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[Background and aims]: The ever-increasing demand of fresh water from irrigated agriculture and the water scarcity condition that characterizes the Mediterranean region, encourages the adoption of efficient water saving strategies. In this study, the electrical resistivity imaging (ERI) technique was applied for characterizing the mass exchange mechanisms affecting the continuous soil-plant system of heterogeneous micro-irrigated orchards., [Methods]: Time-lapse ERI surveys were carried out in a multi-variety and mixed-age orange orchard located in Eastern Sicily (insular Italy). The time-lapse ERI monitoring was coupled with the use of independent soil water content (SWC) measurements., [Results and conclusions]: A clear relationship between the soil electrical resistivity (ER) and the measured SWC changes was identified in the soil under the mixed-age orchard (with an overall coefficient of determination value of 0.63). Specifically, this study highlights the dependency of SWC dynamics as function of tree age. Overall, an increase in ER was obtained in correspondence to the soil domain where the younger trees were located ( < = 3 years). This behaviour was inferred to result from the higher soil evaporation process active at these locations, due to less vegetation cover of the younger trees and, consequently to their lesser root extension in comparison to the mature trees ( > = 8 years). On the other hand, in mature trees, characterized by denser root biomass, there were more evident decreasing patterns of ER (i.e., greater changes in SWC), due to greater transpiration rates that caused lower initial SWC under these conditions in comparison to the younger trees.

Under the current water scarcity scenario, the promotion of water saving strategies is essential for improving the sustainability of the irrigated agriculture. In particular, high resolution irrigated area maps are required for better understanding water uses and supporting water management authorities. The main purpose of this study was to provide a stand-alone remote sensing (RS) methodology for mapping irrigated areas. Specifically, an unsupervised classification approach on Normalized Difference Vegetation Index (NDVI) data was coupled with the OPtical TRApezoid Model (OPTRAM) for detecting actual irrigated areas without the use of any reference data. The proposed methodology was firstly applied and validated at the Marchfeld Cropland region (Austria) during the irrigation season 2021, showing a good agreement with an overall accuracy of 70%. Secondly, it was applied at the irrigation district Quota 102,50 (Italy) for the irrigation seasons 2019–2020. The results of the latter were instead compared with the data declared by the Reclamation Consortium, finding an overestimation of irrigated areas of 21%. In conclusion, this study suggests an easy-to-use approach, eventually independent of reference data such as agricultural statistical surveys or records and replicable under different agricultural settings in continental or Mediterranean climates to support stakeholders for regular estimation of irrigated areas in different growing years or detecting eventual unauthorized water uses. However, some uncertainties should be considered, needing further analyses for improving the accuracy of the proposed approach.

Study region: The study region is represented by seven irrigation districts distributed under different climate and topography conditions in Italy. Study focus: This study explores the reliability and consistency of the global ERA5 single levels and ERA5-Land reanalysis datasets in predicting the main agrometeorological estimates commonly used for crop water requirements calculation. In particular, the reanalysis data was compared, variable-by-variable (e.g., solar radiation, R; air temperature, T; relative humidity, RH; wind speed, u; reference evapotranspiration, ET), with in situ agrometeorological observations obtained from 66 automatic weather stations (2008–2020). In addition, the presence of a climate-dependency on their accuracy was assessed at the different irrigation districts. New hydrological insights for the region: A general good agreement was obtained between observed and reanalysis agrometeorological variables at both daily and seasonal scales. The best performance was obtained for T, followed by RH, R, and u for both reanalysis datasets, especially under temperate climate conditions. These performances were translated into slightly higher accuracy of ET estimates by ERA5-Land product, confirming the potential of using reanalysis datasets as an alternative data source for retrieving the ET and overcoming the unavailability of observed agrometeorological data.

