Multi-scale MSDT inversion based on LAI spatial knowledge

引用方式: Zhu XH, Feng XM, Zhao YS. Multi-scale MSDT inversion based on LAI spatial knowledge. Science China Earth Sciences, 2012, 55(8): 1297-1305.

标题 Multi-scale MSDT inversion based on LAI spatial knowledge
年份 2012
出版社 Science China Earth Sciences
语言 en
DOI 10.1007/s11430-011-4312-0
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 spatial 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.
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