Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness

A new empirical algorithm CWAVE_S1-IW for estimation of significant wave height Hs including swell and wind sea from C-band satellite-borne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swath Mode (IW) imagery. The algorithm was implemented into the...

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Published in:International Journal of Remote Sensing
Main Authors: Pleskachevsky, Andrey, Jacobsen, Sven, Tings, Björn, Schwarz, Egbert
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis 2019
Subjects:
Online Access:https://elib.dlr.de/124579/
https://elib.dlr.de/124579/1/Estimation%20of%20sea%20state%20from%20Sentinel%201%20Synthetic%20aperture%20radar%20imagery%20for%20maritime%20situation%20awareness.pdf
https://doi.org/10.1080/01431161.2018.1558377
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author Pleskachevsky, Andrey
Jacobsen, Sven
Tings, Björn
Schwarz, Egbert
author_facet Pleskachevsky, Andrey
Jacobsen, Sven
Tings, Björn
Schwarz, Egbert
author_sort Pleskachevsky, Andrey
collection Unknown
container_issue 11
container_start_page 4104
container_title International Journal of Remote Sensing
container_volume 40
description A new empirical algorithm CWAVE_S1-IW for estimation of significant wave height Hs including swell and wind sea from C-band satellite-borne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swath Mode (IW) imagery. The algorithm was implemented into the Sea Sate Processor (SSP) for fully automatic processing for near real time (NRT) services and allow the estimation of wave fields of thousands of kilometres in the flight direction and 250 km swath from S1 IW scenes consisting of a sequence of individual images. The priority of CWAVE_S1-IW development was an automatic, fast and robust raster processing independent from wave patterns, applicable even when only clutter is visible in the SAR images. The algorithm is based on the spectral analysis of subscenes in wavenumber space. The empirical function allows direct Hs estimation from image spectra without first converting them into wave spectra and uses integrated image spectra parameters as well as estimated local wind information. A texture analysis based on Grey Level Co-occurrence Matrices (GLCM) is also applied. In this way, also the parameters of short waves can be estimated, which are not visible in S1 IW images and are only represented by clutter. The algorithm was tuned worldwide using in-situ collocated measurements of 92 buoys with more than 2500 acquisitions. The validated SSP allows automatic processing of worldwide S1 IW images in VV or HH polarization, including Atlantic storms, cyclones and huge storms in the Gulf of Alaska with an root mean square error RMSE of 80 cm for Hs. For the closed seas like the North Sea, Baltic Seas and Black Sea the accuracy is higher with an RMSE = 55 cm. The algorithm is integrated into a demonstration service, used for further validation at the DLR ground station in Neustrelitz. The NRT processing has been tested by supporting a research ship cruise in the Antarctic Sea.
format Article in Journal/Newspaper
genre Antarc*
Antarctic
Alaska
genre_facet Antarc*
Antarctic
Alaska
geographic Antarctic
Gulf of Alaska
The Antarctic
geographic_facet Antarctic
Gulf of Alaska
The Antarctic
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op_doi https://doi.org/10.1080/01431161.2018.1558377
op_relation https://elib.dlr.de/124579/1/Estimation%20of%20sea%20state%20from%20Sentinel%201%20Synthetic%20aperture%20radar%20imagery%20for%20maritime%20situation%20awareness.pdf
Pleskachevsky, Andrey und Jacobsen, Sven und Tings, Björn und Schwarz, Egbert (2019) Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness. International Journal of Remote Sensing, 40 (11), Seiten 4104-4142. Taylor & Francis. doi:10.1080/01431161.2018.1558377 <https://doi.org/10.1080/01431161.2018.1558377>. ISSN 0143-1161.
