Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization

Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Mohammed Dabboor, Benoit Montpetit, Stephen Howell
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2018
Subjects:
SAR
Online Access:https://doi.org/10.3390/rs10040594
id ftmdpi:oai:mdpi.com:/2072-4292/10/4/594/
record_format openpolar
spelling ftmdpi:oai:mdpi.com:/2072-4292/10/4/594/ 2023-08-20T04:09:42+02:00 Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization Mohammed Dabboor Benoit Montpetit Stephen Howell agris 2018-04-12 application/pdf https://doi.org/10.3390/rs10040594 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs10040594 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 4; Pages: 594 SAR compact polarimetry sea ice classification Text 2018 ftmdpi https://doi.org/10.3390/rs10040594 2023-07-31T21:28:13Z Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the RCM associated with a higher noise floor (Noise Equivalent Sigma Zero of −19 dB), which can prove challenging for sea ice monitoring. Twenty three Compact Polarimetric (CP) parameters were derived and analyzed for the discrimination between first year ice (FYI) and multiyear ice (MYI). The results of the RCM HR mode are compared with those previously obtained for other RCM SAR modes for possible CP consistency parameters in sea ice classification under different noise floors, spatial resolutions, and radar incidence angles. Finally, effective CP parameters were identified and used for the classification of FYI and MYI using the Random Forest (RF) classification algorithm. This study indicates that, despite the expected high noise floor of the RCM HR mode, CP SAR data from this mode are promising for the classification of FYI and MYI in dry ice winter conditions. The overall classification accuracies of CP SAR data over two test sites (96.13% and 96.84%) were found to be comparable to the accuracies obtained using Full Polarimetric (FP) SAR data (98.99% and 99.20%). Text Sea ice MDPI Open Access Publishing Remote Sensing 10 4 594
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic SAR
compact polarimetry
sea ice
classification
spellingShingle SAR
compact polarimetry
sea ice
classification
Mohammed Dabboor
Benoit Montpetit
Stephen Howell
Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
topic_facet SAR
compact polarimetry
sea ice
classification
description Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the RCM associated with a higher noise floor (Noise Equivalent Sigma Zero of −19 dB), which can prove challenging for sea ice monitoring. Twenty three Compact Polarimetric (CP) parameters were derived and analyzed for the discrimination between first year ice (FYI) and multiyear ice (MYI). The results of the RCM HR mode are compared with those previously obtained for other RCM SAR modes for possible CP consistency parameters in sea ice classification under different noise floors, spatial resolutions, and radar incidence angles. Finally, effective CP parameters were identified and used for the classification of FYI and MYI using the Random Forest (RF) classification algorithm. This study indicates that, despite the expected high noise floor of the RCM HR mode, CP SAR data from this mode are promising for the classification of FYI and MYI in dry ice winter conditions. The overall classification accuracies of CP SAR data over two test sites (96.13% and 96.84%) were found to be comparable to the accuracies obtained using Full Polarimetric (FP) SAR data (98.99% and 99.20%).
format Text
author Mohammed Dabboor
Benoit Montpetit
Stephen Howell
author_facet Mohammed Dabboor
Benoit Montpetit
Stephen Howell
author_sort Mohammed Dabboor
title Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
title_short Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
title_full Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
title_fullStr Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
title_full_unstemmed Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
title_sort assessment of the high resolution sar mode of the radarsat constellation mission for first year ice and multiyear ice characterization
publisher Multidisciplinary Digital Publishing Institute
publishDate 2018
url https://doi.org/10.3390/rs10040594
op_coverage agris
genre Sea ice
genre_facet Sea ice
op_source Remote Sensing; Volume 10; Issue 4; Pages: 594
op_relation Ocean Remote Sensing
https://dx.doi.org/10.3390/rs10040594
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs10040594
container_title Remote Sensing
container_volume 10
container_issue 4
container_start_page 594
_version_ 1774723330689990656