Multi-scale, multi-stage inversion method for retrieval of LAI

Aim at the ill-posedness of vegetation biophysical variables inversion problems, the paper presents a multi-scale, multistage (MSMS) inversion approach based on field data, multi-resolution remotely sensed observations and spatial knowledge for estimating crop leaf area index (LAI). The proposed MSMS inversion method takes advantage of multiple stages inversion strategy and prior information. Firstly, Hyperion data (30 meter) is upscaled to 300 meters and 3000 meters for establishing a multi-scale data series. Secondly, a multiple scale inversion frame is constructed to update the prior knowledge by using coarse scale inversion results as the prior information for middle scale inversion process. Thirdly, the spatial information, extracted by Taylor expansion method, is applied to reduce the influence of spatial heterogeneity on LAI retrieval. At last, a multiple stage inversion process is established based on uncertainty and sensibility matrix (USM) to realize the reasonable distribution of limited remote sensing observation in the model inversion, with which the most uncertainty parameters will be retrieved from the most sensibility remote sensing data. The experiment results indicate that the methodology proposed in this paper is reasonable and accurate for LAI estimation.