Integration Of Remote Sensing and Crop Modelling for the Assessment of Tomato Water Requirements

In the last years the development and the application of precision farming techniques has constantly increased with the aim of stabilizing crop yield and minimizing in-field variability, while reducing the footprint of agriculture. In this context, the increased offer of freely available remote sensing information at high spatial resolution (10-20 m) and frequent revisiting time (5 days) is boosting scientific research and innovation. In particular, the integration of remote sensing data into crop growth models is rapidly increasing for operational applications, such as monitoring crop nutrition, managing site specific fertilization, and driving irrigation. The state of the art, in fact, suggests that significant progresses in saving irrigation volumes at farm level can be attained by assessing crop water requirements through an optimal combination of satellite imagery with crop models since the first provide information concerning the current state and the second is able to simulate the biophysical processes of the growing crop. The aim of this research was to investigate the possibility to improve crop water requirement predictions by assimilating the fraction of vegetation cover (Fvc) estimated by Sentinel-2 imagery into the AquaCrop model.