegetation have similar roughness to the unvcgctated areas. The coniferous forests are not well detected by the SAR signal in the SRL-1 data, because a significant part of the backscattered signal comes from thc wet snow on the underlying ground and not from the trees. The ground contribution is important because the sub-alpine forest is not so dense. In SRL-2 data thc volume scattering contribution of the trees dominates at 1-band therefore a good separation from the other classes is obtained.

8.3 Supervised classification based on MLE

The fiow chart of the supervised classification procedure based on a maximum likelihood estimate (MLE) is shown in figure 8.16. The components of the image feature vector are chosen from the multi-channel SIR-C/X-SAR image as described in section 8.2 . 1 . Using high resolution optical data (Landsat TM) thc segmentation of


SRL-1 DT 46 Spectral classes dfi accumulation areas & ice 1 .998 rocks & sparse vegetation 1 . 178 rocks & dense vegetation 0.991 rocks & forest 1.345 sparse & dense vegetation 0.761 sparse vegetation & forest 1.563 dense vegetation & forest 1 .647

146

Table 8 .4 : The separabilities of pairs of classes derived from SRL-1 data . All training sites

were covered by snow in this period. d!'ij is the transformed divergence measure. The dass "rocks" includes all unvegetated ice-free surfaces.

I SRL-2 DT 46 Spectral classes

wet snow & ice 1 .998 rocks & sparse vegetation 0.479 rocks & dense vegetation 1 .429 rocks & forest 2.000 sparse & dense vegetation 1 .302 sparse vegetation & forest 2.000 dense vegetation & forest 2 .000

Table 8 . 5: The separabilities of pairs of classes derived from SRL-2 data . d!' ij is the t.ransformed divergence measure.

Chapter 8. Target classification in thc higl1 alpine test site

Extraction of Feature Vectors

c::=:;:>j Segmentation

� �

� Maximum Likelihood

Oassifica tion

Terrain Corrected Geocoding

Sensor & Orbit Parameters

Image Simulation

147

Figure 8 . 1G: Classification procedure based on MLE applied to one SIR-C/X-SAR data t.ake.

glaciers and ice-free areas is performed. Additional topographic information (DEM) is necessary to create local incidence angle maps with layover and shadow masks, areas which are excluded from the calculations. Using orbit and ground control points the local incidence angle map is transformed into the radar geometry of the data. Spectral signature data is required as input for the supervised classification. The MLE classifier is applied separately to glaciated and unglaciated areas. The classified image is transformed into a map projection and a thematic map is created.

8.3.1 Description of the algorithm

The classification was performed with the Maximum Likelihood Classification (MLC) program of the image processing software EASI/PACE. The discriminant function for maximum likelihood classification, based upon the assumption of normal statistics, is given by:

Chapter 8. Target classiflcation in the l1igh alpine l;esl; si f;c 148

where 9i(x) is the function for class wi on pixel x; if d is the number of channels in the classification ( the dimension of the feature vector) , then the vectors in the equation 8.5 can be written as x = (x1 , ... , xd) , mi = (m1, ... , md); I Eil is the determinant of the covariance matrix, p(wi) = !Ai is the a priori probability for class wi, Bi is the relative a priori probability for class wi. From the spectral signatures created for each class the following measures are obtained: d, mi, Bi, E;-1, JEiJ, and Ti, the threshold value for class wi. In general, the matrix Ei1 determines the shape and orientation characteristics of the hyperellipsoid in feature space for class wi· The vector mi determines its position and Ti determines its size. The first step in the classification is to determine if the pixel x lies within the hyperellipsoid of the class wi. The following must be true:

(.... .... )t"'-1( .... .... ) < T2

x - mi L.Ji x - mi _ i (8.6)

The Mahalanobis distance is the square root of the left term of equation 8.6 [Richards, 1986]. It is obtained from the discriminant function (equation 8.5) for the special case of equal a priori probabilities p(wi), and covariances Ei= E (E can be a class average or a pooled variance) . If :i is not in any hyperellipsoid it will be assigned to the null class. If the pixel can be included in one or more classes 9i(:i) is calculated for each class and the pixel is assigned to the class which has the maximum 9i ( :i).

The EASI/PACE procedure requires as input the multichannel image data base and the spectral signature data for each class. The user may define and change Bi and T; and set the option whether pixels can be assigned to the null class. If the option is "yes" then a pixel is assigned to a class only if 8.6 is true. If the option is "no" then the parameter Ti is ignored and every pixel will be assigned to the most probable class. The output consists of thematic maps and a posteriori probabilities ( calculated with Bayes rule) that a pixel belongs to the class to which it was assigned. An output listing produced by MLC contains information about the pixel value used to encode the thematic map, the number of pixels in each class, the percentage of image covered by each class, Ti and Bi.

8.3.2 MLC classification results

The MLC procedure was applied separately on the glaciers and ice-free areas. Seasonal differences in backscattering properties will determine different classification results for SRL-1 and SRL-2 data. On the glaciers the classification is reduced to a two class problem, accumulation and glacier ice areas. On the unglaciated areas 4 surface classes must be classified.

Often there is no useful information about the a priori probability in which case Bi = 1 is set for all classes. This was assumed for the first run of the classification. In the case of the test site Ötztal the relative a priori probability of a class was estimated from the classified Landsat TM image from 16 August 1992 as the ratio

Chapter 8. Target classification in the lligh alpine l;csf; sil;e

DT 46 No. pixels Class signature in mask No. pixels glacier mask 97855 accumulation ice

No. pixels unglaciated areas 158141 rocks sparse vegetation dense vegetation forest

149

No. pixels TM classif. Bi

19370 0.4 33714 0.6

72234 0.4 46348 0.3 33270 0.2

r--o.r-Table 8 .6: The relative a priori probabilities Bi for dass signatures, estimated from clas

sification of Landsat TM data from 16 August 1993 and the number of pixels included in the glacier mask and unglaciated areas.

between the number of pixels classified in that class and the total number of pixels in the glacier or ice-free areas. The firn class in the Landsat classification covering about 27 % of the glacier area was equally divided between ice and accumulation area. The estimated values of Bi shown in table 8.6 were used to observe the infl.uence of this parameter on the result of MLC.

The default value for the Gaussian feature space threshold, 7i, when the spectral signature data are created is 3.0 standard deviation units. The default values of Ti are used at the first run of the classification then they were changed in order to improve the results. The null class option is successively set to "yes" and "no" because a large percentage of the pixels remain unclassified in all situations.

Glacier areas. About 60% of the pixels remain unclassified in both SRL-1 and SRL-2 data. The parameter Bi has a weak infl.uence on the classification result as observed in table 8.7. A possibility to reduce the number of unclassified pixels is to increase the class threshold, Ti, but although more pixels a.re classified the classifica.tion errors for the two classes increase. This result is not infiuenced by the selection of a priori probabilities.

For SRL-2 da.ta. when the null class option is set to "no" more tha.n 50% of the pixels a.re classified as accumulation a.rea, an unrealistic situation for the glaciers in Ötztal in October 1994. By decreasing Taccum and increasing Tice more pixels are classified as ice, but at the same time the unclassified area becomes I arger.

