Forcing the SAFY Dynamic Crop Growth Model with Sentinel-2 LAI Estimates and Weather Inputs from AgERA5 Reanalysis and CM SAF SARAH-3 Radiation Data for Estimating Crop Water Requirements and Yield
Year: 2025
Type: article
Abstract: The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental for estimating crop water requirements (CWR) and yield. This study used the latest reanalysis dataset, AgERA5, combined with the up-to-date CM SAF SARAH-3 Satellite-Based Radiation Data as meteorological inputs of the SAFY dynamic crop growth model and a one-step evapotranspiration formula for CWR and yield estimates at the farm scale of tomato crops. The Sentinel-2 (S2) estimates of Leaf Area Index (LAI) were used to force the SAFY model as soon as they became available during the growing stage, according to the satellite passages over the area of interest. The SAFY model was calibrated with ground-based weather observations and S2 LAI data on tomato crops that were collected in several farms in Campania Region (Southern Italy) during the irrigation season, which spans from April to August. To validate the method, the model estimates were compared with field observations of irrigation volumes and harvested yield from a monitored farm in the same region for the year 2021. Results demonstrated that integrating AgERA5 and CM SAF weather datasets with S2 imagery for assimilation into the SAFY model enables accurate estimates of both CWR and yield.
Source: Remote Sensing
DOI: 10.3390/rs17142464
Institutions: University of Salerno, University of Naples Federico II
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Climate action
Open Access status: gold
Estimated APC paid: $3,258
Award: G972786147
OpenAlex ID: W4412476873