Precipitation data availability plays a crucial role in many climatic, hydrological and agricultural-related applications. In this study, the use of alternative data sources (i.e. interpolation methods and ERA5-Land reanalysis data) was combined for improving the spatially distributed precipitation estimates at the Simeto river basin, located in Eastern Sicily (Italy). A total of 51 rain gauges were used to generate a monthly precipitation dataset for the reference period 2002–2019. Among the 6 tested interpolation methods, Natural Neighbour was the method that predicted precipitation the best at monthly level with a Distance between Indices of Simulation and Observation (DISO) of 0.51. Radial Basis Functions and Inverse Distance Weighting provided the highest precipitation accuracies, respectively, for winter and autumn (with DISO values of 0.44 and 0.50, respectively), and for spring and summer seasons (with DISO values of 0.50 and 0.63, respectively). Underestimations on the ERA5-Land precipitation estimates were observed when compared to the most accurate interpolation methods both at monthly (25%) and seasonal temporal scales (21% in winter and summer, 36% in autumn), with the exception for spring. The performance was significantly improved when the interpolation estimates were corrected with local observations (with RMSD values ranging from 35.29 mm to 26.46 mm at monthly scale, and from 23.33–55.34 mm to 23.15–37.88 mm at seasonal level). The spatial distribution of the estimation errors associated to precipitation obtained from ERA5-Land reanalysis revealed a significant positive correlation (p value <0.05) with the altitude variation in each ERA5-Land cell within the basin under study. These results confirm the good performance on the combined use of alternative precipitation data sources, while adjustments are required to reduce site-specific uncertainties due to local microclimatic conditions occurring at the basin scale.

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes. The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes. Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions. The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

The use of hydrogeophysical methods provides insights for supporting optimal irrigation design and management. In the present study, the electrical resistivity imaging (ERI) was applied for monitoring the soil water motion patterns resulting from the adoption of water deficit scenarios in a micro-irrigated orange orchard (Eastern Sicily, Italy). The relationship of ERI with independent ancillary data of soil water content (SWC), plant transpiration (T) and in situ measurements of hydraulic conductivity at saturation (K, i.e., using the falling head method, FH) was evaluated. The soil water motion patterns and the maximum wet depths in the soil profile identified by ERI were quite dependent on SWC (R = 0.79 and 0.82, respectively). Moreover, ERI was able to detect T in the severe deficit irrigation treatment (electrical resistivity increases of about 20%), whereas this phenomenon was masked at higher SWC conditions. K rates derived from ERI and FH approaches revealed different patterns and magnitudes among the irrigation treatments, as consequence of their different measurement scales and the methodological specificity. Finally, ERI has been proved suitable for identifying the soil wetting/drying patterns and the geometrical characteristics of wet bulbs, which represent some of the most influential variables for the optimal design and management of micro-irrigation systems.

The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R2 from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and w

The technological advances of remote sensing (RS) have allowed its use in a number of fields of application including plant disease depiction. In this study, an RS approach based on an 18-year (i.e., 2001–2018) time-series analysis of Normalized Difference Vegetation Index (NDVI) data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and processed with TIMESAT free software, was applied in Sicily (insular Italy). The RS approach was carried out in four orchards infected by Citrus tristeza virus (CTV) at different temporal stages and characterized by heterogeneous conditions (e.g., elevation, location, plant age). The temporal analysis allowed the identification of specific metrics of the NDVI time-series at the selected sites during the study period. The most reliable parameter which was able to identify the temporal evolution of CTV syndrome and the impact of operational management practices was the “Base value” (i.e., average NDVI during the growing seasons, which reached R2 values up to 0.88), showing good relationships with “Peak value”, “Small integrated value” and “Amplitude”, with R2 values of 0.63, 0.70 and 0.75, respectively. The approach herein developed is valid to be transferred to regional agencies involved in and/or in charge of the management of plant diseases, especially if it is integrated with ground-based early detection methods or high-resolution RS approaches, in the case of quarantine plant pathogens requiring control measures at large-scale level.