Unglaciated areas. For the first run of the classification the a priori proba.bilities a.re equal for all classes, then the values of Bi from table 8.6 are used. For both SRL-1 a.nd SRL-2 data. the null class is initia.lly :2: 37 % of the image pixels, and too few pixels are classified as rocks and dense vegetation (ta.ble 8.8). When the null dass option is set "no" 43.51 % of the a.rea is classified as dense vegetation ( 49.7 % for SRL-2 data) , which is undoubtedly too much. For the classes rocks, sparse vegeta.tion, and forest the result is less dependent on this option. This means that the pixels belonging to these classes were included in hyperellipsoids ( condition 8.6 is true) , whereas most of the pixels assigned to dense vegetation cannot be included in a.ny hyperellipsoid. Choosing a larger Ti when the null class is set "yes" will

Chapter 8. Target classification in Uw high al}Jinn l;nsl; sif;e

SRL-1 DT 46 Null dass YES Class Pixels % Image Ti Bi accumulation 19198 19.62 3.0 1 .0 1ce 19764 20.20 3.0 1 .0 not classified 58893 60. 18

accumulation 18958 19.37 3.0 0.4 1ce 20004 20.44 3.0 0.6 not classified 58893 60. 18 accumulation 18755 19 .17 3 .0 0.3 1ce 20207 20.65 3.0 0.7 not classified 58893 60. 18 accumulation 35123 35.89 4.0 1 .0 1ce 31221 31 .91 4.0 1 .0 not classified 31551 32.20 accumulation 43946 44.92 5.0 1 .0 1ce 35857 36.64 5.0 1 .0 not classified 18042 18.44

150

NO Pixels % Image 41764 42.68 56091 57.32 0 0 41348 42.25 56507 57.75 0 0

40953 41 .85 56902 58. 15 0 0

Table 8.7: Results of the maximum likelihoocl dassification performecl on the glaciatecl areas using SRL- 1 DT 46 clata. The number of pixels in each dass ancl the percentage of

image coverecl by the dass are shown. The effect of the parameters Bi ancl 7i ancl of the option "null dass" on the dassified pixels is shown. Ti defaults to 3.0 stanclarcl deviation units ancl Bi clefaults to 1.0 .

Chapter 8. Target classißcation in tlle high alpine test site

SRL-1 DT 46 Null dass YES Class Pixels % Image 1i Bi rocks 16184 10.23 3.0 1 .0 sparse vegetation 53674 33.94 3.0 1.0 dense vegetation 14746 9.32 3.0 1.0 forest 15021 9 .50 3.0 1.0 not classified 58516 37.00

rocks 58349 36.90 3.5 0.4 sparse vegetation 37481 23.70 2.5 0.3 clense vegetation 22 0.01 1.5 0.2 forest 9986 6 .31 3.0 0.1 not classified 52303 33.07 rocks 44424 28.09 3 .5 0.4 sparse vegetation 37481 23.70 2 .5 0 .3 clense vegetation 33588 21.24 4.0 0.2 forest 9329 5 .90 3.0 0.1 not classified 33329 21.07

151

NO Pixels % Image 20627 13.04 53684 33.95 68809 43. 51 15021 9 .50 0 0

62899 39.77 37493 23.71 47763 30.20 9986 6 .31 0 0

Ta.ble 8 .8: Results of the maximum likelihoocl classification performecl on the unglaciatecl areas using SRL-1 DT 46 clata. The number of pixels in each class ancl the percentage of ima.ge coverecl by the class are shown. The effect of the parameters Bi ancl Ti ancl of the option "null class'' on the classified pixels is shown. 7i clefaults to 3 .0 stanclarcl cleviation

units ancl Bi clefaults to 1 .0 .

increase the number of pixels in the dass "dense vegetation" and will redistribute the classification results for the other classes . If Ti is small and all pixels are classifiecl the three vegetation classes become less significant and the rocks class increases. These situations are illustrated for SRL-1 data in table 8.8.

The geocoded thematic maps corresponding to DT 46 from 12 April 1994 and DT 46 from 3 October 1994 are shown in figures 8. 17 and 8.18. The null dass option is set to "no"; in black appear the shadow and layover regions which were not incluclecl in the classification. Single classified pixels are eliminated with a 3x3 median filter appliecl to the MLC result.

The classification basecl on data from SRL-1 corresponds to winter conclitions on the test site. The glaciers and the unglaciated areas up to 2500 m altitude were covered by dry snow, at lower elevations wet layers were observed at the bottarn of the snow pack ( chapter 5) . On the glaciers the two classes shoulcl corresponcl to the extension of the accumulation and ablation areas from autumn 1993 or, according to the glaciological convention, to the end of the mass balance year 1992/1993. Thus the classification result may be comparecl with the extension of these areas derivecl from fielcl observations. Ice surfaces are incorrectly classified as accumulation areas in the crevasse zones which are very rough and have high backscattering coefficients. As preclicted by the discriminant analysis the separation of rocks and vegetation classes is very poor.

SRL-2 clata were acquired dose to the end of the mass balance year 1993/94

Clwptor 8. Target classification in tlw hip,l1 alpine tost sito 152

Figurc 8. 17: Thcmatic map of thc test sitc Öty;tal bascd on l\ILC classification of DT 46 acquired on 12 April 1994. White ... accumulation areas. bluc ... glacier ice areas, dark grey ... bare soil and rocks, light grcy ... sparse vcgetation, green ... densc vegetation, meadow, yellow ... forest. The red lincs corrcspond to thc bounclarics of the glacicrs Hintereisfcrner, Kesselwandferner and Vernagtfcrner.

thcrcforc for t.hc glacicr area.<> t.hc cla.<;sificat.ion rcsult. can bc compared wit.h t.hc mmlysis bascd on ficld obscrvat.ions. Thc cla.<;sificat.ion of t.hc unglaciatcd mca.'> shows somc errors among rocks, sparsc and dcnse vcgct.at.ion hut. t.hc forcsts, which, due to t.he high volume scat.tering cont.rihut.ion at. L-band, show a good scpambilit.y, are correct.ly c:lassified. Despit.c t.he small TD measmc densc veget.at.ion is bet.t.cr cle1.ssified t.han in SRL-1.

The accuracy of t.hc classificat.ion is cxpresscd in t.enns of confusion mat.riccs. ßccause t.hc available Landsat. TM image was acquircd two years bcfore t.hc SIRC/X-SAR ovcrflight.s, a pixcl by pixcl comparison of t.hc SAR and Landsat. cl�clssificat.ions is not. possiblc for thc glacicrs. For t.he first. cvaluat.ion of t.hc correct.ness of t.he dassificat.ion, t.hc t.raining pixcls used t.o generate t.he spect.ral signatures me considered t.o bc t.he rcfcrence dat.a. This met.hod t.o det.enninat.e t.he correct. classes is considered t.o be st.rongly bia.'>cd and yields t.oo opt.imist.ic accuracy of thc classificat.ion. It. is not. a st.at.ist.ically valid t.est. for t.he t.hemat.ic map accumcy but. is a.n indicat.ion of t.hc homogeneit.y of the t.raining sit.es and t.he sepamhilit.y of t.he t.raining dass sigua.t.ures [Schowengerdt., 1985].

