A New Forest Leaf Area Index Retrieval Algorithm Over Slope Surface

In this study, a novel algorithm for high spatial resolution leaf area index (LAI) retrieval, specifically tailored for mountain forests, has been developed. As an essential climate variable, LAI has been incorporated into many ecohydrological process simulation models; however, the majority of the algorithms are developed on the assumption of flat terrain. Previous studies have proved that neglecting the influence of topography may introduce significant biases and uncertainties into LAI estimates particularly in rugged areas. As an important species in the mountain area, forests occupy a large land area worldwide; nevertheless, it is still challenging to obtain high-quality LAIs from satellite images due to their complex canopy structures. In spite of numerous attempts having been made to address such issues with topographic correction (TC) or mountain canopy reflectance models, few algorithms were actually available for LAI estimation of mountain forests. Here, we try to employ the geometric optical and mutual shadowing and scattering from the arbitrarily inclined-leaves model coupled with the topography (GOSAILT) model to retrieve forest LAI over complex terrain. GOSAILT is a combined model that incorporates the radiative transfer model (RTM) into the geometrical optical model (GOM) on the slope surface. It is capable of characterizing the bidirectional reflectance of both discrete and continuous canopies. The validations against computer-simulated LAIs reveal root-mean square errors (RMSEs) being 1.7160 and 0.6260, corresponding to terrain-ignored scenario and terrain-considered scenario, respectively. Besides, the validation against in situ LAIs demonstrated that the RMSE is 0.9262 over flat terrain and 0.6402 over sloped terrain. This evidence underscores the robust performance of the newly developed algorithm.