摘要
目前用物理模型反演叶面积指数普遍存在缺少先验知识的状况,如何获得准确的先验知识是遥感走向应用的一个关键环节。中国典型地物标准波谱数据库就是结合国家重大行业中的应用需求,研究制定地物波谱获取与分析的技术规范和数据标准,建立典型地物标准波谱数据库。从波谱数据库提取模型反演所需要的先验知识,实现了基于SAIL模型的MOD IS数据(经过几何纠正与大气纠正)叶面积指数的反演。另外,基于TM数据,对MOD IS混合像元进行了分解,用纯像元的叶面积指数与实测数据进行对比验证,同时,反演结果与NASA的LAI产品也进行了对比,结果表明基于波谱库的先验知识可以有效的提高叶面积指数的反演精度。
It is feasible to retrieve LAI over large area from remote sensing data with physical models; however, it is quite difficult to get accurate LAI and thus limit the remote sensing application without enough prior knowledge due to the underdetermined parameters in the physical inversion models. A spectrum database system of typical objects in China（SpecLib） has been set up recently, which may provide a priori knowledge of typical land cover for LAI inversion. MODIS data is used to retrieve LAI after atmosphere correction, geometrical correction and cloud identification. The SAIL （Scattering by Arbitrarily Inclined Layers） model is applied for the inversion of LAI for MODIS data. The vegetation coverage of the mixed pixels of the MODIS data are calculated based on the TM data sets. The LAIs of pure pixels （ computed from the retrieved LAIs and vegetation coverage） are compared with the field measurement data in Luancheng, Heibei Province, China. Meanwhile, the LAIs of pure pixels are also compared with the MODIS LAI data products. The inversion results show that the SpecLib effectively improved the accuracy of leaf area index inversion.
出处
《遥感学报》
EI
CSCD
北大核心
2006年第6期869-878,共10页
NATIONAL REMOTE SENSING BULLETIN
基金
中国科学院知识创新工程重要方向项目(编号:KZCX3-SW-338)
国家自然科学基金(编号:NSFC40371087)
国家重点基础发展规划项目(编号:2000077903)资助
关键词
波谱库
MODIS数据
叶面积指数
反演
SpecLib
moderate resolution imaging spectrometer（MODIS）
leaf area index
inversion