The bcst. agrcement. is found betwcen accumula.t.ion and glacier icc classes in bot.h STI.L-1 and SRL-2 classificat.ions. The forest. in Oct.oher is also morc t.han 90% correctly d:-1ssified. The rocks are het.t.cr dassified in April t.ha.n in Oct.ober hut. for t.he veget.at.ion dasses a. bet.t.er classificat.ion in snmv free condit.ions is ohserved. This causcs a bct.ter overall agreement. for t.he SRL-2 da.'>sific:--tt.ion.

Clwpter 8. Target cl;-"k<;sification in tlw high alpine test site

Figure 8. 18: As figurc 8.17 but for DT 46 acquirccl on 3 Octobcr 1994.

DT 46 SRL-1 DT 46 SRL-1 Training

Training sit.es rocks sp. veg. de. veg. forest. sit.es I Ce accum. rocks 61 .9% 25.7% 10.3% 2.2% ice 98.5% 1 .5% sp. vcg. 42 .8% 37.2% 18.3% 1.8%

accum. 8.5% 91 .5% de.veg. 49.0% 21 .0% 29.8% 0.2% Overall agreement.: 95. 1 1% forest. 41 .7% 24.5% 12.6% 21 .2%

Overall agreement.: 45. 15%

153

Table 8.9: Confusion matriccs for classification of training data for SIR-C/X-SAR DT 46 of

12 April1994. T hc dassification was pcrformcd scparately on thc glacicrs and unglaciatcd

areas.

DT 46 SRL-2 DT 46 SRL-2 Training

Training sit.es rocks sp. veg. de.veg. forest. sit.cs ice accum. rocks 53.5% 31 .7% 14.4% 0.3%

I Ce 91 .2% 8.8% sp. veg. 27.6% 59.1% 13.3% 0 .0% accum. 10.9% 89. 1% de.veg. 19.2% 18.5% 62. 1% 0 .2%

Overall agrccment.: 90. 16% forest. 0.0% 0.7% 6.6% 92.7% Overall agreement: 59.24%

Tablc 8. 10: Confusion matriccs for classification of training clata for SIR-C/X-SAR DT

46 of 3 Octobcr 1994. T he clai-isification was performccl scparatcly on thc glacicrs and

unglaciated arcas.

Chapter 8. Target classification in tl1e high alpine test site

DT 46 SRL-1 Landsat

TM rocks sp. veg. de. veg.+forest rocks 56.7 % 18.9 % 24.4 %

sp. veg. 45.3 % 24.4 % 30.3 % de. veg.+forest 36.8 % 25. 1 % 38. 1 % Overall agreement: 42.6 %

154

Table 8 . 1 1: Confusion matrix between Landsat and SIR-C/X-SAR SRL-1 classifications of the unglaciated areas.

DT 46 SRL-2 Landsat

TM rocks sp. veg. de. veg.+forest rocks 37.8 % 33.7 % 28.5 %

sp. veg. 30.3 % 39. 1 % 30.6 % de. veg.+forest 19.9 % 18.0 % 62.1 % Overall agreement: 43.6 %

Table 8 . 12: Confusion matrix between Landsat and SIR-C/X-SAR SRL-2 classifications of the unglaciated areas.

For the unglaciated areas one can assume that their extent changes only little within one or two years. Thus the classified Landsat TM image from 16 August 1992 can be used as reference to get a less biased estimation of the SAR based classification. Because the Landsat classification provides only two vegetation classes, with forest included in the dense vegetation dass, in the MLC classification results the classes dense vegetation and forest are merged. The confusion matrices with Landsat TM classification as reference are shown in tables 8. 1 1 and 8. 12 . The overall agreement is similar to the previous estimation for SRL-1 data (table 8 .9) and lower for SRL-2 data ( table 8. 10) . Both methods reveal a poor identification of rocks, sparse and dense vegetation because they have similar backscattering properties at microwave frequency, and good classification of forest in the October data.

For the glaciers a pixel by pixel comparison of the SAR classification with a two years older Landsat TM image is not possible. The Landsat classification is used to obtain the total glacier area. In the Alps the annual variation of the glacier termini is of the order of only meters to few tens of meters, thus the glacier boundaries determined from 1992 data can be considered true also for 1994. The areal ratio of the accumulation area Sc to the total glacier area, S, is a key parameter for glaciological applications. Sc can be obtained from the classification presented above. The areal ratio derived from SAR images is approximated by

Sc Sc sar - f'V --'-'-

s- S- Sls where the layover and shadow areas in the SAR image, S15, are subtracted from the total glacier area. The extent of the shadow and layover areas is only a few percent of the image (see table 2.5) and may be considered to be equally distributed on the accumulation and ablation areas.


Field measurements

Sc/S Period HEF KWF VF 92/93 0.48 0.72 0 .32 93/94 0.31 0.33 0 .22

155

Table 8. 13: Areal ratio of the accumulation area and the total glacier area for the years 1992/93 ancl 1993/94 derived from field observation and measurements for the glaciers KWF, HEF, ancl VF.

For the three glaciers in the test site where glaciological research was carried out over these years the Sc/ S ratio derived from the SAR classification is compared with the field measurements.

In figure 8. 19 details of the supervised classifications based on SRL-1 and SRL-2 data for the glaciers Hintereisferner, Kesselwandferner and Vernagtferner are shown. For SRL-1 data the classifier underestimates the accumulation areas for all three glaciers, for SRL-2 data the values of Sc/ S derived from classification are larger than those derived from field measurements.

8.4 Classification of wet snow based on multitem

poral segmentation

In section 7.5 multi-temporal ratios of backscattering (SRL-2 versus SRL-1) for repeat pass clata takes were discussed. It was found that ratios of interest are based on X-band VV polarized and C-band HV polarized backscattering coefficients. Applying thresholds to the sequence of ratio images (DT 14, 46 and 78) the changes of the extent of the wet snow d uring SRL-2 were moni tored ( figures 7 .16 and 7 . 17) . These images were used to classify the accumulation areas at the end of the hydrological year 1993/94 and to compare the results with the field measurements and the maximum likelihood classification. The threshold values chosen for segmentation of accumulation and glacier ice areas depend on the data take because the surface properties changed between the overfiights ( chapter 5) . For small changes, abou t ±1 dB, of the threshold applied to one data take the differences are only small.

For DT 46 the threshold applied to the X-band ratios for classification was -6 dB, for C-band ratios -10 dB. The result of the classification for the three glaciers is shown in figure 8 .20. For Hintereisferner and Vernagtferner the multitemporal classifications are closer to the field measurements than the MLC classification. For Kesselwandferner all three classifications are similar but Sc/ S is higher than the ratio derived from the field measurements because at high altitudes the ablation areas were partly covered by the September snow (chapter 5) , which is classified as accumulation area.


Maximum likelihood classification DT 46 SRL-1 DT 46 SRL-2

Figure 8.19: Accumulation areas (in white) derived from maximum likelihood classifica of DT 46 of 12 April1994 for Hintereisferner, Kesselwandferner, and Vernagtferner (a, and from DT 46 of 3 October 1994 (d,e,f). Black ... glacier areas, dark grey ... sha and layover.


