Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?

The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Tai...

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Autores principales:	Ya Liu, Qing Zhu, Kaihua Liao, Xiaoming Lai, Junbang Wang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a
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spelling	oai:doaj.org-article:bdd01ba794a44b19a57935695a66bf6a2021-11-05T19:01:35ZDownscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?2040-22442408-935410.2166/wcc.2020.131https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a2021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/1564https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.;Ya LiuQing ZhuKaihua LiaoXiaoming LaiJunbang WangIWA Publishingarticleremote sensingsoil hydrologysoil moisturetaihu lake basinwatershedEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 1564-1579 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Ya Liu Qing Zhu Kaihua Liao Xiaoming Lai Junbang Wang Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
description	The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.;
format	article
author	Ya Liu Qing Zhu Kaihua Liao Xiaoming Lai Junbang Wang
author_facet	Ya Liu Qing Zhu Kaihua Liao Xiaoming Lai Junbang Wang
author_sort	Ya Liu
title	Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
title_short	Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
title_full	Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
title_fullStr	Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
title_full_unstemmed	Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
title_sort	downscaling of esa cci soil moisture in taihu lake basin: are wetness conditions and non-linearity important?
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a
work_keys_str_mv	AT yaliu downscalingofesaccisoilmoistureintaihulakebasinarewetnessconditionsandnonlinearityimportant AT qingzhu downscalingofesaccisoilmoistureintaihulakebasinarewetnessconditionsandnonlinearityimportant AT kaihualiao downscalingofesaccisoilmoistureintaihulakebasinarewetnessconditionsandnonlinearityimportant AT xiaominglai downscalingofesaccisoilmoistureintaihulakebasinarewetnessconditionsandnonlinearityimportant AT junbangwang downscalingofesaccisoilmoistureintaihulakebasinarewetnessconditionsandnonlinearityimportant
_version_	1718444062410801152

Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?

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