基于有机质特征谱段的土壤Cd含量高光谱遥感反演  

Soil Cd Content Retrieval from Hyperspectral Remote Sensing DataBased on Organic Matter Characteristic Spectral Bands

作　　者：张霞 孙友鑫 尚坤 丁松滔 孙伟超 ZHANG Xia;SUN Youxin;SHANG Kun;DING Songtao;SUN Weichao(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China;Land Satellite Remote Sensing Application Center,Ministry of Natural Resources,Beijing 100048,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100101 [2]中国科学院大学资源与环境学院,北京100049 [3]自然资源部国土卫星遥感应用中心,北京100048

出　　处：《农业机械学报》2024年第1期186-195,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(42371360);中国科学院战略性先导科技专项(XDA28080500)。

摘　　要：针对土壤Cd高光谱遥感定量反演中的机理性不足及数据冗余问题,提出一种基于有机质特征谱段的反演方法。该方法首先提取土壤光谱中对重金属Cd具有吸附作用的有机质特征谱段,进而通过竞争性自适应重加权采样法(Competitive adaptive reweighted sampling, CARS)优选特征谱段,采用偏最小二乘回归法(Partial least squares regression, PLSR)建立重金属Cd的反演模型,并利用郴州矿区土壤实验室光谱数据和哈密黄山南矿区野外光谱数据进行方法验证。研究表明:有机质特征谱段提取在降低数据冗余的同时提高了重金属Cd的反演精度,CARS算法相对于相关系数法(Correlation coefficient, CC)和遗传算法(Genetic algorithm, GA)特征选择具有更高的反演精度,基于有机质特征谱段的CARS-PLSR算法在土壤实验室光谱和野外实测光谱所得验证精度R2分别为0.94和0.80,表明该算法对于实验室和野外光谱均具有一定适用性。研究可为土壤重金属含量高光谱反演的特征波段选择和算法优选提供参考。To address the mechanistic limitations and data redundancy issues in the quantitative retrieval of soil Cd using hyperspectral remote sensing,an inversion method was proposed based on organic matter characteristic spectral bands.The method involved the extraction of characteristic spectral bands of organic matter with adsorption effects on heavy metal Cd in soil spectra.Subsequently,competitive adaptive reweighted sampling(CARS)was employed to optimize the selected spectral bands,and a partial least squares regression(PLSR)model was developed for the inversion of heavy metal Cd.The proposed method was validated by using laboratory spectral data from the Chenzhou mine and field spectral data from the Hami Huangshan South mine.The results demonstrated that the extraction of organic matter characteristic spectral bands not only reduced data redundancy but also significantly improved the accuracy of Cd inversion.In comparison to the correlation coefficient(CC)and genetic algorithm(GA)methods,the CARS algorithm exhibited superior performance in feature selection and inversion accuracy.The validation accuracies,expressed as R2,were 0.94 for the Chenzhou laboratory spectral data and 0.80 for the Hami field spectral data,indicating the robustness of the CARS-PLSR algorithm for both laboratory and field spectra.The findings can provide valuable references for feature band selection and algorithm optimization in the hyperspectral estimation of soil heavy metal content.The proposed method effectively addressed the limitations of existing approaches by leveraging the unique spectral characteristics of organic matter in soil.

关 键 词：高光谱遥感 土壤重金属 土壤光谱活性物质 特征选择 反演 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]