Multitemporal segmentation of SRL-2/SRL-1 ratio images DT 46

oc ßcrHV�-10 dB ßcr0X .E-6 dß yy:::.

157

Figure 8 .20: Accumulation areas (in white) on 3 October 1994 on the glaciers Hintereisferner, Kesselwandferner, and Vernagtferner derived from multitemporal ratios of DT 46 at C-band HV (a,b,c) and X-band VV polarization (d,e,f). Black ... glacier areas, dark grey .. . shadow and layover.

Chapter 8. Target classification in the higl1 alpilw l.l'.'il . .'iil.e

DT ![() SltL-2 Landsat

TM rocks+sp. veg. d<�. vq..;. I fon�st rocks+sp. veg. 92.3 % 7.7% de. veg.+forest 65.7 % 34.3% Overall agreement: 79 %

158

Table 8 .14: Confusion matrix between Landsat and SIR-C/X-SAR SRL-2 classification of the unglaciated areas based on the decision tree algorithm.

8.5 Decision tree classification algorithms

In order to study possible improvements of classifications for single data takes, hierarchical algorithms were investigated.

In figure 8 .21 the decision tree for classifying DT 46 from 3 October 1994 is shown. The classification is performed on geocoded SIR-C/X-SAR data. The glacier and glacier-free areas are separated using the glacier boundaries derived from Landsat data and the layover and shadow zones are eliminated from the area to be classified. The decision rules are established from the average signatures shown in figures 8 . 12 to 8 . 15 and from observations of the multi-channel image. For the glaciers the ratio a�� / a�� was used to discriminate between accumulation and glacier ice areas. On the unglaciated areas relatively high a�� / a�; ratios are observed for the forest compared to the other surface types. After classifying the forest the remaining classes are separated through the magnitude of the correlation coeffi.cient at Cband, IPf,wv l , using an incidence angle dependent threshold. For SRL-2 data the signatures of sparse vegetation and rocks are very similar for all the elements of the feature vector thus these classes are considered inseparable. The dense vegetation class cannot be very well discriminated from sparse vegetation and rocks as observed from its presence at high altitudes in the thematic map (figure 8 .22) . The confusion matrix with the Landsat classification as reference data is given in table 8. 14. The sparse vegetation and rocl<,S classes are merged in the Landsat classification, as well as dense vegetation and f�rest in the SIR-C/X-SAR classification. About 92% of the "rocks and sparse vegetation" class from Landsat TM is correct classified by the SAR data, but for the "dense vegetation and forest" class only 34% agreement is obtained.

Details of the classification on the glaciers are shown in figure 8 .23 . Cornpared with the results of the previous classification procedures (figures 8 . 19 (d)-(f) and 8 .20 (a)- (f)) similar features are detected by the different methods. On the upper part of Hintereisferner towards the peak Weißkugel the snow is misclassified by the a�� / a�� ratio, while in other parts ( e.g. Egg, SE of the glacier) too much snow is detected. On Kesselwandferner (figure 8 .23 (b)) misclassifications are observed in the crevasse zone of the ablation area and on the glacier plateau at altitudes of about 3300 m. The misclassifications at higher altitudes are caused by the frozen top layer of the snow. For Hintereisferner and Vernagtferner the areal ratios Sc/ S are close to those derived from field measurements, for Kesselwandferner, as for the other procedures, Sc/ S is too large.

For SRL-1 data the accumulation area could be separated from the ablation area

Chapter 8. Target classificaUun in tlw high alpine test site

Geocoded SIR-C/X-SAR

---+-/ Segmentation /+---

Glacier I �

� Yes� \,No

'A,_c_c_u_m_u--.1-ati.,.,., o..:::.._

n---, I Glacier area . ice

Unglaciated areas

Layover & shadow mask

15!)

Figure 8.21: Decision tree classification based on DT 46 acquired on 3 October 1994.

through the ratio a�� / a�; . The threshold of -4.7 dB was chosen according to figure 8 .9 . Details of the classification for the three glaciers are shown in figure 8 .24. The agreement with the field derived Sc/ S is better than after MLC classification for Hintereisferner and Vernagtferner, for Kesselwandferner the result is worse. For icefree areas no reasonable separation between the classes could be obtained for SRL-1 with any ratio or correlation coeffi.cient image. Rocks and dense vegetation are too similar in backscattering; confusions are observed also between the snow covered bare surfaces and forest.

Clutptcr 8. Target cl<L.''>Hification in tlw lligh alpine te::;t ::;ite 160

Figure 8.22: Classification result bascd on thc dccision trcc shown in figure 8 .21 for DT

46 from 3 Octobcr 1994. White . . . accumulation areas, bluc . . . glacier ice areas, grey . . . sparse vegetation and rocks, grecn . . . dcnsc vcgctation, ycllow . . . forcst, black . . . layover

and shadow arcas.

Chapter 8. Target classification in the l1igh alpine test sitc

DT 46 SRL-2

Lhv /Chv>2.5 dB

161

Figure 8.23: Accumulation areas (in white) derived from hierarchical classification of DT

46 of 3 October 1994 for Hintereisferner(a) , Kesselwandferner (b) , and Vernagtferner (c). Black ... glacier areas, dark grey ... shadow and layover. Lhv/Chv ratios !arger than 2.5 dB are assigned to the accumulation area.

DT 46 SRL-1

Chv /Xvv>-4.7 dB

Figure 8.24: As figure 8.23 but for DT 46 from 12 April 1994. Chv/Xv v ratios larger than -4.7 dB are assigned to the accumulation area.

Chapter 9

Summary and conclusions

The Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/XSAR) system operated on board the Space Shuttle Endeavour during two missions, in April 1994 (Space Radar Laboratory-1 , SRL-1) and in October 1994 (SRL-2) . SIR-C/X-SAR recorded polarimetric data at two wavelengths (L- and C-band) and at vertical polarization at X-band, at various look angles, over test sites located all over the world. The SIR-C/X-SAR experiment was focused on methods and applications of SAR to derive properties of land surfaces and oceans. In addition, investigations were carried out in the fields of SAR calibration, electromagnetic theory, and validation of inversion algorithms.

This thesis was based on SIR-C/X-SAR data acquired over the hydrology supersite Ötztal , in the Austrian Alps. The aim was to evaluate the potential of multifrequency polarimetric SAR for hydrological and glaciological applications in high alpine regions. In the following the results are summarized and answers are provided for the questions raised in chapter 1 .

Calibration and quality of SIR-C/X-SAR data

Polarimetrie systems require special techniques for calibration. The SIR-C and X-SAR data for this work were provided by JPL and D-PAF /DLR as absolutely calibrated products. The corner reflectors which were deployed on the test site enabled the absolute calibration factors to be checked and image quality parameters to be derived.

The trihedral corner reflectors with 1 .8 m side length were used to check the imbalance between the co-polarized C-band channels . The residual amplitudes of the co-polarized channel imbalances were lower than ±0.5 dB the corresponding residual phases were lower than 2.5° except for two values in DT 46.0 SRL-2. At L-band the cantrast between the corner reflectors and the background was too weak and the procedure could not be applied. The accuracy of the absolute calibration of the Ötztal data was verified at C-band with all corner reflectors. With one exception all the residuals of the absolute calibration constant were lower than the values given in the literature.

