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Article Dans Une Revue Remote Sensing of Environment Année : 2024

3D Monte Carlo differentiable radiative transfer with DART

Résumé

Understanding the sensitivity of remote sensing (RS) observation to land surface parameters (e.g., reflectance and temperature) is very important for estimating the accuracy of RS products and optimizing inversion algorithms. The most precise method for quantifying this sensitivity is physical modelling of derivative propagation in simulated 3D landscapes. However, to our knowledge, present land surface radiative transfer models (RTM) do not simulate derivative propagation. This paper proposes an original “differentiable radiative transfer modelling” that simulates the derivative propagation in natural and urban landscapes, for reflectance. It is integrated in the framework of DART RTM. We validated it both analytically and with a finite difference method applied to a 3D landscape. This new modelling extends the efficiency of 3D RTMs for sensitivity studies. It is implemented in the DART version freely available for research and education (https://dart.omp.eu).
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Dates et versions

hal-04643492 , version 1 (10-07-2024)

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Yingjie Wang, Abdelaziz Kallel, Zhijun Zhen, Nicolas Lauret, Jordan Guilleux, et al.. 3D Monte Carlo differentiable radiative transfer with DART. Remote Sensing of Environment, 2024, 308, pp.114201. ⟨10.1016/j.rse.2024.114201⟩. ⟨hal-04643492⟩
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