Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water...
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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-452035 https://doi.org/10.1088/1748-9326/abfe8d |
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ftuppsalauniv:oai:DiVA.org:uu-452035 2024-02-11T10:07:11+01:00 Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach Zhang, Jie Pohjola, Veijo Pettersson, Rickard Norell, Björn Wolf-Dietrich, Marchand Clemenzi, Ilaria Gustafsson, David 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035 https://doi.org/10.1088/1748-9326/abfe8d eng eng Uppsala universitet, Luft-, vatten- och landskapslära Vattenregleringsforetagen, Östersund, Sweden. Sweco Norge AS, Trondheim, Norway. Swedish Meteorol & Hydrol Inst, Norrköping, Sweden. Swedish Meteorological and Hydrological Institute, Norrköping, Sweden. Environmental Research Letters, 2021, 16:8, orcid:0000-0001-6851-1673 orcid:0000-0002-6961-0128 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035 doi:10.1088/1748-9326/abfe8d ISI:000678345800001 info:eu-repo/semantics/openAccess Physical Geography Naturgeografi Article in journal info:eu-repo/semantics/article text 2021 ftuppsalauniv https://doi.org/10.1088/1748-9326/abfe8d 2024-01-17T23:33:35Z Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas. Article in Journal/Newspaper Northern Sweden Uppsala University: Publications (DiVA) Environmental Research Letters 16 8 084007 |
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Open Polar |
collection |
Uppsala University: Publications (DiVA) |
op_collection_id |
ftuppsalauniv |
language |
English |
topic |
Physical Geography Naturgeografi |
spellingShingle |
Physical Geography Naturgeografi Zhang, Jie Pohjola, Veijo Pettersson, Rickard Norell, Björn Wolf-Dietrich, Marchand Clemenzi, Ilaria Gustafsson, David Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
topic_facet |
Physical Geography Naturgeografi |
description |
Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas. |
format |
Article in Journal/Newspaper |
author |
Zhang, Jie Pohjola, Veijo Pettersson, Rickard Norell, Björn Wolf-Dietrich, Marchand Clemenzi, Ilaria Gustafsson, David |
author_facet |
Zhang, Jie Pohjola, Veijo Pettersson, Rickard Norell, Björn Wolf-Dietrich, Marchand Clemenzi, Ilaria Gustafsson, David |
author_sort |
Zhang, Jie |
title |
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
title_short |
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
title_full |
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
title_fullStr |
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
title_full_unstemmed |
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
title_sort |
improving the snowpack monitoring in the mountainous areas of sweden from space : a machine learning approach |
publisher |
Uppsala universitet, Luft-, vatten- och landskapslära |
publishDate |
2021 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035 https://doi.org/10.1088/1748-9326/abfe8d |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
Environmental Research Letters, 2021, 16:8, orcid:0000-0001-6851-1673 orcid:0000-0002-6961-0128 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035 doi:10.1088/1748-9326/abfe8d ISI:000678345800001 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1088/1748-9326/abfe8d |
container_title |
Environmental Research Letters |
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16 |
container_issue |
8 |
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084007 |
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1790605339712815104 |