Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments

Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in hetero...

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Published in:Remote Sensing
Main Authors: Barbara Widhalm, Annett Bartsch, Robert Goler
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2018
Subjects:
Q
Online Access:https://doi.org/10.3390/rs10040551
https://doaj.org/article/f410d765b9204c83a39f6e4f6c0f3140
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spelling ftdoajarticles:oai:doaj.org/article:f410d765b9204c83a39f6e4f6c0f3140 2023-05-15T18:40:13+02:00 Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments Barbara Widhalm Annett Bartsch Robert Goler 2018-04-01T00:00:00Z https://doi.org/10.3390/rs10040551 https://doaj.org/article/f410d765b9204c83a39f6e4f6c0f3140 EN eng MDPI AG http://www.mdpi.com/2072-4292/10/4/551 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10040551 https://doaj.org/article/f410d765b9204c83a39f6e4f6c0f3140 Remote Sensing, Vol 10, Iss 4, p 551 (2018) synthetic aperture radar normalization tundra frozen soil Science Q article 2018 ftdoajarticles https://doi.org/10.3390/rs10040551 2022-12-31T16:33:27Z Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas. Article in Journal/Newspaper Tundra Directory of Open Access Journals: DOAJ Articles Remote Sensing 10 4 551
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic synthetic aperture radar
normalization
tundra
frozen soil
Science
Q
spellingShingle synthetic aperture radar
normalization
tundra
frozen soil
Science
Q
Barbara Widhalm
Annett Bartsch
Robert Goler
Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
topic_facet synthetic aperture radar
normalization
tundra
frozen soil
Science
Q
description Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas.
format Article in Journal/Newspaper
author Barbara Widhalm
Annett Bartsch
Robert Goler
author_facet Barbara Widhalm
Annett Bartsch
Robert Goler
author_sort Barbara Widhalm
title Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
title_short Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
title_full Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
title_fullStr Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
title_full_unstemmed Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments
title_sort simplified normalization of c-band synthetic aperture radar data for terrestrial applications in high latitude environments
publisher MDPI AG
publishDate 2018
url https://doi.org/10.3390/rs10040551
https://doaj.org/article/f410d765b9204c83a39f6e4f6c0f3140
genre Tundra
genre_facet Tundra
op_source Remote Sensing, Vol 10, Iss 4, p 551 (2018)
op_relation http://www.mdpi.com/2072-4292/10/4/551
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs10040551
https://doaj.org/article/f410d765b9204c83a39f6e4f6c0f3140
op_doi https://doi.org/10.3390/rs10040551
container_title Remote Sensing
container_volume 10
container_issue 4
container_start_page 551
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