Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge deal with tree shadows using atmospheric correction models if crown transmittance Tc not properly taken into account. This requires knowledge complex radiation mechanisms that occur in crowns, which can be provided by coupling modeling canopy radiative transfer codes (here DART) 3D representations trees. First this study, a sensitivity analysis carried out on DART simulations empirical model led statistical regression predicting from leaf area index (LAI) solar zenith angle good performances (RMSE ? 4.3% R2 ? 0.91 LAI 4 m2.m?2). Secondly, more realistic voxel-grid derived terrestrial LiDAR measurements over two trees were considered. The comparison DART-simulated these previous predicted 0.4–2.5 µm showed three main sources inaccuracy quoted order importance: (1) global geometry shape (mean bias up 21.5%), (2) fraction associated multiple scattering, Tscat (maximum 13%), (3) degree realism representation 7.5%). Results neglecting leads very inaccurate > 0.04), particularly background high, near shortwave infrared – NIR SWIR due Tscat. non-intercepted transmitted light, Tdir, reach 95% SWIR, 20% NIR. Their contributions computed shadow have maximum dispersion 27% 8% respectively. Investigating how approximate Tdir spectral variability along most appropriate crucial improve when models.