Modeling the hotspot effect for vegetation canopies based on path length distribution

Abstract

The hotspot effect, which refers to the increased reflectance in the solar direction, relies on the canopy structure. Numerous bi-directional reflectance distribution function (BRDF) models have been developed with careful attention to the hotspot effect. However, different assumptions may lead to very different shapes of BRDF, especially in the hotspot direction. With the development of high-resolution remote sensing satellites and three-dimensional (3D) mapping technologies, abundant 3D structural vegetation canopy information is available. However, traditional analytical models might not fully use these kinds of information. As a result, most of them are still very limited by their assumptions, such as the shape and spatial distribution of crowns. One BRDF model may do well only for one vegetation type and shows significant errors under other scenarios. We propose a new model named PATH_RT, which can model the hotspot effect at both the leaf and crown levels using the path length distribution (PLD). PLD can be easily obtained with the 3D information of the canopy at given remote sensing geometries. We validated the bi-directional reflectance factor (BRF) predicted by the PATH_RT model with the simulated scenes and field measurements which were produced by a 3D radiative transfer model LESS and UAV observations respectively. Compared to traditional analytical models SAILH, 4SAIL2, GORT, and 5SCALE, the PATH_RT model exhibits the highest accuracy, reducing the average RMSE by 86%, 66%, 52%, and 58% for the hotspot region respectively. PATH_RT accurately depicts the hotspot effects at the canopy and leaf levels and is highly efficient (99%, 75%, 86%, 99%, and 99% less time spent for one repeat run compared to LESS, SAILH, 4SAIL2, GORT, and 5SCALE models, respectively). Considering its high accuracy and efficiency, PATH_RT is expected to improve the accuracy and efficiency of remote sensing inversion.