Modifying geometric-optical bidirectional reflectance model for direct inversion of forest canopy leaf area index

Abstract

Forest canopy leaf area index (LAI) inversion based on remote sensing data is an important method to obtain LAI. Currently, the most widely-used model to achieve forest canopy structure parameters is the Li-Strahler geometric-optical bidirectional reflectance model, by considering the effect of crown shape and mutual shadowing, which is referred to as the GOMS model. However, it is difficult to retrieve LAI through the GOMS model directly because LAI is not a fundamental parameter of the model. In this study, a gap probability model was used to obtain the relationship between the canopy structure parameter nR 2 and LAI. Thus, LAI was introduced into the GOMS model as an independent variable by replacing nR 2 The modified GOMS (MGOMS) model was validated by application to Dayekou in the Heihe River Basin of China. The LAI retrieved using the MGOMS model with optical multi-angle remote sensing data, high spatial resolution images and field-measured data was in good agreement with the field-measured LAI, with an R-square (R 2) of 0.64, and an RMSE of 0.67. The results demonstrate that the MGOMS model obtained by replacing the canopy structure parameter nR 2 of the GOMS model with LAI can be used to invert LAI directly and precisely.