Use of Sar Based Regressors for Leaf Area Index (Lai) Spatial/Temporal Filling: a Machine Learning (Ml)-Based Outlook

This study investigates the efficacy of incoherent and coherent SAR descriptors for filling spatial and temporal gaps in optical-driven Leaf Area Index (LAI) time series. Within this context, an artificial intelligence (AI) algorithm based on Multi-Output Gaussian Process (MOGP) [1], [2] demonstrated its effectiveness in handling the different information derived from SAR signatures in a unified corpus. The study utilizes sequences of Sentinel-2 imagery to derive Leaf Area Index (LAI) maps, while Sentinel-1 observations over the same area are utilized to obtain SAR backscatter coefficients and interferometric coherence data. This comprehensive dataset is then employed as input for training the MOGP model. Experimental tests demonstrate the usefulness of the MOGP model in obtaining accurate LAI time series even during very cloudy periods.