Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate chan...
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Autor*in:	Shaohua Wang [verfasserIn] Gang Fu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	soil quality global change random forest alpine ecosystem alpine region ‘third pole’

Übergeordnetes Werk:	In: Frontiers in Environmental Science - Frontiers Media S.A., 2014, 11(2023)
Übergeordnetes Werk:	volume:11 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fenvs.2023.1130448

Katalog-ID:	DOAJ080704530

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520			\|a Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.
650		4	\|a soil quality
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language	English
source	In Frontiers in Environmental Science 11(2023) volume:11 year:2023
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topic_facet	soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences
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container_title	Frontiers in Environmental Science
authorswithroles_txt_mv	Shaohua Wang @@aut@@ Gang Fu @@aut@@
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callnumber-first	G - Geography, Anthropology, Recreation
author	Shaohua Wang
spellingShingle	Shaohua Wang misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
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topic_title	GE1-350 Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands soil quality global change random forest alpine ecosystem alpine region ‘third pole’
topic	misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences
topic_unstemmed	misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences
topic_browse	misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences
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title	Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
ctrlnum	(DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17
title_full	Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
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title_sort	modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
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title_auth	Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
abstract	Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.
abstractGer	Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.
abstract_unstemmed	Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.
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title_short	Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
url	https://doi.org/10.3389/fenvs.2023.1130448 https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full https://doaj.org/toc/2296-665X
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