Salinity stress and water availability inferred from satellite leaf area index assimilated into a water-energy-crop model

Purpose: Climate change, increasing aridity, water scarcity and population growth, enhancing food demand and irrigated land expansion, are expected to increase the extent of salinity-affected areas. This study aims to combine the crop-energy-water balance model FEST-EWB-SAFY with Leaf Area Index (LAI) and Land Surface Temperature (LST) data from remote sensing to monitor maize development in a field with a shallow water table and highly affected by salinity. Methods: The FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB model, which computes time-continuous soil moisture and evapotranspiration, and the SAFY crop model for yield prediction. The model was employed in synergy with satellite observations of LST and LAI. LST was used for the calibration/validation of the water and energy balances, whereas LAI was used both for the calibration of crop parameters and a data assimilation scheme. Results: The data assimilation scheme was able to reproduce the observed spatial heterogeneity in crop development, associated to the uneven water table depth and salinity distribution, as these effects were picked up from satellite. A good correspondence was also found between modelled yield and the distributed samplings from a combined harvester equipped with a yield monitor. Conclusion: The results, comparable to those obtained with the a posteriori calibration, show that data assimilation of remote sensing observations allow to improve the model as the agricultural season progresses, including information which is difficult to monitor continuously in-situ.