Estimation of Olive Tree Properties from Satellite Images using Variational Inversion of an ANN based Emulator of a Radiative Transfer Model

In Tunisia, olive tree cultivation is a significant agricultural asset. Ensuring the sustainability and optimal yield, both in terms of quality and quantity, of these tree orchards is therefore crucial. It needs careful monitoring due to its susceptibility to various anomalies, water stress, and nutrient deficiency. This can be achieved by observing the biophysical properties of the trees, such as chlorophyll content (Cab) and leaf area index (LAI), to assess their growth and health. This is accomplished by utilizing both time-series imagery from the high spectral resolution Sentinel-2 and high spatial resolution Pleiades sensors. Establishing the relationship between the images from one side and the biophysical properties from the other requires the inversion of radiative transfer models (RTM). RTM are time-consuming, thus their inversion is impractical. Our approach involves designing a fast RTM emulator based on an artificial neural network (ANN). Our inversion technique is based on the multi-scale variational approach to accurately retrieve the required properties from the satellite image time series. It allows convergence towards the global optimal solution since it is able to avoid local optima. The suggested approach for inverting RTM Emulator promises superior retrieval performance, achieving remarkably low RMSE values of 0.03 and $1.57\mu g|cm^{2}$ for LAI and Cab, respectively.