A Simulation-Based Analysis of Topographic Effects on LAI Inversion Over Sloped Terrain

Topography significantly complicates the radiative transfer process of vegetation and further causes variation in reflectance observed by remote sensors. Leaf area index (LAI) inversion based on reflectance data is subsequently influenced by topography. Neglecting the topographic effects may lead to large biases when estimating LAI over rugged terrain. How the topography influences the LAI inversion process has rarely been explored. In this article, the topographic effects on LAI inversion over sloped terrain are quantitatively investigated and analyzed based on a dataset generated from the discrete anisotropy radiative transfer model. An artificial neural network (ANN) model is established to represent the flat surface LAI inversion algorithms. Then, the reflectance of sloped terrain is input into the ANN model to obtain the biased LAI inversion values. The results reveal that topography effects on LAI inversion are related to canopy density and generally lead to an underestimation except for sparse canopies. The mean relative bias could reach 51% when the slope angle reaches 60°. The variation trends of inverted LAI are closely related to the local incident angle. The different levels of bias in reflectance at red and near-infrared bands lead to different patterns of inversion errors for different canopies densities. Finally, we compared the existing strategies (geometric correction and topographic correction strategies) designed for LAI inversion over sloped terrain. It is found that these strategies apply in different situations. The results are helpful in understanding the topographic effects and further finding a better strategy for LAI inversion over sloped terrain.