Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models  

作　　者：Ruiqi Du Junying Chen Youzhen Xiang Ru Xiang Xizhen Yang Tianyang Wang Yujie He Yuxiao Wu Haoyuan Yin Zhitao Zhang Yinwen Chen 

机构地区：[1]College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling,Shanxi,712100,China [2]College of Language and Culture,Northwest A&F University,Yangling,Shanxi,712100,China

出　　处：《International Soil and Water Conservation Research》2024年第3期726-740,共15页国际水土保持研究（英文）

基　　金：the National Natural Science Foundation of China for the project(No.52279047).

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,LSU-XGBoost model achieved the best performance.This model properly reflected the process of soil water-salt 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 gover-nance of salinized land and optimal management

关 键 词：XGBoost Sentinel-2 Spectral unmixing Soil water Soil salt Irrigation area 

分 类 号：S15[农业科学—土壤学]