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Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia
Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its app...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838832/ https://www.ncbi.nlm.nih.gov/pubmed/36638093 http://dx.doi.org/10.1371/journal.pone.0279895 |
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author | Sishah, Shimelis Abrahem, Temesgen Azene, Getasew Dessalew, Amare Hundera, Hurgesa |
author_facet | Sishah, Shimelis Abrahem, Temesgen Azene, Getasew Dessalew, Amare Hundera, Hurgesa |
author_sort | Sishah, Shimelis |
collection | PubMed |
description | Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm(3) /cm(3), -0.036 cm(3) /cm(3), and 0.112 cm(3) /cm(3) with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm(3) /cm(3), -0.033 cm(3) /cm(3), and 0.148 cm(3) /cm(3) with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region. |
format | Online Article Text |
id | pubmed-9838832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98388322023-01-14 Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia Sishah, Shimelis Abrahem, Temesgen Azene, Getasew Dessalew, Amare Hundera, Hurgesa PLoS One Research Article Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm(3) /cm(3), -0.036 cm(3) /cm(3), and 0.112 cm(3) /cm(3) with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm(3) /cm(3), -0.033 cm(3) /cm(3), and 0.148 cm(3) /cm(3) with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region. Public Library of Science 2023-01-13 /pmc/articles/PMC9838832/ /pubmed/36638093 http://dx.doi.org/10.1371/journal.pone.0279895 Text en © 2023 Sishah et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Sishah, Shimelis Abrahem, Temesgen Azene, Getasew Dessalew, Amare Hundera, Hurgesa Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia |
title | Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia |
title_full | Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia |
title_fullStr | Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia |
title_full_unstemmed | Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia |
title_short | Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia |
title_sort | downscaling and validating smap soil moisture using a machine learning algorithm over the awash river basin, ethiopia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838832/ https://www.ncbi.nlm.nih.gov/pubmed/36638093 http://dx.doi.org/10.1371/journal.pone.0279895 |
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