A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation
Abstract Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimate...
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Wiley
2024-11-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036162 |
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| author | Shalini Mahanthege William Kleiber Karl Rittger Balaji Rajagopalan Mary J. Brodzik Edward Bair |
| author_facet | Shalini Mahanthege William Kleiber Karl Rittger Balaji Rajagopalan Mary J. Brodzik Edward Bair |
| author_sort | Shalini Mahanthege |
| collection | DOAJ |
| description | Abstract Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%. |
| format | Article |
| id | doaj-art-e4888f4c86ea4c7587ff2da589f008f0 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
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| series | Water Resources Research |
| spelling | doaj-art-e4888f4c86ea4c7587ff2da589f008f02025-08-23T13:05:51ZengWileyWater Resources Research0043-13971944-79732024-11-016011n/an/a10.1029/2023WR036162A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area EstimationShalini Mahanthege0William Kleiber1Karl Rittger2Balaji Rajagopalan3Mary J. Brodzik4Edward Bair5Department of Statistics University of Missouri Columbia MO USADepartment of Applied Mathematics University of Colorado at Boulder Boulder CO USAInstitute of Arctic and Alpine Research University of Colorado at Boulder Boulder CO USADepartment of Civil, Environmental and Architectural Engineering Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USANational Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USALeidos Inc. Reston VA USAAbstract Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%.https://doi.org/10.1029/2023WR036162random forestMODISLandsatfeature importancedownscalingdata fusion |
| spellingShingle | Shalini Mahanthege William Kleiber Karl Rittger Balaji Rajagopalan Mary J. Brodzik Edward Bair A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation Water Resources Research random forest MODIS Landsat feature importance downscaling data fusion |
| title | A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation |
| title_full | A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation |
| title_fullStr | A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation |
| title_full_unstemmed | A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation |
| title_short | A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation |
| title_sort | spatially distributed machine learning approach for fractional snow covered area estimation |
| topic | random forest MODIS Landsat feature importance downscaling data fusion |
| url | https://doi.org/10.1029/2023WR036162 |
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