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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Online Access: | https://doi.org/10.1088/1748-9326/abfe8d https://doaj.org/article/c44beb963c364cfe83e06e50c80c1bfa |
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ftdoajarticles:oai:doaj.org/article:c44beb963c364cfe83e06e50c80c1bfa 2023-09-05T13:22:00+02:00 Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach Jie Zhang Veijo A Pohjola Rickard Pettersson Björn Norell Wolf-Dietrich Marchand Ilaria Clemenzi David Gustafsson 2021-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/abfe8d https://doaj.org/article/c44beb963c364cfe83e06e50c80c1bfa EN eng IOP Publishing https://doi.org/10.1088/1748-9326/abfe8d https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abfe8d 1748-9326 https://doaj.org/article/c44beb963c364cfe83e06e50c80c1bfa Environmental Research Letters, Vol 16, Iss 8, p 084007 (2021) mountain snow satellite GPR machine learning Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 article 2021 ftdoajarticles https://doi.org/10.1088/1748-9326/abfe8d 2023-08-13T00:37:11Z 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 Överuman 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 Directory of Open Access Journals: DOAJ Articles Överuman ENVELOPE(14.833,14.833,66.050,66.050) Environmental Research Letters 16 8 084007 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
mountain snow satellite GPR machine learning Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
spellingShingle |
mountain snow satellite GPR machine learning Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 Jie Zhang Veijo A Pohjola Rickard Pettersson Björn Norell Wolf-Dietrich Marchand Ilaria Clemenzi David Gustafsson Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach |
topic_facet |
mountain snow satellite GPR machine learning Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
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 Överuman 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 |
Jie Zhang Veijo A Pohjola Rickard Pettersson Björn Norell Wolf-Dietrich Marchand Ilaria Clemenzi David Gustafsson |
author_facet |
Jie Zhang Veijo A Pohjola Rickard Pettersson Björn Norell Wolf-Dietrich Marchand Ilaria Clemenzi David Gustafsson |
author_sort |
Jie Zhang |
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 |
IOP Publishing |
publishDate |
2021 |
url |
https://doi.org/10.1088/1748-9326/abfe8d https://doaj.org/article/c44beb963c364cfe83e06e50c80c1bfa |
long_lat |
ENVELOPE(14.833,14.833,66.050,66.050) |
geographic |
Överuman |
geographic_facet |
Överuman |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Environmental Research Letters, Vol 16, Iss 8, p 084007 (2021) |
op_relation |
https://doi.org/10.1088/1748-9326/abfe8d https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abfe8d 1748-9326 https://doaj.org/article/c44beb963c364cfe83e06e50c80c1bfa |
op_doi |
https://doi.org/10.1088/1748-9326/abfe8d |
container_title |
Environmental Research Letters |
container_volume |
16 |
container_issue |
8 |
container_start_page |
084007 |
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1776202537428320256 |