Dynamic Retrieval of Olive Tree Properties Using Bayesian Model and Sentinel-2 Images

Abstract

The goal of this study is to provide a fine detection and monitoring of olive orchard trees over large areas to anticipate any damage. We developed an original method to assess the spatiotemporal dynamics of biophysical parameters in the olive orchards using satellite observations and radiative transfer models. Sentinel-2 time-series data collected over a four-year period were fused with Planet images from the same time period to enhance the temporal trends in olive orchards in the Sfax region located in southern Tunisia. These images also served to extract soil spectrum variations required by the 3-D discrete anisotropic radiative transfer model to account for canopy background effect. As a backward model, we developed an original technique based on the Markov chain Monte Carlo method that has the advantage of being able to model sensor noise and account for spatial and temporal regularization. It allows retrieving key parameters such as leaf area index (LAI), chlorophyll content, water content, and mesophyll structure. Taking advantage of 1) the Sentinel-2 images downscaled to a moderate resolution of 80 m to ensure representative pixels of the local mixing (i.e., trees and soil); 2) the appropriate soil signature derived from high spatial and spectral resolution image; and 3) the accuracy of the direct and inverse modeling, it was possible to retrieve the plant properties even when LAI values are less than 0.14. Indeed, our inversion results show that the estimated parameters are strongly correlated especially with the LAI field measurements with $R^{2}=0.9937$ .