摘要
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,X1019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。
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 R2 value was 0.85. When the vegeta- tion spectrum is not eliminated, the best model was established by the bands X541, X979 and X1632 of the soil first order difierential spectrum, and the forecasted R2 value was only 0.36. The method offorecasting 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.
出处
《水土保持通报》
CSCD
北大核心
2013年第5期156-160,共5页
Bulletin of Soil and Water Conservation
基金
安徽高等学校省级自然科学研究项目"安徽省生态环境质量定量评价遥感信息模型研究"(KJ2013B189)
滁州学院校级科研启动基金项目"江淮分水岭地区植被盖度遥感信息提取技术研究"(2012qd18)
关键词
土壤含水量
高光谱
混合像元分解
一阶微分光谱
包络线去除光谱
soil water content
hyperspectral
pixel unmixing
first order differential spectrum
continuum re-moval spectrum
