Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet
The backscatter coefficients of Synthetic Aperture Radar (SAR) images that observe the Greenland Ice Sheet (GrIS) are incidence angle dependent, which impedes subsequent applications, such as monitoring its surface melting. Therefore, backscatter intensities with varying incidence angles should be n...
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ftmdpi:oai:mdpi.com:/2072-4292/14/21/5534/ 2023-08-20T04:06:51+02:00 Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet Xiao Chen Gang Li Zhuoqi Chen Qi Ju Xiao Cheng agris 2022-11-02 application/pdf https://doi.org/10.3390/rs14215534 EN eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs14215534 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 21; Pages: 5534 backscatter coefficient normalization greenland ice sheet incidence angle sentinel-1 extra wide (EW) mode Text 2022 ftmdpi https://doi.org/10.3390/rs14215534 2023-08-01T07:10:01Z The backscatter coefficients of Synthetic Aperture Radar (SAR) images that observe the Greenland Ice Sheet (GrIS) are incidence angle dependent, which impedes subsequent applications, such as monitoring its surface melting. Therefore, backscatter intensities with varying incidence angles should be normalized. This study proposes an incidence angle normalization method for dual-polarized Sentinel-1 images for GrIS. A multiple linear regression model is trained using the ratio between the backscatter coefficient differences and the incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include the geographical position and elevation. The precision evaluation to the ascending and descending images suggests better normalization results than the widely used cosine-square correction method for horizontal transmit and horizontal receive (HH) images and a slight improvement for horizontal transmit and vertical receive (HV) images. Another dataset of GrIS Sentinel-1 mosaics in four 6-day repeating periods in 2020 is also tested to evaluate the proposed method and yields similar results. For HH images, the proposed method performs better than the cosine-square method, reducing 0.34 dB RMSE on average. The overall accuracy of our proposed method is 0.77 and 0.75 dB for HH and HV images, respectively. The proposed incidence angle normalization method can benefit the application of wide-swath SAR images to the study of large-scale and long-period observation on GrIS. Text Greenland Ice Sheet MDPI Open Access Publishing Greenland Remote Sensing 14 21 5534 |
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MDPI Open Access Publishing |
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English |
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backscatter coefficient normalization greenland ice sheet incidence angle sentinel-1 extra wide (EW) mode |
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backscatter coefficient normalization greenland ice sheet incidence angle sentinel-1 extra wide (EW) mode Xiao Chen Gang Li Zhuoqi Chen Qi Ju Xiao Cheng Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet |
topic_facet |
backscatter coefficient normalization greenland ice sheet incidence angle sentinel-1 extra wide (EW) mode |
description |
The backscatter coefficients of Synthetic Aperture Radar (SAR) images that observe the Greenland Ice Sheet (GrIS) are incidence angle dependent, which impedes subsequent applications, such as monitoring its surface melting. Therefore, backscatter intensities with varying incidence angles should be normalized. This study proposes an incidence angle normalization method for dual-polarized Sentinel-1 images for GrIS. A multiple linear regression model is trained using the ratio between the backscatter coefficient differences and the incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include the geographical position and elevation. The precision evaluation to the ascending and descending images suggests better normalization results than the widely used cosine-square correction method for horizontal transmit and horizontal receive (HH) images and a slight improvement for horizontal transmit and vertical receive (HV) images. Another dataset of GrIS Sentinel-1 mosaics in four 6-day repeating periods in 2020 is also tested to evaluate the proposed method and yields similar results. For HH images, the proposed method performs better than the cosine-square method, reducing 0.34 dB RMSE on average. The overall accuracy of our proposed method is 0.77 and 0.75 dB for HH and HV images, respectively. The proposed incidence angle normalization method can benefit the application of wide-swath SAR images to the study of large-scale and long-period observation on GrIS. |
format |
Text |
author |
Xiao Chen Gang Li Zhuoqi Chen Qi Ju Xiao Cheng |
author_facet |
Xiao Chen Gang Li Zhuoqi Chen Qi Ju Xiao Cheng |
author_sort |
Xiao Chen |
title |
Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet |
title_short |
Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet |
title_full |
Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet |
title_fullStr |
Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet |
title_full_unstemmed |
Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet |
title_sort |
incidence angle normalization of dual-polarized sentinel-1 backscatter data on greenland ice sheet |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14215534 |
op_coverage |
agris |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Remote Sensing; Volume 14; Issue 21; Pages: 5534 |
op_relation |
Environmental Remote Sensing https://dx.doi.org/10.3390/rs14215534 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14215534 |
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Remote Sensing |
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14 |
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21 |
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