Multi-scale MSDT inversion based on LAI spatial knowledge  

作　　者：ZHU XiaoHua FENG XiaoMing ZHAO YingShi 

机构地区：[1]Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101,China [2]Graduate School of the Chinese Academy of Sciences,Beijing 100049,China [3]Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085,China

出　　处：《Science China Earth Sciences》2012年第8期1297-1305,共9页中国科学（地球科学英文版）

基　　金：supported by Action Plan for West Development Program of the Chinese Academy of Sciences (Grant No. KZCX2-XB2-09);National Basic Research Program of China (Grant No. 2007CB714407);Na-tional Natural Science Foundation of China (Grant No.40801070)

摘　　要：Quantitative remote sensing inversion is ill-posed. The Moderate Resolution Imaging Spectroradiometer at 250 m resolution （MODIS_250m） contains two bands. To deal with this ill-posed inversion of MODIS_250m data, we propose a framework, the Multi-scale, Multi-stage, Sample-direction Dependent, Target-decisions （Multi-scale MSDT） inversion method, based on spa- tial knowledge. First, MODIS images （1 km, 500 m, 250 m） are used to extract multi-scale spatial knowledge. The inversion accuracy of MODIS_lkm data is improved by reducing the impact of spatial heterogeneity. Then, coarse-scale inversion is taken as prior knowledge for the fine scale, again by inversion. The prior knowledge is updated after each inversion step. At each scale, MODIS_lkm to MODIS_250m, the inversion is directed by the Uncertainty and Sensitivity Matrix （USM）, and the most uncertain parameters are inversed by the most sensitive data. All remote sensing data are involved in the inversion, during which multi-scale spatial knowledge is introduced, to reduce the impact of spatial heterogeneity. The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space. In the entire multi-scale inversion process field data, spatial knowledge and multi-scale remote sensing data are all involved. As the multi-scale, multi-stage inversion is gradually refined, initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced, so that the inversion becomes increasingly targeted. Finally, the method is tested by retrieving the Leaf Area Index （LAI） of the crop canopy in the Heihe River Basin. The results show that the proposed method is reliable.Quantitative remote sensing inversion is ill-posed.The Moderate Resolution Imaging Spectroradiometer at 250 m resolution(MODIS_250m) contains two bands.To deal with this ill-posed inversion of MODIS_250m data,we propose a framework,the Multi-scale,Multi-stage,Sample-direction Dependent,Target-decisions(Multi-scale MSDT) inversion method,based on spa-tial knowledge.First,MODIS images(1 km,500 m,250 m) are used to extract multi-scale spatial knowledge.The inversion accuracy of MODIS_1km data is improved by reducing the impact of spatial heterogeneity.Then,coarse-scale inversion is taken as prior knowledge for the fine scale,again by inversion.The prior knowledge is updated after each inversion step.At each scale,MODIS_1km to MODIS_250m,the inversion is directed by the Uncertainty and Sensitivity Matrix(USM),and the most uncertain parameters are inversed by the most sensitive data.All remote sensing data are involved in the inversion,during which multi-scale spatial knowledge is introduced,to reduce the impact of spatial heterogeneity.The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space.In the entire multi-scale inversion process,field data,spatial knowledge and multi-scale remote sensing data are all involved.As the multi-scale,multi-stage inversion is gradually refined,initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced,so that the inversion becomes increasingly targeted.Finally,the method is tested by retrieving the Leaf Area Index(LAI) of the crop canopy in the Heihe River Basin.The results show that the proposed method is reliable.

关 键 词：ill-posed inversion prior knowledge MSDT MULTI-SCALE 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] P208[自动化与计算机技术—计算机科学与技术]