The additive noise level in each product was estimated. At C-band the mean value of noise equivalent cross-polarized backscattering coefficient was -30.7 dB, at

162

Chapter 9. Summary and conclusions

L-band -35.2 dB.

163

The impulse response functions of the corner refiectors were used to derive image quality parameters at C-band. For data takes acquired with 10 MHz pulse bandwidth the range and azimuth 3 dB resolutions agree weil with the published values from [Freeman et al . , 1995] except the azimuth resolution of dual-pol data. At 20 MHz only the azimuth resolution confirms the published results the other parameters do not fuily meet the specifications.

Backscattering models for snow covered terrain

Chapter 6 addresses theoretical aspects of the interaction of the microwave radiation with snow covered terrain and gives an overview of the physical background as far as it is needed for the interpretation of backscattering signatures . The effects of various parameters are discussed separately for the surface and the volume scattering term. Finaily the surface and volume scattering effects are combined in a one layer model which is applied for dry and slightly wet snow conditions. The changes in the level of the total backscattering and of each contributing term for specified snow conditions are presented at L-, C- and X-band.

Model calculations are compared with backscattering coefficients derived from SAR data for snow covered and snow free moraines and cultivated meadow, showing partly significant differences. One reason for the differences between simulated and SAR data is the limited IEM range of validity. Natural surfaces do not always fulfil the restrictions applied to the surface parameters , at X- and C-band. For smooth surfaces and at large incidence angles the IEM predicts significant differences between a�h and a�v' but in the SAR data they differ only by about 1 dB. Particularly large are the differences between theoretical values and observed SAR data for L-band a�h.

Backscattering coefficients over accumulation areas from the October fiight compare reasonable weil with theory for slightly wet snow, when the scattering at the air /snow boundary and the snow volume dominate. For the accumulation areas in winter ancl for bare ice the one layer model could not be applied. No suitable model was available to account for the highly complex structure of firn, made up of internal interfaces and a range of different scattering elements, and glacier ice which has a complex surface and internal structure.

Backscattering signature analysis

The ground measurements and the meteorological observations during the Shuttle overfiights are described in chapter 5 . These data are the basis for interpreting backscattering from the various targets. Chapter 7 is dedicated to the analysis of backscattering and correlation coefficients derived from the SAR data for four surface types: accumulation area, glacier ice, vegetation and bare surfaces (mainly rocks and moraines) . Short term and seasonal variations and incidence angle dependences of the elements of the covariance matrix have been determined. A summary of the results derived from the signature analysis is presented below.

Signatures of glacier areas covered with dry snow (April'94 data) :

Chapter 9. Summary and conclusions 164

• all the backscattering coefficients are relatively high ( e.g. , at (}i = 40° , O"�� = -5 dB, aX� = -9 dB on the accumulation area) and the HHVV correlation coefficients are low ( e.g. , at Bi = 40° , I pfhvv I = 0.22 on the accumulation area) . For co-polarized backscattering coefficients the main contributions to scattering are coming from the snow/firn or snow/ice interface and from the volume of the underlying firn and glacier ice . At X-band volume scattering in snow is dominant at incidence angles above 45° . At cross-polarization the contributions from snow, firn andjor ice volume dominate, the contributions from the interfaces can be neglected.

• the difference between C- and X-band co-polarized backscattering coefficients is about 1 dB. This can be explained by volume scattering in a semi-infinite medium with various sizes of scatterers, including scattering at rough internal interfaces.

• in the accumulation area the correlation coefficient at C-band is significantly lower than at L-band (e.g . , at Bi= 40° , IPkhvv l = 0.42) . At L-band the internal interfaces are smoother in relation to the wavelength, therefore the correlation coefficients are high er. On glacier ice differences appear only at incidence angles above 50° .

Signatures of accumulation areas covered with wet snow and of snow

free glacier ice (October'94 data) :

• on the accumulation areas the co- and cross-polarized backscattering coefficients are in most cases about 8 dB lower than in SRL-1 data. The seasonal difference observed for a�� on the accumulation area is larger than 10 dB. At C- and X-band the main contribution to backscattering comes from the air/snow or from the air/ice interface. At L-band on the accumulation area a contribution may come also from the snow/firn interface.

• on the glacier ice the backscattering coefficients are higher in October than in April. This is due to the higher surface roughness and dielectric constant of bare melting ice relative to snow-covered ice and the larger dielectric discontinuity at the air/melting ice compared to the snow/frozen ice interface.

• typically the frequency dependence of the backscattering coefficients is weak (section 7 .3 .2) , with the exception of the accumulation areas, where O"�� is about 2 dB larger than a�� . The reason for this difference is the larger penetration depth at L-band in slightly wet snow.

• the correlation coefficients are higher than in April. In October \pfhvv I is significantly larger than IPkhvv I ( e.g. , at Bi = 40° , IP�wv I = 0.68 and Pkhvv = 0.50 on the accumulation area) . This is due to the larger penetration depth at L-band and the dominance of surface scattering at C-band.

• the signatures during the October experiment are infiuenced by the air temperature drop of 12°C. X-band co-polarized and C-band cross-polarized backscattering coefficients increased within five days by about 4 dB and 5 dB, respectively. These frequencies show high potential for monitaring short term


changes of snow properties. Changes of glacier ice properties infl.uence L-band co-polarized backscattering coefficients.

Signatures of ice-free areas covered with snow (April '94) :

• typical signatures of rock-covered and vegetated areas show the same trend as glacier ice. X- and C-band co-polarized backscattering coefficients are almost identical, a�� is larger than a�� . The main contribution to backscattering comes from the snowjground interface at co-polarization, and from snow volume, including groundjvolume interaction, at cross-polarization. The snow layer is less deep than on glaciers, therefore a�� and a�� are lower than on the glaciers . Lower backscattering coefficients on meadows are at least partly infl.uenced by the wet snow cover observed at low altitudes.

• the correlation coefficients are higher than on the glaciers , showing a weak frequency dependence at incidence angles above 60° .

Signatures of ice- and snow-free areas (October'94) :

• The backscattering coefficients for vegetated and unvegetated (mainly rocks and moraines) areas are up to 4 dB higher in October than in April. For selected sites seasonal variations up to 5 dB are observed at C- and L-band. The main reason is the high permittivity of wet soil relative to frozen soil and, at low elevations, the disappearance of the attenuation due to the partly wet snow layer.

• the correlation coefficients at C-band on the bare surfaces are higher in October (e.g. , at ()i = 40° , J pfhvv l = 0.64 in April and J pfhvv l = 0.76 in October) as well as on vegetation areas at incidence angles above 50° . This points towards a de-correlating effect of the snow volume.

• The HH and HV L-band backscattering coefficients in October are relatively high over forested areas (a�k = -5 dB and a�� = - 10 dB at ()i = 57° ) . In April they differ only slightly from rocks or short vegetation due to the presence of wet snow below the trees.

