A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study
Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the las...
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Language: | English |
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Uppsala universitet, Luft-, vatten- och landskapslära
2021
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Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442843 https://doi.org/10.3390/rs13102002 |
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ftuppsalauniv:oai:DiVA.org:uu-442843 2024-02-11T10:05:59+01:00 A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study Vickers, Hannah Malnes, Eirik Van Pelt, Ward Pohjola, Veijo Killie, Mari Anne Saloranta, Tuomo Karlsen, Stein Rune 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442843 https://doi.org/10.3390/rs13102002 eng eng Uppsala universitet, Luft-, vatten- och landskapslära NORCE Norwegian Research Centre AS, P.O. Box 6434, NO-9294 Tromsø, Norway Norwegian Meteorological Institute, P.O. Box 43, NO-0313 Oslo, Norway Hydrology Department, Norwegian Water Resources and Energy Directorate, P.O. Box 5091, NO-0301 Oslo, Norway Remote Sensing, 2021, 13:10, orcid:0000-0003-4839-7900 orcid:0000-0001-6851-1673 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442843 doi:10.3390/rs13102002 ISI:000662610500001 info:eu-repo/semantics/openAccess polar regions snow cover remote sensing snow modelling MODIS Sentinel-2 Physical Geography Naturgeografi Article in journal info:eu-repo/semantics/article text 2021 ftuppsalauniv https://doi.org/10.3390/rs13102002 2024-01-17T23:32:42Z Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Article in Journal/Newspaper Nordenskiöld Land Svalbard Uppsala University: Publications (DiVA) Nordenskiöld Land ENVELOPE(15.000,15.000,77.833,77.833) Svalbard Remote Sensing 13 10 2002 |
institution |
Open Polar |
collection |
Uppsala University: Publications (DiVA) |
op_collection_id |
ftuppsalauniv |
language |
English |
topic |
polar regions snow cover remote sensing snow modelling MODIS Sentinel-2 Physical Geography Naturgeografi |
spellingShingle |
polar regions snow cover remote sensing snow modelling MODIS Sentinel-2 Physical Geography Naturgeografi Vickers, Hannah Malnes, Eirik Van Pelt, Ward Pohjola, Veijo Killie, Mari Anne Saloranta, Tuomo Karlsen, Stein Rune A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study |
topic_facet |
polar regions snow cover remote sensing snow modelling MODIS Sentinel-2 Physical Geography Naturgeografi |
description |
Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. |
format |
Article in Journal/Newspaper |
author |
Vickers, Hannah Malnes, Eirik Van Pelt, Ward Pohjola, Veijo Killie, Mari Anne Saloranta, Tuomo Karlsen, Stein Rune |
author_facet |
Vickers, Hannah Malnes, Eirik Van Pelt, Ward Pohjola, Veijo Killie, Mari Anne Saloranta, Tuomo Karlsen, Stein Rune |
author_sort |
Vickers, Hannah |
title |
A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study |
title_short |
A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study |
title_full |
A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study |
title_fullStr |
A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study |
title_full_unstemmed |
A compilation of snow cover datasets for Svalbard : A multi-sensor, multi-model study |
title_sort |
compilation of snow cover datasets for svalbard : a multi-sensor, multi-model study |
publisher |
Uppsala universitet, Luft-, vatten- och landskapslära |
publishDate |
2021 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442843 https://doi.org/10.3390/rs13102002 |
long_lat |
ENVELOPE(15.000,15.000,77.833,77.833) |
geographic |
Nordenskiöld Land Svalbard |
geographic_facet |
Nordenskiöld Land Svalbard |
genre |
Nordenskiöld Land Svalbard |
genre_facet |
Nordenskiöld Land Svalbard |
op_relation |
Remote Sensing, 2021, 13:10, orcid:0000-0003-4839-7900 orcid:0000-0001-6851-1673 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442843 doi:10.3390/rs13102002 ISI:000662610500001 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs13102002 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
10 |
container_start_page |
2002 |
_version_ |
1790603352385519616 |