Bayesian inversion technique of olive tree biophysical properties using Sentinel-2 images

Abstract

In this paper, we study the estimation of olive tree biophysical properties driven by Sentinel-2 (S2) image inversion. The latter is based on the forward/backward radiative transfer model (RTM). The forward step is done simulating the DART model on a realistic olive tree mock-up, whereas the backward is done based on a coupling between the Look UP Table (LUT) and the Markov Chain Monte Carlo (MCMC). The parameters Leaf area index (LAI), chlorophyll (Cab) water (Cw) contents and mesophyll structure (N) are therefore derived. Soil reflectance is pre-calculated based on an upscaling of the S2 resolution to 3m using Planet images. Moreover to obtain a significant representation of the local heterogeneity, S2 are upscaled to the 80m resolution. The estimation results are promising.