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spelling ftdlr:oai:elib.dlr.de:124579 2025-06-15T14:09:38+00:00 Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness Pleskachevsky, Andrey Jacobsen, Sven Tings, Björn Schwarz, Egbert 2019-01-17 application/pdf https://elib.dlr.de/124579/ https://elib.dlr.de/124579/1/Estimation%20of%20sea%20state%20from%20Sentinel%201%20Synthetic%20aperture%20radar%20imagery%20for%20maritime%20situation%20awareness.pdf https://doi.org/10.1080/01431161.2018.1558377 en eng Taylor & Francis https://elib.dlr.de/124579/1/Estimation%20of%20sea%20state%20from%20Sentinel%201%20Synthetic%20aperture%20radar%20imagery%20for%20maritime%20situation%20awareness.pdf Pleskachevsky, Andrey und Jacobsen, Sven und Tings, Björn und Schwarz, Egbert (2019) Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness. International Journal of Remote Sensing, 40 (11), Seiten 4104-4142. Taylor & Francis. doi:10.1080/01431161.2018.1558377 <https://doi.org/10.1080/01431161.2018.1558377>. ISSN 0143-1161. cc_by_nc_nd SAR-Signalverarbeitung Nationales Bodensegment Zeitschriftenbeitrag PeerReviewed 2019 ftdlr https://doi.org/10.1080/01431161.2018.1558377 2025-06-04T04:58:07Z A new empirical algorithm CWAVE_S1-IW for estimation of significant wave height Hs including swell and wind sea from C-band satellite-borne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swath Mode (IW) imagery. The algorithm was implemented into the Sea Sate Processor (SSP) for fully automatic processing for near real time (NRT) services and allow the estimation of wave fields of thousands of kilometres in the flight direction and 250 km swath from S1 IW scenes consisting of a sequence of individual images. The priority of CWAVE_S1-IW development was an automatic, fast and robust raster processing independent from wave patterns, applicable even when only clutter is visible in the SAR images. The algorithm is based on the spectral analysis of subscenes in wavenumber space. The empirical function allows direct Hs estimation from image spectra without first converting them into wave spectra and uses integrated image spectra parameters as well as estimated local wind information. A texture analysis based on Grey Level Co-occurrence Matrices (GLCM) is also applied. In this way, also the parameters of short waves can be estimated, which are not visible in S1 IW images and are only represented by clutter. The algorithm was tuned worldwide using in-situ collocated measurements of 92 buoys with more than 2500 acquisitions. The validated SSP allows automatic processing of worldwide S1 IW images in VV or HH polarization, including Atlantic storms, cyclones and huge storms in the Gulf of Alaska with an root mean square error RMSE of 80 cm for Hs. For the closed seas like the North Sea, Baltic Seas and Black Sea the accuracy is higher with an RMSE = 55 cm. The algorithm is integrated into a demonstration service, used for further validation at the DLR ground station in Neustrelitz. The NRT processing has been tested by supporting a research ship cruise in the Antarctic Sea. Article in Journal/Newspaper Antarc* Antarctic Alaska Unknown Antarctic Gulf of Alaska The Antarctic International Journal of Remote Sensing 40 11 4104 4142
spellingShingle SAR-Signalverarbeitung
Nationales Bodensegment
Pleskachevsky, Andrey
Jacobsen, Sven
Tings, Björn
Schwarz, Egbert
Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness
title Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness
title_full Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness
title_fullStr Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness
title_full_unstemmed Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness
title_short Estimation of Sea State from Sentinel-1 Synthetic Aperture Radar Imagery for Maritime Situation Awareness
title_sort estimation of sea state from sentinel-1 synthetic aperture radar imagery for maritime situation awareness
topic SAR-Signalverarbeitung
Nationales Bodensegment
topic_facet SAR-Signalverarbeitung
Nationales Bodensegment
url https://elib.dlr.de/124579/
https://elib.dlr.de/124579/1/Estimation%20of%20sea%20state%20from%20Sentinel%201%20Synthetic%20aperture%20radar%20imagery%20for%20maritime%20situation%20awareness.pdf
https://doi.org/10.1080/01431161.2018.1558377