Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities
Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the sensitivity of the Synthetic Aperture Radar (SAR) bac...
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Online Access: | https://hdl.handle.net/11250/2991424 https://doi.org/10.3390/rs14081866 |
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ftnorce:oai:norceresearch.brage.unit.no:11250/2991424 2023-05-15T13:05:40+02:00 Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities Stendardi, Laura Karlsen, Stein Rune Malnes, Eirik Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. Notarnicola, Claudia 2022 application/pdf https://hdl.handle.net/11250/2991424 https://doi.org/10.3390/rs14081866 eng eng EU/869471 Norges forskningsråd: 269927 Norges forskningsråd: 230970 urn:issn:2072-4292 https://hdl.handle.net/11250/2991424 https://doi.org/10.3390/rs14081866 cristin:2017572 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © 2022 by the authors CC-BY Remote Sensing VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 Peer reviewed Journal article 2022 ftnorce https://doi.org/10.3390/rs14081866 2022-10-13T05:50:42Z Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the sensitivity of the Synthetic Aperture Radar (SAR) backscatter to vegetation growth and soil moisture in an arctic environment. A combined approach using time series data from active remote sensing sensors such as SAR and passive optical sensors is a known technique in snow monitoring, while there is little knowledge of the radar C-band’s response pattern to vegetation dynamics in the arctic. First, we created multi-sensor masks using the HV backscatter coefficients from Sentinel-1 and the Normalized Difference Snow Index (NDSI) time series from Sentinel-2, monitoring the snow dynamics in Adventdalen (Svalbard) for the season from 2017 to 2018. Second, radar sensitivity analysis was performed using the HV polarized channel responses to vegetation growth and soil moisture dynamics. (1) Our results showed that the C-band radar data are capable of monitoring the seasonal variability in timing of snow melting in Adventdalen, revealing an earlier start by approximately 20 days in 2018 compared to 2017. (2) From the sensitivity analyses, the HV channel showed a major response to the vegetation component in areas with drier graminoid dominated vegetation without water-saturated soil (R = 0.69). However, the temperature was strongly correlated with the HV channel (R = 0.74) during the years with delayed snow melting. Areas of frozen tundra with drier vegetation dominated by graminoids had delayed soil thawing processes and therefore this may limit the ability of the radar to follow the vegetation growth pattern and soil moisture. View Full-Text remote sensing; Sentinel-1 and Sentinel-2; time series analysis; snow melt; Svalbard; tundra; plant phenology publishedVersion Article in Journal/Newspaper Adventdalen Arctic Svalbard Tundra NORCE vitenarkiv (Norwegian Research Centre) Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Arctic Svalbard Svalbard Archipelago Remote Sensing 14 8 1866 |
institution |
Open Polar |
collection |
NORCE vitenarkiv (Norwegian Research Centre) |
op_collection_id |
ftnorce |
language |
English |
topic |
VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 |
spellingShingle |
VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 Stendardi, Laura Karlsen, Stein Rune Malnes, Eirik Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. Notarnicola, Claudia Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities |
topic_facet |
VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 |
description |
Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the sensitivity of the Synthetic Aperture Radar (SAR) backscatter to vegetation growth and soil moisture in an arctic environment. A combined approach using time series data from active remote sensing sensors such as SAR and passive optical sensors is a known technique in snow monitoring, while there is little knowledge of the radar C-band’s response pattern to vegetation dynamics in the arctic. First, we created multi-sensor masks using the HV backscatter coefficients from Sentinel-1 and the Normalized Difference Snow Index (NDSI) time series from Sentinel-2, monitoring the snow dynamics in Adventdalen (Svalbard) for the season from 2017 to 2018. Second, radar sensitivity analysis was performed using the HV polarized channel responses to vegetation growth and soil moisture dynamics. (1) Our results showed that the C-band radar data are capable of monitoring the seasonal variability in timing of snow melting in Adventdalen, revealing an earlier start by approximately 20 days in 2018 compared to 2017. (2) From the sensitivity analyses, the HV channel showed a major response to the vegetation component in areas with drier graminoid dominated vegetation without water-saturated soil (R = 0.69). However, the temperature was strongly correlated with the HV channel (R = 0.74) during the years with delayed snow melting. Areas of frozen tundra with drier vegetation dominated by graminoids had delayed soil thawing processes and therefore this may limit the ability of the radar to follow the vegetation growth pattern and soil moisture. View Full-Text remote sensing; Sentinel-1 and Sentinel-2; time series analysis; snow melt; Svalbard; tundra; plant phenology publishedVersion |
format |
Article in Journal/Newspaper |
author |
Stendardi, Laura Karlsen, Stein Rune Malnes, Eirik Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. Notarnicola, Claudia |
author_facet |
Stendardi, Laura Karlsen, Stein Rune Malnes, Eirik Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. Notarnicola, Claudia |
author_sort |
Stendardi, Laura |
title |
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities |
title_short |
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities |
title_full |
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities |
title_fullStr |
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities |
title_full_unstemmed |
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities |
title_sort |
multi-sensor analysis of snow seasonality and a preliminary assessment of sar backscatter sensitivity to arctic vegetation: limits and capabilities |
publishDate |
2022 |
url |
https://hdl.handle.net/11250/2991424 https://doi.org/10.3390/rs14081866 |
long_lat |
ENVELOPE(16.264,16.264,78.181,78.181) |
geographic |
Adventdalen Arctic Svalbard Svalbard Archipelago |
geographic_facet |
Adventdalen Arctic Svalbard Svalbard Archipelago |
genre |
Adventdalen Arctic Svalbard Tundra |
genre_facet |
Adventdalen Arctic Svalbard Tundra |
op_source |
Remote Sensing |
op_relation |
EU/869471 Norges forskningsråd: 269927 Norges forskningsråd: 230970 urn:issn:2072-4292 https://hdl.handle.net/11250/2991424 https://doi.org/10.3390/rs14081866 cristin:2017572 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © 2022 by the authors |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/rs14081866 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
8 |
container_start_page |
1866 |
_version_ |
1766390286240448512 |