Irrigation scheduling of tomato crop by combining Sentinel-2 imagery with an agro-hydrological model

This study explored the possibility to optimize irrigation scheduling through the integrated use of crop data derived from multispectral satellite imagery and an agro-hydrological model. The study was conducted with reference to an industrial tomato crop in an irrigated open field. Three methods for estimating irrigation needs were compared: estimates obtained with a calibrated AquaCrop model; estimates obtained by applying the AquaCrop model with sequential assimilation of crop cover retrieved from multispectral images; estimates obtained with the IRRISAT irrigation advisory service, based only crop state parameters retrieved from satellite multispectral images. The results confirm the usefulness of integrating agro-hydrological models and satellite observations to improve the prediction of crop water requirements. The agro-hydrological model offers more reliable estimates of the water irrigation requirements in the early stages of crop development, being able to simulate the effect of evaporative losses from the soil, when the canopy cover is still small. On the other hand, satellite data allows reducing model simulation errors in the most advanced stages of crop development and during senescence.