| Summary: | This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite (e.g., PRISMA, EnMAP, and GF-5). Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index (LAI) and Leaf chlorophyll content (Cab) were evaluated: non-kernel-based and kernel-based Machine Learning Regression Algorithms (MLRA); Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season. Results show that for Sentinel-2 data, Gaussian Process Regression (GPR) was the best performing algorithm for both LAI (R2=0.89 and RMSE=0.59) and Cab (R2=0.70 and RMSE=8.31). Whereas, for PRISMA simulated data the Kernel Ridge Regression (KRR) was the best performing algorithm among all the other MLRA (R2=0.91 and RMSE=0.51) for LAI and (R2=0.83 and RMSE=6.09) for Cab, respectively. Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands, which are used as tie-points in the PROSAIL inversion, are extremely useful for an accurate retrieving of crop biophysical parameters.
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