干旱区生态环境知识资源中心(Arid): Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models

Arid

DOI	10.1016/j.iswcr.2023.09.007
	Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models
	Du, Ruiqi; Chen, Junying; Xiang, Youzhen; Xiang, Ru; Yang, Xizhen; Wang, Tianyang; He, Yujie; Wu, Yuxiao; Yin, Haoyuan; Zhang, Zhitao; Chen, Yinwen
通讯作者	Chen, JY
来源期刊	INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH
ISSN	2095-6339
EISSN	2589-059X
出版年	2024
卷号	12 期号:3 页码:726-740
英文摘要	Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R2) being 0.55 and 0.57, respectively. Compared to other models, LSUXGBoost model achieved the best performance. This model properly reflected the process of soil watersalt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation. (c) 2023 International Research and Training Center on Erosion and Sedimentation, China Water and Power Press, and China Institute of Water Resources and Hydropower Research. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
英文关键词	XGBoost Sentinel-2 Spectral unmixing Soil water Soil salt Irrigation area
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001273368400001
WOS关键词	HETAO IRRIGATION DISTRICT ; SALINITY DYNAMICS ; MOISTURE INDEXES ; STRESS
WOS类目	Environmental Sciences ; Soil Science ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404278
推荐引用方式 GB/T 7714	Du, Ruiqi,Chen, Junying,Xiang, Youzhen,et al. Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models[J],2024,12(3):726-740.
APA	Du, Ruiqi.,Chen, Junying.,Xiang, Youzhen.,Xiang, Ru.,Yang, Xizhen.,...&Chen, Yinwen.(2024).Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models.INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH,12(3),726-740.
MLA	Du, Ruiqi,et al."Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models".INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH 12.3(2024):726-740.