Classification techniques in alpine terrain

At the beginning of chapter 8 the classification procedures in mountainous terrain based on SAR data are reviewed. The published decision-tree algorithms for SIR-C/X-SAR data, developed for other test sites [Shi and Dozier, 1997] , could not successfully be applied to the Ötztal site. In this work three classification procedures based on multiparameter or multitemporal SAR data have been tested. The aim was the discrimination of accumulation and ablation areas on the glaciers, and of unvegetated surfaces, sparse vegetation, dense vegetation and forests. The incidence angle dependence of the backscattering coefficients was reduced by means of ratios. Depolarization, co-polarization, spectral ratios and correlation coefficients have been calculated in order to select the components of the feature vector. High information


for classifying alpine targets is provided by C- and L-band correlation coefficients, depolarization factors and spectral ratios. Because glacier ice and unvegetated areas have similar backscattering characteristics , optical data are used to clearly separate glaciated and ice-free areas. After this segmentation the classification algorithms are applied sequentially. The discriminant analysis shows good separability between accumulation and ablation areas in both seasons, and between forest and the other classes in October data. Poor separability was observed between the classes on the ice-free areas in April and between bare soil and short vegetation in October data. On the unglaciated areas the performance of the algorithms has been checked by means of a pixel by pixel comparison with a two years older Landsat classification. On the glaciers the areal ratio of the accumulation area to the total glacier area derived from the classifications was calculated. For three glaciers in the test site a comparison with the ratio derived from field observations was made.

First the supervised classification based on maximum likelihood estimation (MLC) was applied. The confusion matrix between MLC and the classification based on Landsat TM shows for ice-free areas an overall agreement of about 43% for both April and October classifications. After merging sparse vegetation with unvegetated areas and forests with dense vegetation the overall agreement in the two classifications becomes 65% and 69%, respectively. For April data the MLC classifier underestimatecl the accumulation areas for all three glaciers while for October data the reverse was observed.

The seconcl algorithm, appliecl only for glacier areas, is segmentation of multitemporal ratio images (October/ April) in wet snow ancl ice areas. Most useful are X-band VV and C-band HV ratios; the thresholds applied are -6 dB ancl - 1 0 dB, respectively, for DT 46. The threshold values depend on the data take because the surface properties changed between the overflights. The result of the segmentation refers to the wet snow extent in October 1994, which was not fully identical with the accumulation area because of snowfall in September. For two glaciers (Hintereisferner ancl Vernagtferner) the areal ratios derived from multitemporal segmentation are closer to the field measurements than those derived from the MLC classification. For the glacier Kesselwandferner the three classification methocls give very similar results, overestimating the extent of the accumulation area because of the more extensive coverage with September snow than on the other glaciers .

The third classification approach consists of hierarchical classifiers based on single data takes. For the October data the decision rule for separating the accumulation and ablation areas on the glaciers uses the spectral ratio between the L- and C-band cross-polarized backscattering coefficients. On the unglaciated areas forest is separated from the other classes due to the high values of the L-band HV versus the X-band VV ratio. Dense vegetation is classified through the magnitude of the HHVV correlation coefficient at C-band. Sparse vegetation and unvegetated surfaces could not be separated in the single term data because of the similarity in the surface roughness properties. The overall accuracy between the hierarchical and Landsat classifications was 79% for the glacier-free areas. For this comparison unvegetated areas and sparse vegetation were merged, as well as clense vegetation and forests. On the glaciers Hintereisferner and Vernagtferner the results correspond well with the field measurements and with the segmentation basecl on the C-bancl HV ratio. For Kesselwandferner, although the snow extent is the smallest among all the clas-


sifications, it is much larger than the field derived accumulation area. In April the accumulation and ablation areas were separated through the C-band HV versus Xband VV ratio. The extent of the accumulation area is in very good agreement with the field measurements for Hintereisferner and Vernagtferner, for Kesselwandferner the classified area is too small. For ice-free areas no reasonable separation between the classes could be obtained in April. The reasons are the complete snow-coverage and the change of snow conditions (wetness, layering) with altitude and exposure.

The characteristics of surface classes discussed in this thesis, as well as the parameters used for classifications are specific for the high alpine test site Ötztal at the time of data acquisition. Various natural conditions in Ötztal or at other sites may cause changes of target characteristics. This results in different behavior of the polarimetric measures used for classification. For classification of snow and ice areas on the glaciers , backscattering ratios proviele better results than more complex classification algorithms.

Regarding the comparison with Landsat TM in the ice-free zones, it has to be

pointed md that the Landsat classification is primarily sensitive to photosynthetic

activity, whereas SAR is sensitive to soil surface roughness, dielectric properlies and

vegetation structure. Therefore no perfect match between the two classifications can

be expected.

Information content of multiparameter SAR versus

single channel SAR

Microwave radiation is particularly sensitive to scatterers comparable in size to the radar wavelength. Therefore single frequency data can characterize fewer surface types than multi-frequency data. The studies in chapters 7 and 8 showed that the availability of three frequencies expands the possibilities to characterize and classify surface types in alpine terrain. On the glaciers , X- and C-band data are suited to characterize and classify snow, while L-band revealed seasonal and short term variations of the glacier ice properties. On the unglaciated surfaces L-band data enabled the classification of the coniferous forests in snow free conditions.

Single polarization SARs presently in orbit operate at VV or HH polarizations, whereas a polarimetric sensor measures four linear polarizations HH, VV, HV and VH. For other polarizations the backscattering coefficients are not measured but calculated through polarization synthesis. Fully polarimetric data at C-band can be used to derive snow wetness if the snow surface is smooth [Shi and Dozier, 1995] . In terrain with complicated topography the incidence angle dependence can be recluced for classifications by means of ratios between backscattering coeffi.cients and other polarimetric parameters . Single frequency polarimetric data offer the following parameters : the depolarization ratio, the co-polarization ratio, the magnitude and phase of the HHVV correlation coeffi.cient . In the test site only the first and third were useful for classification.

Single channel SAR data can be used for classification only if multitemporal data are available or if the incidence angle effects are eliminated by means of topographic clata. In alpine terrain wet snow can be classified by applying one threshold to the multitemporal ratio image [Nagler, 1996] . This classifier is based on the large


clifference between backscattering of wet snow versus dry snow or snow free terrain. The dynamic range of the ratio image is only a few dB, hence the limitation to one or two thresholds.

Multifrequency polarimetric data allow the formation of several ratios which enable separation of diverse targets. Spectral ratios formed with cross-polarized backscattering coefficients and the magnitude of the HHVV correlation coefficients are key parameters in establishing the classification rules of the hierarchical classifiers. However, all of these parameters show some kind of angular dependence, though less pronounced than for single channel backscattering coeffi.cients.

Applications of multiparameter SAR data •

In mountainous regions

Initially, the goal of the SIR-C/X-SAR experiment in Ötztal was focused on snow hydrology. In 1994, however, the melting period lay between the SRL missions. Therefore the emphasis of the investigations moved towards glaciological applications of radar polarimetry. Hydrological applications would require a langer time series of data or at least a mission cluring the main melt period. Conclusions concerning the applications of multiparameter SAR data for glaciers and ice-free alpine surfaces are outlined below.

