Abstract
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 is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting Tc from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R2 ≥ 0.91 for LAI ≤ 4 m2.m−2). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated Tc from these models with the previous predicted Tc over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: (1) the global tree geometry shape (mean bias up to 21.5%), (2) the transmittance fraction associated to multiple scattering, Tscat (maximum bias up to 13%), and (3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting Tc leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to Tscat. The transmittance fraction associated to the non-intercepted transmitted light, Tdir, can reach up to 95% in the SWIR, and Tscat up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate Tdir and Tscat spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.
Highlights
Its performance is assessed through the study of the tree crown transmittance Tc, when predicted or simulated from different levels of abstraction of tree models built from TLS measurements
The results derived from this preliminary and non-exhaustive study aim at giving the orders of magnitude of the sources of error to estimate Tc correctly, and new ways of improvements for atmospheric correction codes to deal with reflectance retrieval in tree shadows
We conclude that a statistical regression coming from a simplified empirical tree model showed good performance in predicting Tc (RMSE ≤ 4.3%, R2 ≥ 0.91), it globally fails when compared to tree models from more realistic scenarios because in order of importance: (1) the global tree geometry shape has not been taken into account, (2) Tscat is always underestimated, and (3) the degree of realism for the 3D
Summary
2021, 13, 931 land cover mapping, vegetation, and soil monitoring in both urban and natural ecosystems [1,2,3]. Their derivation requires accurate surface reflectance retrieval. Erroneous surface reflectances lead to incorrect calculation of vegetation indices and, later, to a spurious assessment of vegetation biochemical and biophysical properties [4,5,6]. In an urban context composed of buildings and a tree-covered park, Adeline et al [7] showed that the proportion of shadows can account around between 20%
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