Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.