LESS: Large-scale remote sensing data and image simulation framework over Vegetated Areas

Three-dimensional (3D) radiative transfer (RT) modeling and simulation of the transport of radiation through earth surfaces is a challenging and difficult task. The difficulties lie in the complexity of the landscapes and also the intensive computational cost of 3D RT simulations. Current models usually work with abstract landscape elements to reduce complexity or only consider relatively small realistic scenes. In this study, a new 3D RT modeling framework (called LESS) is proposed. It employs a forward photon tracing method to simulate bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km²) spectral images from visible to thermal infrared band. In this framework, a graphic user interface (GUI) and a set of tools are also provided to help to construct the landscape and set parameters, e.g., extracting tree crowns from airborne LiDAR data, which makes it more accessible to common users. The accuracy of LESS is evaluated with other models and field measurements in terms of directional BRF and pixel-wise comparisons. It shows that the accuracy of LESS is consistent with the reference models from RAMI model inter-comparison website (http://rami-benchmark.jrc.ec.europa.eu/HTML/Home.php) as well as field measurements. LESS has also been extended to simulate atmosphere, LiDAR and in-situ sensors. It provides as a useful tool for studying the radiative transfer process over complex forest canopies from leaf to canopy scales. The simulated datasets can be used as benchmarks for validating other physical remote sensing inversion algorithm and developing parameterized models for retrieving bio-geophysical variables of canopy. LESS can be accessed from http://lessrt.org.