Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia
Our study focuses on reconstruction root zone soil moisture (RZSM) in the Kulunda plain, a representative dry steppe area in southern Western Siberia, using remote sensing data (RSD) and machine learning techniques. We employed modern machine learning methods with soil surface layer moisture data fr...
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Altai State University
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ftaltaistuniojs:oai:journal.asu.ru:article/13907 2023-12-17T10:49:59+01:00 Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia Bondarovich, Andrei A. Mordvin, Egor Yu. Pochyomin, Nikita M. Lagutin, Anatoly A. 2023-11-04 application/pdf application/xml http://journal.asu.ru/biol/article/view/13907 https://doi.org/10.5281/zenodo.10061576 eng eng Altai State University http://journal.asu.ru/biol/article/view/13907/11722 http://journal.asu.ru/biol/article/view/13907/11787 http://journal.asu.ru/biol/article/view/13907 doi:10.5281/zenodo.10061576 Acta Biologica Sibirica; Vol 9 (2023): Acta Biologica Sibirica 805–829 Acta Biologica Sibirica; Том 9 (2023): Acta Biologica Sibirica 2412-1908 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2023 ftaltaistuniojs https://doi.org/10.5281/zenodo.10061576 2023-11-21T18:16:19Z Our study focuses on reconstruction root zone soil moisture (RZSM) in the Kulunda plain, a representative dry steppe area in southern Western Siberia, using remote sensing data (RSD) and machine learning techniques. We employed modern machine learning methods with soil surface layer moisture data from the AMSR2 passive microwave radiometer as the primary predictor. Additionally, we incorporated data from local meteorological and soil hydrological stations, as well as gravity lysimeter data for 2015–2017. This choice of predictors was based on the extensive time series of continuous observations and the availability of selected meteorological parameters. Among the machine learning models we evaluated, Random Forest (RF) and Extreme Gradient Boosting (XGW) yielded the best results, achieving statistical metrics of R-squared (R2) values of 0.96 and 0.94, respectively, with corresponding root mean square error (RMSE) values of 0.34 and 0.41. Article in Journal/Newspaper Sibirica Siberia Altai State University: Scientific Journals of ASU |
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Open Polar |
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Altai State University: Scientific Journals of ASU |
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ftaltaistuniojs |
language |
English |
description |
Our study focuses on reconstruction root zone soil moisture (RZSM) in the Kulunda plain, a representative dry steppe area in southern Western Siberia, using remote sensing data (RSD) and machine learning techniques. We employed modern machine learning methods with soil surface layer moisture data from the AMSR2 passive microwave radiometer as the primary predictor. Additionally, we incorporated data from local meteorological and soil hydrological stations, as well as gravity lysimeter data for 2015–2017. This choice of predictors was based on the extensive time series of continuous observations and the availability of selected meteorological parameters. Among the machine learning models we evaluated, Random Forest (RF) and Extreme Gradient Boosting (XGW) yielded the best results, achieving statistical metrics of R-squared (R2) values of 0.96 and 0.94, respectively, with corresponding root mean square error (RMSE) values of 0.34 and 0.41. |
format |
Article in Journal/Newspaper |
author |
Bondarovich, Andrei A. Mordvin, Egor Yu. Pochyomin, Nikita M. Lagutin, Anatoly A. |
spellingShingle |
Bondarovich, Andrei A. Mordvin, Egor Yu. Pochyomin, Nikita M. Lagutin, Anatoly A. Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia |
author_facet |
Bondarovich, Andrei A. Mordvin, Egor Yu. Pochyomin, Nikita M. Lagutin, Anatoly A. |
author_sort |
Bondarovich, Andrei A. |
title |
Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia |
title_short |
Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia |
title_full |
Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia |
title_fullStr |
Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia |
title_full_unstemmed |
Reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of Southern Western Siberia |
title_sort |
reconstruction of root zone soil moisture according to the data from passive microwave radiometer and machine learning in the arid steppe region of southern western siberia |
publisher |
Altai State University |
publishDate |
2023 |
url |
http://journal.asu.ru/biol/article/view/13907 https://doi.org/10.5281/zenodo.10061576 |
genre |
Sibirica Siberia |
genre_facet |
Sibirica Siberia |
op_source |
Acta Biologica Sibirica; Vol 9 (2023): Acta Biologica Sibirica 805–829 Acta Biologica Sibirica; Том 9 (2023): Acta Biologica Sibirica 2412-1908 |
op_relation |
http://journal.asu.ru/biol/article/view/13907/11722 http://journal.asu.ru/biol/article/view/13907/11787 http://journal.asu.ru/biol/article/view/13907 doi:10.5281/zenodo.10061576 |
op_doi |
https://doi.org/10.5281/zenodo.10061576 |
_version_ |
1785574614242426880 |