In glaciology the extent of the accumulation and ablation areas is important. When the glaciers are covered with dry snow the accumulation areas can be separated due to high a-�� relative to the other surface classes. When the snow was wet the same backscattering coefficient was used to classify the accumulation areas because of its low values relative to bare ice. Multitemporal ratios between other backscattering coefficients at C- and X-band can also proviele wet snow maps. The interpretation regarding the accumulation areas based on the signature of wet snow is correct only if the snow line and equilibrium line coincide.

For applications in hydrology the extent of snow covered areas is of relevance. For glaciers , short term changes in the extent of the wet snow can be monitared at C- and X-band, as was shown in section 7.5 . The detection of dry snow by means of SAR is still a point of discussion. The signature analysis and classification revealed that the dry firn area on the glaciers can clearly be detected. Dry snow in the unglaciated areas could not be distinguished from snow-free surfaces. The main reason is probably the high roughness of these surfaces in the test site so that the signal from the snow / ground interface is dominant. The strong infiuence of the roughness on a-0 values for both vegetated and unvegetated areas may also explain the lack of separation between them and the small sensitivity of SAR data for detecting changes in their properties.

We may conclude that multiparameter SAR data are not suitable for mapping surface classes with similar roughness if the infiuence of this parameter in backscattering is dominant. Therefore, for the detection of vegetation above the tree line and for estimation of glacier extent optical data are recommended. On the other hand, surface classes with volume or multiple scattering contributions, like snow covers and forests , can be weil characterized with SAR data.

Chapter 9. Summary and conclw;ions 169

Inversion algorithms for thc rctricval of snow parameters from polarimetric data have been tested. The algorithm of [Shi and Dozier, 1995] for the estimation of the permittivity at the top layer of wet snow covers from polarimetric SAR was applied in thc Ötztal site using SRL-2 SIR-C clata. The trend in the short term changes of snow wetness confirmed the field observations, but the derived values of snow wetness are strongly eiependent on the method applied to remove the additive noise.

Outlook

Plans for future spaceborne polarimetric sensors are not finalized. NASA is considering the launch of an 1-band polarimetric SAR (LightSAR) with multiple resolutions and swath imaging capabilities. In Europe feasibility studies are going on for a high resolution, dual frequency SAR and for a dual frequency, dual polarization SAR with low resolution for global climatic studies (CLIMACS) . Presently polarimetric SARs are available only on aircraft, affering interesting opportunities for environmental research at local to regional scales.

The ENVISAT mission of ESA (launch 1999) will proviele new remote sensing clata for study and monitaring the earth's environment and atmosphere in a synergistic way. In particular, for investigations of land surface properties, the Advanced SAR (ASAR) and Medium Resolution Imaging Spectrometer (MERIS) of ENVISAT are of interest . ASAR is a; active phased array antenna system operating at C-band and two alternating polarizations, HH and VV, and HV as an experimental mode. MERIS is a programmable spectrometer measuring solar refl.ected radiation from the earth's surface and clouds in the visible and near infrared range. In this thesis the possibilities but also the limits of multiparameter SAR data in the characterization of natural surfaces in high alpine terrain were demonstrated. As shown in the classification study, the complementarity of optical and SAR sensors will significantly cnlarge the applicability of remote sensing data in mountainous regions.

Appendix A

lncidence angle dependence of

backscattering derived from DT 14, 18 and 78

In section 7.3 the dependence of co- and cross-polarization backscattering coefficients on the incidence angle is discussed in detail for DT 46. The angular patterns derived from DT 14, 18 and 78 SR1-1 and SR1-2 are shown below (figures A .1 to A.5) .

Short term variation of the backscattering coefficients during SRL-2 are analyzed at X-band VV and C-band HV in section 7.5 . For C- and 1-band co-polarization and 1-band cross-polarization the changes occurred in the same periocl are illustrated in figures A.6 to A.8 .

170

Appendix A. Incidence angle dependence of backscattering derived from DT 14, 18 and 78171

5

0

-5 DJ �

0 -10 b

-15

-20

-25

5

0

-5

DJ �

0 -10 b

-15

-20

-25

ACCUMULATION AREA

SRL-1 DT 1 4

~ �

W X-band(+) W C-band(6) HV C-band(<>) w L-band(e) HV L-band(O)

20 40 60

ei [deg)


~ � �

20 40 60

ei [deg]

80

80

5

0

-5

iD �

0 -10 b

-15

-20

-25

5

0

-5

iD �

0 -10 b

-15

-20

-25


~ � �

20 40 60

ei [deg) 80


~ � 20 40 60

ei [deg] 80

Figure A.1: Incidence angle dependence of co- and cross-polarized backscattering coefficients at X- , C- and L-band for different types of natural surfaces derived from SRL-1 DT 14, multilooked SSC data.

Appendix A. Incidence angle dependence of backscattering derived from DT 14, 18 and 78172

5

0

-5 10 �

0 -1 0 b

-15

-20

-25

5

0

-5 10 �

0 -10 b

-1 5

·20

-25

ACCUMULATION AREA

SRL-1 DT 78 W X-band(+)

20

20

40

HH C-band(L'I) HH L-band(e) HV C-band(<>) HV L-band (0)

60 ei [deg]

80


40 60 ei [deg)

80

5

0

-5 10 �

0 -10 b

·1 5

-20

-25

5

0

-5 10 �

0 -10 b

-15

-20

-25


20 40 60 ei [deg]

80


� 20 40 60

ei [deg) 80

Figure A.2 : Incidence angle dependence of co- and cross-polarized backscattering coeffi

cients at X- , C- and L-band for different types of natural surfaces derived from SRL-1 DT 78 , multilooked SSC data.

Appendix A . Incidence angle dependence of backscattering derived from DT 1 4, 18 and 78173

5

0

·5 ii5" :Q.

0 -10 b

-15

-20

·25

5

0

·5 ii5" :Q.

0 ·10 b

·1 5

·20

-25

ACCUMULATION AREA

SRL-2 DT 1 4 W X-band(+) W C-band(6) W L-band(e) HV C-band(O)

~ HV L-band (0)

~ 20 40 60

ei[deg] ROCKS

~ �

20 40 60 ei [deg]

80

80

5

0

·5 ii5" :Q.

0 · 10 b

· 1 5

·20

·25

5

0

·5 ii5" :Q.

0 ·10 b

· 1 5

-20

-25

GLACIER ICE

' �

20 40 60 ei [deg]

VEGETATION

~ �

20 40 60 ei [deg]

80

80

Figure A.3 : Incidence angle dependence of co- and cross-polarized backscattering coefficients at X- , C- and L-band for different types of natural surfaces derived from SRL-2 DT 14, multilooked SSC data.

Appendix A. Incidence angle dependence of backscattering derived from DT 1 4, 1 8 and 78174

5

0

·5 10 :!:!.

0 -10 b

-1 5

-20

-25

5

0

-5 10 :!:!.

0 -10 b

-15

-20

-25

ACCUMULATION AREA

SRL-2 DT 1 8

20

20

40

W X-band(+) W C-band(.6) W L-band(e) HV C-band(<>) HV L-band (0)

60 ei [deg]

ROCKS

40 60 ei (deg)

5

0

·5 10 :!:!.

