Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery

The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop–water balance models, thereby improving crop water requirements (CWR) yield estimates from field regional scale. Satellite imagery weather prediction outputs offer high resolution (in time space) gridded that can compensate paucity parameter measurements ground observations, required assessments CWR yield. In this study, AquaCrop model was used assess tomato on a farm Southern Italy by assimilating Sentinel-2 (S2) canopy cover using CM-SAF satellite-based radiation ERA5-Land reanalysis forcing data. accuracy evaluated with collected during irrigation season (April–July) 2021. differed RMSE about 11%. predictions were compared actual regarding volumes harvested results showed S2 parameters represent added value, since their assimilation into growth models improved estimates. Reliable be achieved combining databases model.