Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine
Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, t...
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ftmdpi:oai:mdpi.com:/2072-4292/13/9/1626/ 2023-08-20T04:04:19+02:00 Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine Georg Pointner Annett Bartsch 2021-04-21 application/pdf https://doi.org/10.3390/rs13091626 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs13091626 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 9; Pages: 1626 arctic lake ice SAR change detection methane Yamal permafrost Google Earth Engine Text 2021 ftmdpi https://doi.org/10.3390/rs13091626 2023-08-01T01:33:12Z Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect ... Text Arctic Ice lena delta permafrost Yamal Peninsula Alaska Siberia MDPI Open Access Publishing Arctic Yamal Peninsula ENVELOPE(69.873,69.873,70.816,70.816) Arctic Lake ENVELOPE(-130.826,-130.826,57.231,57.231) The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Remote Sensing 13 9 1626 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
arctic lake ice SAR change detection methane Yamal permafrost Google Earth Engine |
spellingShingle |
arctic lake ice SAR change detection methane Yamal permafrost Google Earth Engine Georg Pointner Annett Bartsch Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine |
topic_facet |
arctic lake ice SAR change detection methane Yamal permafrost Google Earth Engine |
description |
Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect ... |
format |
Text |
author |
Georg Pointner Annett Bartsch |
author_facet |
Georg Pointner Annett Bartsch |
author_sort |
Georg Pointner |
title |
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine |
title_short |
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine |
title_full |
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine |
title_fullStr |
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine |
title_full_unstemmed |
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine |
title_sort |
mapping arctic lake ice backscatter anomalies using sentinel-1 time series on google earth engine |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13091626 |
long_lat |
ENVELOPE(69.873,69.873,70.816,70.816) ENVELOPE(-130.826,-130.826,57.231,57.231) ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
Arctic Yamal Peninsula Arctic Lake The Sentinel |
geographic_facet |
Arctic Yamal Peninsula Arctic Lake The Sentinel |
genre |
Arctic Ice lena delta permafrost Yamal Peninsula Alaska Siberia |
genre_facet |
Arctic Ice lena delta permafrost Yamal Peninsula Alaska Siberia |
op_source |
Remote Sensing; Volume 13; Issue 9; Pages: 1626 |
op_relation |
https://dx.doi.org/10.3390/rs13091626 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13091626 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
9 |
container_start_page |
1626 |
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1774714702983593984 |