0 - 10 b

· 1 5

·20

·25 80

5

0

-5 10 :!:!.

0 -10 b

· 1 5

-20

·25 80

20

20

GLACIER ICE

40 60 ei [deg]

VEGETATION

40 60 ei [deg]

80

80

Figure A.4 : Incidence angle dependence of co- and cross-polarized backscattering coefficients at X- , C- and 1-band for different types of natural surfaces derived from SRL-2 DT 18 , multilooked SSC data.

Appendix A. Incidence angle depencleuce of backscattering derived from DT 14, 1 8 and 78175

5

0

·5 äi' :!:!.

0 -10 b

-15

-20

-25

5

0

-5 äi' :!:!.

0 -10 b

-15

-20

-25

ACCUMULATION AREA

SRL-2 DT 78 W X-band(+)

20

20

HH C-band(6) HH L-band(e) HV C-band(<>) HV L-band (0)

40 60 80 ei [degl

ROCKS

40 60 ei (deg]

80

5

0

-5 äi' :!:!.

0 -10 b

- 1 5

-20

-25

5

0

-5 äi' :!:!.

0 -10 b

- 15

-20

-25

20

20

GLACIER ICE

40 60 ei [deg]

VEGETATION

40 60 ei [deg]

80

80

Figure A .5 : Incidence angle dependence of co- and cross-polarized backscattering coeffi

cients at X-, C- and 1-band for different types of natural surfaces derived from SRL-2 DT 78 , multilooked SSC data.

Appendix A. Incidence angle dependence of backscattering derived from DT 14, 18 and 781 76

5

0

-5 iii' :!:!.

0 :E -1 0 b

-1 5

-20

-25

5

0

-5 iii' :!:!.

0 .C -10 t)_c

-15

-20

-25

ACCUMULATION AREA

SRL-2 C-band

20 40 60 ei [deg]

ROCKS

20 40 60 ei [deg]

DT 1 4 (D) DT 1 8 (+) DT 46 (.t.) DT 78 (e)

80

80

5

0

-5 iii' :!:!.

0 ::E-10 b

-1 5

-20

·25

5

0

-5 iii' :!:!.

0 .c -10 b..c:

·1 5

-20

-25

20

20

GLACIER ICE

40 60 ei [deg]

VEGETATION

40 60 ei [deg]

80

80

Figure A.G : Incidence angle dependence of the co-polarized backscattering coefficient at C-band derived from DT 14, 18 , 46 and 78 SRL-2, multilooked SSC data.

Appendix A. Incidence angle dependence of backscattering derived from DT 14, 1 8 and 781 77

ACCUMULATION AREA GLACIER ICE 5 5

SRL-2 L-band DT 1 4 (D) 0 DT 1 B (+) 0

DT 46 (&) DT 78 (e)

·5 -5 CiJ CiJ :!:!. :!:!.

0 :E -10 b

0 :E -10 b

-1 5 -1 5

-20 -20

-25 -25 20 40 60 80 20 40 60 80

ei [deg] ei [deg] ROCKS VEGETATION

5 5

0 � 0

-5 -5 CiJ CiJ :!:!. :!:!.

0 -'= -10 - 0 :§ -10 t:l-'= t:l

-1 5 -15

-20 -20

-25 -25 20 40 60 80 20 40 60 80

ei (deg) ei [deg]

Figure A.7 : Incidence angle dependence of the co-polarized backscattering coefficient at 1-band derived from DT 14, 18 , 46 and 78 SRL-2, multilooked SSC data.

Appendix A . Incidence angle dependence of backscattering derived from DT 14, 18 and 78178

ACCUMULATION AREA -5 ,_----------------------,

-10

0 ..E; -1 5 b

-20

SRL-2 L-band

20 40 60 ei [deg]

ROCKS

DT 14 (O) DT 1 8 (+) DT 46 (.&) DT 78 (e)

80

-5,-----------------------,

-10

-20

20 40 60 ei [deg)

80

GLACIER ICE -5,_----------------------,

-10

0 > -15 b..c::

-20

20 40 60 ei [deg)

VEGETATION

80

-5 ,-----------------------,

äi' !!.

-1 0

0 ..E; -15 b

-20

20 40 60 ei [deg)

80

Figure A.8 : Incidence angle dependence of the cross-polarized backscattering coefficient at 1-bancl clerived from DT 14, 18 , 46 and 78 SRL-2, multilooked SSC data.

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Curriculum Vitae

Dana-Marie Floricioiu

Born on 18 August 1 966 in Ttrgu-Mure:;; , Romania Current Position Research Associate at the Institute of Meteorology and Geo

physics, University of Innsbruck

Education

June 1984

1 984/88

School leaving exam (Bacalaureat) in Bra:;;ov, Romania.

Physics study at the University 1 1Babe:;;-Bolyai 11 Cluj-Napoca, Romania. Diplama thesis on properties at very high frequencies of some oxidic compounds (Proprieta�i la frecven�e foarte inalte ale unor compu§i oxidici).

Employment and Scientific Experience

1 988/90

1 99 1/92

from October 1 992 to July 1 993

1 993/94

April 1 994 and October 1 994

1 994/96

from SS 1 994 to WS 1 995/96

since 1 N ov 1 996

Teacher of general physics at high school.

Teaching Assistant at the Physics Department of the Technical University of Bucharest, Romania.

Schalarship of the Austrian Foreign Exchange Office ( ÖA D) ; research work on theoretical modeHing of radar backscattering from natural surfaces and comparison with ERS-1 data at the Institute of Meteorology and Geophysics, University of Innsbruck.

Several work contracts on analysis and target classification by

means of polarimetric AIRSAR data and preparation of the SIRC/X-SAR experiment.

Participation at the two SIR-C/X-SAR experiments: real time analysis of multiparameter SAR data of the test site Ötztal.

Several work contracts on the topic polarimetric signature analysis, target discrimination and classification by means of multifrequency polarimetric SIR-C/X-SAR data of Ötztal.

Teaching Assistant for Strahlungstheorie, Digitale Bilddatenverarbeitung and for Grundlagen der Fernerkundung at the Institute of

Meteorology and Geophysics, University of Innsbruck. Research Associate at the Institute of Meteorology and Geo

physics, University of Innsbruck.

Research visits and field campaigns

SeptiOct 1991

November 1991

Dec 1992 to lVlarch 1993

July 1994

Aug 1995

April/May 1996

Research on elaborating computer models for coherent radiation propagation at the Mathematics Department of the University of Oviedo, Spain. Academic exchange program with the Institute of Meteorology and Geophysics, University of Innsbruck, studying polarimetric SAR signatures of natural surfaces .

Several field campaigns at the test site InnsbruckiLeutasch during ERS-1 passes.

Research about the development of interactive and automatic data

analysis techniques for SAR applications in hydrology at the Department of Applied and Computational Mathematics, University of Sheffield.

Field campaign at the test site Ötztal during ERS-1 I ERS-2 Tandem Mission. Field campaigns at the test site ZillertaliSchlegeis during ERS I Radarsat overftights.

Documents

Polarimetrie Signatures and Classification of Alpine ... · fore, a polarimetric sensor operating in various frequency bands provides information about the imaged target over a wide