Inversion of a New Designed ANN-Based 3-D-RTM Emulator by Continuous MCMC Technique to Monitor Crop Biophysical Properties Using Sentinel-2 Images
Achraf Makhloufi, Abdelaziz Kallel
Abstract
Precise monitoring of the crop growth is useful for agriculture management systems that allows to improve and sustain food security. Measuring the biophysical properties such as Leaf area index (LAI) and chlorophyll (Cab) content are the key to properly follow the growth cycle of crops. In this perspective, we propose an innovative approach for monitoring landscape-scale biophysical properties of wheat and barley field crops using Sentinel-2 time-series imagery based on accurate 3D direct and inverse radiative transfer modeling. It relies on an original 3D radiative transfer model emulator architecture based on a residual artificial neural network (ResNet). We employed the well-known Discrete Anisotropic Radiative Transfer (DART) model to simulate Sentinel-2 images and generate a database that serves to train and validate the ResNet emulator. To do it, two realistic mockups both composed of soil and 3D wheat plants are set up. The first mockup mimics the wheat and barley from their start of growth to their full growth and the second reproduce the features of the wheat and barley plant when they turn yellowish and mature for harvesting. Besides, we extract the bare soil spectra signature from Sentinel-2 images acquired before sowing. Results of the proposed emulator show its similarity to DART simulation while it is much faster. These emulator features let it possible to adapt an inversion technique based on the Markov Chain Monte Carlo (MCMC), specifically, as novelty, a continuous MCMC. The latter exploits the emulator to perform the iterative sampling processing on all continuous values in allowed intervals according to each sought biophysical property, until converging to its respective posterior distribution. The proposed scheme of RTM Emulator inversion based on continues MCMC and using Sentinel-2 Imagery is called REMI. It ensures high retrieval performances which reach a RMSE equal to 0.06 and 0.1 for LAI and Cab, respectively.