Statistical characterization and exploitation of Synthetic Aperture radar vegetation indexes for the generation of Leaf area Index time series

This study investigates the efficacy of Synthetic Aperture Radar (SAR)-based vegetation indexes for filling gaps in the optical-driven Leaf Area Index (LAI) time series. The statistical properties of coherent (e.g., interferometric coherence) and incoherent (e.g., backscattered signal) SAR vegetation indexes are systematically studied, including their correlation with LAI measurements and significance for LAI reconstruction. First, the Multi-Output Gaussian Process (MOGP) algorithm is selected, analyzed, and subsequently improved to handle the non-Gaussian distribution of the exploited SAR indexes. Hence, a refined MOGP method incorporating a quantile-transform (QT) operation is proposed. Experiments focus on the Arborea zone in Sardinia, Italy, exploiting one year of optical and radar images from the European Copernicus Sentinel-1/2 sensors. The results prove the usefulness of the refined MOGP model in obtaining LAI time series with reduced uncertainties (R2?=?0.9/0.7 training/validation) and filling gaps in optical-based LAI observations, reconstructing feasible crop dynamic along the season. The study also provides insights into phenological state evolution and implications for future applications of the presented method.