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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Published in:Remote Sensing
Main Authors: Laura Stendardi, Stein Rune Karlsen, Eirik Malnes, Lennart Nilsen, Hans Tømmervik, Elisabeth J. Cooper, Claudia Notarnicola
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
Q
Online Access:https://doi.org/10.3390/rs14081866
https://doaj.org/article/2b884029a347498a8d49d977f4eb3cc7
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spelling ftdoajarticles:oai:doaj.org/article:2b884029a347498a8d49d977f4eb3cc7 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 Laura Stendardi Stein Rune Karlsen Eirik Malnes Lennart Nilsen Hans Tømmervik Elisabeth J. Cooper Claudia Notarnicola 2022-04-01T00:00:00Z https://doi.org/10.3390/rs14081866 https://doaj.org/article/2b884029a347498a8d49d977f4eb3cc7 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/8/1866 https://doaj.org/toc/2072-4292 doi:10.3390/rs14081866 2072-4292 https://doaj.org/article/2b884029a347498a8d49d977f4eb3cc7 Remote Sensing, Vol 14, Iss 1866, p 1866 (2022) remote sensing Sentinel-1 and Sentinel-2 time series analysis snow melt Svalbard tundra Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14081866 2022-12-30T22:51:52Z 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. Article in Journal/Newspaper Adventdalen Arctic Svalbard Tundra Directory of Open Access Journals: DOAJ Articles Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Arctic Svalbard Svalbard Archipelago Remote Sensing 14 8 1866
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic remote sensing
Sentinel-1 and Sentinel-2
time series analysis
snow melt
Svalbard
tundra
Science
Q
spellingShingle remote sensing
Sentinel-1 and Sentinel-2
time series analysis
snow melt
Svalbard
tundra
Science
Q
Laura Stendardi
Stein Rune Karlsen
Eirik Malnes
Lennart Nilsen
Hans Tømmervik
Elisabeth J. Cooper
Claudia Notarnicola
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities
topic_facet remote sensing
Sentinel-1 and Sentinel-2
time series analysis
snow melt
Svalbard
tundra
Science
Q
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.
format Article in Journal/Newspaper
author Laura Stendardi
Stein Rune Karlsen
Eirik Malnes
Lennart Nilsen
Hans Tømmervik
Elisabeth J. Cooper
Claudia Notarnicola
author_facet Laura Stendardi
Stein Rune Karlsen
Eirik Malnes
Lennart Nilsen
Hans Tømmervik
Elisabeth J. Cooper
Claudia Notarnicola
author_sort Laura Stendardi
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
publisher MDPI AG
publishDate 2022
url https://doi.org/10.3390/rs14081866
https://doaj.org/article/2b884029a347498a8d49d977f4eb3cc7
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, Vol 14, Iss 1866, p 1866 (2022)
op_relation https://www.mdpi.com/2072-4292/14/8/1866
https://doaj.org/toc/2072-4292
doi:10.3390/rs14081866
2072-4292
https://doaj.org/article/2b884029a347498a8d49d977f4eb3cc7
op_doi https://doi.org/10.3390/rs14081866
container_title Remote Sensing
container_volume 14
container_issue 8
container_start_page 1866
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