干旱区生态环境知识资源中心(Arid): Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models

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DOI	10.1002/ldr.4505
	Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models
	Wang, Sinan; Wu, Yingjie; Li, Ruiping; Wang, Xiuqing
通讯作者	Li, RP
来源期刊	LAND DEGRADATION & DEVELOPMENT
ISSN	1085-3278
EISSN	1099-145X
出版年	2023
卷号	34 期号:3 页码:911-925
英文摘要	Machine learning combined with multisource remote sensing data to assess soil moisture content (SMC) has attracted considerable attention in SMC studies, but the retrieval results still remain uncertain. The purpose of this study is to combine multiple single machine learning models with integrated learning algorithms and propose an SMC retrieval method based on multiple differentiated models under a stacking integrated learning architecture. First, 19 factors, including: radar backscattering coefficient, vegetation index, and drought index, that affect SMC were extracted from SENTINEL-1, LANDSAT, and terrain factors. Those with the highest importance scores were selected as retrieval factors using the Boruta algorithm combined with four single machine learning methods-classified regression tree, random forest, gradient boosting decision tree (GBDT), and extreme random tree. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and the generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that radar backscattering coefficient, temperature, vegetation drought index, land surface temperature, enhanced vegetation index, and solar local incident angle were the most important environmental variables for soil moisture retrieval. A comparison of the four machine learning methods in April and August showed that the GBDT model revealed the highest SMC retrieval accuracy, with root mean square error values of 1.87% and 1.64%, respectively. The stacking models were more accurate than the optimal single machine learning model, especially when using GBM. The multifactor integrated model constructed using spectral indices, radar backscatter coefficients, and topographic data exhibited high accuracy in soil surface moisture retrieval in an arid zone, providing a reference for land desertification studies and ecological environment management in the study region.
英文关键词	machine learning radar backscattering coefficient remote sensing soil moisture stacking
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000888175300001
WOS关键词	SENTINEL-1 ; ALGORITHM ; INDEX
WOS类目	Environmental Sciences ; Soil Science
WOS研究方向	Environmental Sciences & Ecology ; Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397725
推荐引用方式 GB/T 7714	Wang, Sinan,Wu, Yingjie,Li, Ruiping,et al. Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models[J],2023,34(3):911-925.
APA	Wang, Sinan,Wu, Yingjie,Li, Ruiping,&Wang, Xiuqing.(2023).Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models.LAND DEGRADATION & DEVELOPMENT,34(3),911-925.
MLA	Wang, Sinan,et al."Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models".LAND DEGRADATION & DEVELOPMENT 34.3(2023):911-925.