Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

Topographic correction methods rarely consider the canopy parameter effects directly and explicitly for sloping canopies. In order to address this problem, the topographic correction method MFM-GOST2 was developed by implementing the second version of the Geometric-Optical model for Sloping Terrains (the GOST2 model) in the multiple forward mode (MFM) inversion framework. First, a look up table (LUT) was constructed by multiple forward modeling of the GOST2 model; second, the radiance of a remotely sensed image and its corresponding topographic data were used for searching potential canopy parameter combinations from the LUT; and third, the corrected radiance was determined by averaging potential radiances of horizontal canopies from the LUT according to the canopy parameter combinations. The MFM-GOST2 and twelve generally used topographic correction methods were evaluated via a case study by visual analysis, linear relationship analysis, and the rose diagram analysis. The result showed that the MFM-GOST2 method successfully removed most of the topographic effects of a subset image of the Landsat-8 image in a case study. The case study also illustrates that the rose diagram analysis is a good way to evaluate topographic corrections, but the linear relationship analysis cannot be used independently for the evaluations because the decorrelation is not a sufficient condition to determine a successful topographic correction.