土壤盐渍化光学遥感监测方法研究进展  

Advances in research on methods for optical remote sensing monitoring of soil salinization

作　　者：骆振海 张超 冯绍元[1] 唐敏 刘锐 孔纪迎 LUO Zhenhai;ZHANG Chao;FENG Shaoyuan;TANG Min;LIU Rui;KONG Jiying(College of Hydraulic Science and Engineering,Yangzhou University,Yangzhou 225009,China)

机构地区：[1]扬州大学水利科学与工程学院,扬州225009

出　　处：《自然资源遥感》2024年第4期9-22,共14页Remote Sensing for Natural Resources

基　　金：国家自然科学基金项目“多源遥感与作物-水盐耦合模型同化的盐渍化农田水盐反演方法研究”(编号:52379049)和江苏省研究生科研与实践创新计划项目(SJCX23_1945)共同资助。

摘　　要：土壤盐渍化是导致土壤肥力下降、生产力衰退、植被覆盖减少以及作物产量降低的重要原因之一。光学遥感监测技术具有宏观、及时、动态和低成本等优点,对于实现土壤盐渍化的动态监测具有重要意义。然而,目前少有对多尺度遥感数据、多类型遥感特征参量以及反演模型进行系统性梳理和总结的综述研究。为此,该文首先对光学遥感数据源进行了梳理,并总结了目前盐渍土监测相关研究中使用的遥感数据来源与尺度平台,将多源遥感数据分为卫星、航空和地面3种不同平台;其次,整理了目前主流的建模特征参量以及统计回归和机器学习2类经典的反演方法,并分析了各方法的研究现状;最后,对遥感数据源间的融合进行了探讨,比较了各建模方法的优缺点,并结合当前研究热点,展望了将数据同化与深度学习应用于土壤盐渍化监测研究的前景。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 monitoring.

关 键 词：土壤盐渍化 土壤盐分 光学遥感 反演模型 特征参量 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]