Advances in research on methods for optical remote sensing monitoring of soil salinization
Soil salinization is identified as a major cause of decreased soil fertility,productivity,vegetation coverage,and crop yield.Optical remote sensing monitoring enjoys advantages such as macro-scale,timeliness,dynamics,and low costs,rendering this technology significant for the dynamic monitoring of soil salinization.However,there is a lack of reviews of the systematic organization of multi-scale remote sensing data,multi-type remote sensing feature parameters,and inversion models.This study first organized the optical remote sensing data sources and summarized the remote sensing data sources and scale platforms utilized in current studies on saline soil monitoring.Accordingly,this study categorized multi-source remote sensing data into three different platforms:satellite,aerial,and ground.Second,this study organized the mainstream characteristic parameters for modeling and two typical inversion methods,i.e.,statistical regression and machine learning,and analyzed the current status of research on both methods.Finally,this study explored the fusion of remote sensing data sources and compared the pros and cons of various modeling methods.Furthermore,in combination with current hot research topics,this study discussed the prospects for the application of data assimilation and deep learning to soil salinization monito-ring.
万方数据
维普
