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In water-scarce regions, effective water resource management is crucial for sustainable agriculture. Scientists and decision-makers are working to address issues of resource conservation and agricultural productivity, with a growing interest in coupling hydrological and crop models. The current trend of interest seems to be limited to the improvement of crop system performance and environmental impact assessment, but attention also needs to be paid to sustainable crop production and water management concerns. Driven by these needs, in the framework of I4DP-SCIENCE program, the Italian Space Agency (ASI) supports the ambitious collaborative project THETIS (Earth Observation for the Early forecasT of Irrigation needS; Agreement n. 2023-52-HH.0), involving the National Research Council Institute for Electromagnetic Sensing of the Environment (CNR-IREA), the Council for Agricultural Research and Economics (CREA), the Polytechnic of Bari, the University of Bari, and the Reclamation Consortium of the Capitanata, Foggia, Italy. The project focuses on the early assessment and forecasting of irrigation needs in the Fortore irrigation district in the Apulian Tavoliere (Southern Italy). THETIS aims to develop a Spatial Decision Support System (SDSS) integrating hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for the efficient planning of irrigation resources for three different use cases (i.e., early forecasting, irrigation start and mid-season estimation) for different target crops. The project architecture significantly relies on the use of EO derived products obtained through the integrated use of Synthetic Aperture Radar (SAR), multispectral and hyperspectral data. They serve the purpose of describing land surface processes and represent crucial parameters for hydrological and crop growth model constraints. Specifically, the calibration of the hydrological model spans from summer 2021 to autumn 2022. The subsequent phase will include a validation phase (year 2023) and an operational phase to estimate water use for the upcoming irrigation season. The validation of the model outputs includes the comparison of the estimated water demand with the actual irrigation volumes applied by the Consortium. A primary focus of the proposed architecture lies in generating time-series estimates of root zone soil moisture, essential for defining the initial conditions in the crop growth model AquaCrop which plays a pivotal role in managing the water balance at the field scale in the areas relevant to irrigation needs assessment. To achieve this goal, the project aims to integrate, for the first time, a revised version of the well-known daily basin-scale hydrological model DREAM with the physically based Soil Moisture Accounting and Routing (SMAR) model. This work concerns data selection and assessment for calibration and validation phases of the basin-scale hydrological model and the SMAR model. They include sparse daily field measurements and satellite data retrievals. Although field monitoring remains essential, preliminary results regarding the use of satellite-derived and downscaled products for a proper model calibration are encouraging. The proposed approach shows promise for providing insights into soil dynamics for operational implementation, supporting advances in sustainable agricultural techniques and rational water resource management in semi-arid environments.
Vito Iacobellis (Mon,) studied this question.