基于高光谱的土壤有机质含量反演研究  

Studies on the Inversion of Soil Organic Matter Content Based on Hyper-spectrum

作　　者：于士凯[1,2] 姚艳敏[1,2] 王德营[1,2] 司海青[1,2] 

机构地区：[1]农业部农业信息技术重点实验室,北京100081 [2]中国农业科学院农业资源与农业区划研究所,北京100081

出　　处：《中国农学通报》2013年第23期146-152,共7页Chinese Agricultural Science Bulletin

基　　金：国家"973"计划子课题"气候变化对我国粮食生产资源要素的影响机理研究"(2010CB951501);国家科技基础性工作专项课题"大豆;牧草光温数据数字化图集编制"(2007FY120100)

摘　　要：土壤有机质含量是土壤肥力的一个重要指标,利用高光谱对土壤有机质含量进行定量化反演,为精准农业地表土壤有机质含量的快速测定提供参考。利用美国ASDFieldSpecFR地物光谱仪,在室内条件下对经过处理的土壤样品进行光谱测量,通过对土壤样品光谱反射率不同变换形式与有机质含量进行相关性分析,建立土壤光谱变量与土壤有机质含量的多元回归关系模型。结果表明:在波长492nm、663nm、1221nm、1317nm、1835nm和2130nm处,采用光谱反射率一阶微分建立的土壤有机质含量反演回归模型,预测精度最好,决定系数R2为0.909。建立的土壤有机质含量高光谱反演模型,可以较好地预测土壤有机质含量,从而为精准农业土壤有机质含量的快速测定提供新的途径。The use of hyperspectral can conduct quantitative inversion on soil organic matter content, which is an important indicator of soil fertility, and then provide a reference for the rapid determination of surface soil organic matter content of accurate agricultural. The author conducted the spectral measurements on treated soil samples under laboratory conditions by using the spectroradiometer-U.S.ASD FieldSpec FR, established the multiple regression relationship model between the soil spectral variables and soil organic matter content through the correlation analysis between the different variations of the spectral reflectance of the soil samples and the organic matter content of the soil. The results showed that： the soil organic matter content inversed regression model, which was established by employing the first-order differential spectral reflectance at the wavelength of 492 nm, 663 nm, 1221 nm, 1317 nm, 1835 nm and 2130 nm, possessed the best prediction accuracy, the coefficient of determination R 2 was 0.909. The established hyperspectral inversion model of the soil organic matter content could predict the soil organic matter content with the most accurate, and it also provide a new approach for the rapid determination of soil organic matter content of precision agriculture.

关 键 词：土壤有机质 高光谱 多元回归 光谱预测模型 

分 类 号：S-3[农业科学]