基于高光谱影像分解的土壤含水量反演技术

植被和土壤常同时存在于影像像元中，土壤含水量监测不可避免地会受植被光谱影响。因此，剔除植被光谱干扰显得尤为重要。采用基于光谱匹配的分解算法对Hyperion高光谱数据进行分解，剔除植被光谱的干扰，同时对土壤贡献的光谱信息进行一阶微分和包络线去除变换，选取敏感波段，建立土壤含水量反演模型。结果表明：以波段X661，× 1019和X2067的土壤包络线去除光谱为自变量建立的模型最佳，预测R2值为0.85；未剔除植被光谱时，以波段X541，X979和X1632的一阶微分光谱为自变量建立的模型最佳，预测R2值仅为0.36。通过高光谱影像分解剔除植被光谱干扰估测土壤含水量的方法是可行的，可为今后遥感估测土壤含水量的研究提供参考。

Inversion Technology of Soil Water Content Based on Hyperspectral Image Unmixing

Vegetation and soil are usually both in one pixel and soil moisture content monitoring is inevitably influenced by vegetation spectrum,so it is important to eliminate the interference of vegetation spectrum.Hyperion hyperspectral data were decomposed by decomposition algorithm based on the spectrum matching to eliminate vegetation spectrum,and the first order differential and continuum removal transformation were used to dispose soil spectrum information. Then the sensitive bands were selected to establish the inversion model of soil moisture content.Results show that the best model was established by the bands X₆₆₁,X₁₀₁₉ and X₂₀₆₇ of the soil continuum-removal spectrum,and the forecasted Rz value was 0.85.When the vegetation spectrum is not eliminated,the best model was established by the bands X₅₄₁,X₉₇₉ and X₁₆₃₂ of the soil first order differential spectrum,and the forecasted Rz value was only 0.36.The method of forecasting soil water content by decomposing hyperspectral data to eliminate the vegetation spectrum is feasible and it can provide reference for the research on soil water content forecast by remote sensing.