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Water scarcity is a growing social, economic, and political issue, especially in Southern European countries that are becoming even more arid and where different crops can be cultivated only if irrigation is possible. In this context, strategies to enhance water use efficiency are regarded as critical from both an economic and an environmental standpoint. The present work aims to analyse water use efficiency and productivity of processing tomato in Apulia region of Southern Italy. Specifically, the study examines the potential enhancements in economic and physical water productivity through the simulation of the fully coupled FEST-EWB-SAFY model, a hydrological crop model that estimates the optimal water requirements for irrigation using satellite and ground data. The model's estimates suggest that plants require significantly less water than that provided by conventional irrigation systems. The simulations also suggest that information technology, when combined with irrigation water-saving techniques, can lead to a reduction in water waste, an increase in water productivity, and lower incidence of water costs. Policy interventions should integrate water efficiency into existing regulatory measures and promote better water usage planning through the adoption of smart delivery systems aimed at supplying water only when necessary and at optimal volumes. [ABSTRACT FROM AUTHOR]

Drought prediction is crucial especially where the rainfall regime is irregular and agriculture is mainly based on irrigated crops, such as in Mediterranean countries. In this work, the main objective is to develop an EO-based agricultural drought monitoring system (ADMOS) for operative irrigation networks management at different spatial and temporal scales. Different levels of drought are identified based on an integrated indicator combining anomalies of rainfall, soil moisture, land surface temperature and vegetation indices, allowing to consider the different droughts types and their timing looking on the end-user's perspective. Multiple remote sensing data, which differ on sensing techniques, spatial and temporal resolutions and electromagnetic frequencies, are used for each anomaly computation. The analyses have been performed over two Irrigation Consortia in Italy (the Chiese and Capitanata ones), which differ for climate, irrigation volumes and techniques, and crop types. The obtained results show a negative correlation between cumulated ADMOS and the irrigation volumes in the Capitanata area, while in the Chiese Consortium a zero correlation is obtained with an almost constant amount of irrigation volumes provided to the crops every year independently from the drought condition. In both areas, crop yields seem to be almost uncorrelated to the drought index, as production is highly sustained by irrigation. Moreover, discrepancies on the anomalies sign is observed, especially when soil moisture is considered. The results also clearly show that asynchronies may exist especially between soil moisture anomalies and vegetation or land surface temperature anomalies. [ABSTRACT FROM AUTHOR]

Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change, and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-based inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporative efficiency computed based on a MODIS remote sensing dataset and simplified analytical model, while vegetation growth was expressed through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data hosting facility. The results indicate that the integration of process-based features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semi-arid areas, for instance, performance enhancement was observed when the evaporative efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied on observation sites in Tunisia, Italy and South-India that are not part of ISMN. The results reveal that joint use of surface soil moisture, evaporative efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-based features were integrated into the ANN models in addition to SSM. However, process-based features have no to little added value in temperate to tropical conditions. [ABSTRACT FROM AUTHOR]