基于多端元光谱分解的干旱区植被覆盖度遥感反演  

Remote sensing retrieval of vegetation coverage in arid areas based on multiple endmember spectral unmixing

作　　者：廖春华[1] 张显峰[1] 刘羽[1] 

机构地区：[1]北京大学地理信息与遥感研究所,北京100871

出　　处：《应用生态学报》2012年第12期3243-3249,共7页Chinese Journal of Applied Ecology

基　　金：国家自然科学基金项目(41071257);"十二五"国家科技支撑计划项目(2012BAH27B03)资助

摘　　要：植被覆盖度是评价陆地生态系统状况与土地荒漠化程度的重要指标.利用环境一号(HJ-1)小卫星上搭载的新型高光谱传感器HSI获取的数据,通过纯净像元指数和端元平均均方根误差相结合的方法提取合适的端元光谱,然后基于多端元混合像元分解(MESMA)模型,反演了新疆石河子地区的植被覆盖度(FVC).通过与线性光谱分解(LSMA)模型反演结果以及地面样方数据进行比较,对HJ-1/HSI数据反演结果进行精度评价与光谱验证.结果表明:MESMA模型能对不同的像元选取不同端元组合,更接近实际情况,比LSMA模型能更好地提取植被覆盖度信息;与LSMA模型相比,MESMA模型反演的FVC值与地面实测数据的相关系数从0.766提高到0.838,均方根误差从0.375减少到0.196.Vegetation coverage is an important indicator in the assessment of terrestrial ecosystem and land desertification. By using the data acquired from the novel hyperspectral sensor HIS in Chinese HJ-1 small satellite, the suitable endmember spectrum was extracted by the combination of pixel purity index and endmember average root mean square error. Then, the vegetation coverage （FVC） in Shihezi area of Xinjiang, Northwest China was retrieved by the model of multiple endmember spectral mixture analysis （MESMA）. With the comparison of the FVCs retrieved from the linear spectral analysis （LSMA） model and the measurement results, the FVCs retrieved from the MESMA model were evaluated. The results showed that the MESMA model enabled the use of different endmember combinations for different image pixels, and thus, could perform better than the LSMA model in the estimation of regional FVCs. As compared with the LSMA model, the correlation coefficient between the FVCs retrieved from the MESMA model and the measured FVCs increased from 0.766 to 0.838, while the root mean square error decreased from 0.375 to 0. 196.

关 键 词：混合光谱分析 高光谱遥感 端元平均均方根误差 环境一号小卫星 石河子 

分 类 号：Q948[生物学—植物学] P237[天文地球—摄影测量与遥感]