Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework - Archive ouverte HAL Access content directly
Journal Articles Remote Sensing of Environment Year : 2022

Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework

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Abstract

The objective of this study is to evaluate the performances of a semi-empirical approach based on the Bayesian theory to retrieve Green Area Index (GAI) from multiple decametric satellites. It is designed to overcome some limitations in existing Radiative Transfer Model (RTM) inversion methods, including the high dimensionality of the inverse problem, the convergence problem due to possible equifinality, and the dependence of some RTM variables on the crop-specific architecture. The PROSAIL model is first inverted in a calibration step using the Hamiltonian Monte Carlo (HMC) algorithm over a global dataset of ground GAI measurements (for maize, wheat, and rice) and the corresponding reflectance observations from Landsat-8, Sentinel-2, and Quickbird to derive crop-specific distributions of PROSAIL input variables. These distributions were then used as prior information to predict GAI over an independent set of reflectance observations. Results show that the full Bayesian approach provides close estimates of GAI to ground truth, with respective Root Mean Square Error (RMSE) of 1.01, 1.33, and 0.97 for maize, wheat, and rice (R2=0.67, 0.76 and 0.63, respectively). The performances are better than those approaches generally reported using radiative transfer models that are non-crop-specific, like the SNAP algorithm for Sentinel-2, but are slightly behind the purely empirical models based on machine learning. However, the proposed approach provides an explicit insight of the joint distribution of PROSAIL variables that are valid for any satellite platform. This constitutes a major advantage against purely empirical models, as it enables to fully exploit large observational datasets from multiple sensors and generalize to other platforms.
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Dates and versions

hal-03763254 , version 1 (29-08-2022)

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Cite

Jingwen Wang, Raul Lopez-Lozano, Marie Weiss, Samuel Buis, Wenjuan Li, et al.. Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework. Remote Sensing of Environment, 2022, 278, pp.113085. ⟨10.1016/j.rse.2022.113085⟩. ⟨hal-03763254⟩
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