Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data

The grounding line marks the transition between a glacier's floating and grounded parts and serves as a crucial parameter for monitoring sea level changes and assessing glacier retreat. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for grounding line mapping curre...

Full description

Bibliographic Details
Published in:Remote Sensing of Environment
Main Authors: Ross, Natalya, Milillo, Pietro, Dini, Luigi
Format: Article in Journal/Newspaper
Language:unknown
Published: Elsevier 2024
Subjects:
Online Access:https://elib.dlr.de/209391/
https://www.sciencedirect.com/science/article/pii/S0034425724004553
_version_ 1835020820069482496
author Ross, Natalya
Milillo, Pietro
Dini, Luigi
author_facet Ross, Natalya
Milillo, Pietro
Dini, Luigi
author_sort Ross, Natalya
collection Unknown
container_start_page 114429
container_title Remote Sensing of Environment
container_volume 315
description The grounding line marks the transition between a glacier's floating and grounded parts and serves as a crucial parameter for monitoring sea level changes and assessing glacier retreat. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for grounding line mapping currently requires the involvement of human experts, which becomes challenging with the continuously growing volume of grounding line data available for every Antarctic glacier. While a deep learning approach has been recently proposed for mapping grounding lines over C-band Sentinel-1 DInSAR data, its effectiveness has not been assessed over X-Band COSMO-SkyMed DInSAR data. Similarly, the applicability of an analytical algorithm developed for X-band TerraSAR-X DInSAR data has not been evaluated over a large diverse dataset. Here we apply both techniques to map grounding lines over a large X-band COSMO-SkyMed DInSAR dataset from 2020 to 2022, covering Stancomb-Wills, Veststraumen, Jutulstraumen, Moscow University, and Rennick Antarctic glaciers. We determine strengths and limitations of each algorithm, compare their performance with manual mapping and provide recommendations for choosing appropriate data processing methods for effective grounding line mapping. We also note that since 1996, Moscow University glacier's main trunk was retreating at a rate of 340 ± 80 m/year, while the other four glaciers experienced no retreat. Considering the grounding zone widths, which represent the difference between the high and low tide grounding line positions during a tidal cycle, we detect a grounding zone of 9.7 km over Veststraumen Glacier, which is almost six times larger than the average grounding zone of the other four glaciers.
format Article in Journal/Newspaper
genre Antarc*
Antarctic
Veststraumen Glacier
genre_facet Antarc*
Antarctic
Veststraumen Glacier
geographic Antarctic
Jutulstraumen
Rennick
Veststraumen
Veststraumen Glacier
geographic_facet Antarctic
Jutulstraumen
Rennick
Veststraumen
Veststraumen Glacier
id ftdlr:oai:elib.dlr.de:209391
institution Open Polar
language unknown
long_lat ENVELOPE(-1.000,-1.000,-72.000,-72.000)
ENVELOPE(161.500,161.500,-72.000,-72.000)
ENVELOPE(-13.000,-13.000,-74.000,-74.000)
ENVELOPE(-13.000,-13.000,-74.000,-74.000)
op_collection_id ftdlr
op_doi https://doi.org/10.1016/j.rse.2024.114429
op_relation Ross, Natalya und Milillo, Pietro und Dini, Luigi (2024) Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data. Remote Sensing of Environment, 315 (114429). Elsevier. doi:10.1016/j.rse.2024.114429 <https://doi.org/10.1016/j.rse.2024.114429>. ISSN 0034-4257.
publishDate 2024
publisher Elsevier
record_format openpolar
spelling ftdlr:oai:elib.dlr.de:209391 2025-06-15T14:10:31+00:00 Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data Ross, Natalya Milillo, Pietro Dini, Luigi 2024-12-15 https://elib.dlr.de/209391/ https://www.sciencedirect.com/science/article/pii/S0034425724004553 unknown Elsevier Ross, Natalya und Milillo, Pietro und Dini, Luigi (2024) Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data. Remote Sensing of Environment, 315 (114429). Elsevier. doi:10.1016/j.rse.2024.114429 <https://doi.org/10.1016/j.rse.2024.114429>. ISSN 0034-4257. Institut für Hochfrequenztechnik und Radarsysteme Satelliten-SAR-Systeme Zeitschriftenbeitrag PeerReviewed 2024 ftdlr https://doi.org/10.1016/j.rse.2024.114429 2025-06-04T04:58:09Z The grounding line marks the transition between a glacier's floating and grounded parts and serves as a crucial parameter for monitoring sea level changes and assessing glacier retreat. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for grounding line mapping currently requires the involvement of human experts, which becomes challenging with the continuously growing volume of grounding line data available for every Antarctic glacier. While a deep learning approach has been recently proposed for mapping grounding lines over C-band Sentinel-1 DInSAR data, its effectiveness has not been assessed over X-Band COSMO-SkyMed DInSAR data. Similarly, the applicability of an analytical algorithm developed for X-band TerraSAR-X DInSAR data has not been evaluated over a large diverse dataset. Here we apply both techniques to map grounding lines over a large X-band COSMO-SkyMed DInSAR dataset from 2020 to 2022, covering Stancomb-Wills, Veststraumen, Jutulstraumen, Moscow University, and Rennick Antarctic glaciers. We determine strengths and limitations of each algorithm, compare their performance with manual mapping and provide recommendations for choosing appropriate data processing methods for effective grounding line mapping. We also note that since 1996, Moscow University glacier's main trunk was retreating at a rate of 340 ± 80 m/year, while the other four glaciers experienced no retreat. Considering the grounding zone widths, which represent the difference between the high and low tide grounding line positions during a tidal cycle, we detect a grounding zone of 9.7 km over Veststraumen Glacier, which is almost six times larger than the average grounding zone of the other four glaciers. Article in Journal/Newspaper Antarc* Antarctic Veststraumen Glacier Unknown Antarctic Jutulstraumen ENVELOPE(-1.000,-1.000,-72.000,-72.000) Rennick ENVELOPE(161.500,161.500,-72.000,-72.000) Veststraumen ENVELOPE(-13.000,-13.000,-74.000,-74.000) Veststraumen Glacier ENVELOPE(-13.000,-13.000,-74.000,-74.000) Remote Sensing of Environment 315 114429
spellingShingle Institut für Hochfrequenztechnik und Radarsysteme
Satelliten-SAR-Systeme
Ross, Natalya
Milillo, Pietro
Dini, Luigi
Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
title Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
title_full Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
title_fullStr Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
title_full_unstemmed Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
title_short Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
title_sort automated grounding line delineation using deep learning and phase gradient-based approaches on cosmo-skymed dinsar data
topic Institut für Hochfrequenztechnik und Radarsysteme
Satelliten-SAR-Systeme
topic_facet Institut für Hochfrequenztechnik und Radarsysteme
Satelliten-SAR-Systeme
url https://elib.dlr.de/209391/
https://www.sciencedirect.com/science/article/pii/S0034425724004553