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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. • Unsupervised classification was used for detecting potentially irrigated areas. • Optical Trapezoid Model allowed to map actual irrigated areas. • The accuracy could be influenced by soil parameters. • Overestimation could highlight the presence of non-declared irrigated areas. [ABSTRACT FROM AUTHOR]

Abstract: Satellite remote sensed data on canopy biophysical properties, ground data and agro-meteorological information were combined to estimate evapotranspiration (ET) fluxes of orange orchards using a modified Penman–Monteith equation. The study was carried out during the irrigation season 2004 in an irrigation district, cover for about 95% with orange groves, of 1550ha located in eastern Sicily (Italy). The spatial pattern in ET-fluxes have been analysed using IKONOS high-resolution satellite and hyper-spectral ground data acquired and processed for the study-area. The remote estimates of ET-fluxes varied between 1.3 and 5.7mm/day, with a daily average value of about 4.2mm, showing a good agreement with crop ET values determined as residual of soil water balance of selected ground control sites. Crop coefficient estimates ranged between 0.22 and 1.08 showing positive correlations with percentages of ground cover (C g) increasing from 30 to 80% ground shading and with LAI values. By comparing ET estimates with water volumes supplied in each sub-district of the study-area, the performance indicator “IP” was evaluated, allowing to rank the conditions of un-fulfilment of crop water requirements by public and private water distribution systems. Generally, out of 29 sub-districts, 14 had “IP” values less than 50%, revealing a sub-optimal water supply for the study-area. [Copyright &y& Elsevier]

Evapotranspiration (ET) estimation is important for understanding energy exchanges and water cycles. Remote sensing (RS) is the main method used to obtain ET data over large scales. However, owing to surface heterogeneities and different model algorithms, ET estimated from RS products with different spatial resolutions can cause significant uncertainties, whose causes need to be thoroughly analyzed. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) model was selected to explore spatial resolution influences on ET simulations. Three satellite datasets (Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High-Resolution Radiometer (AVHRR)) were selected to independently estimate ET in SEBAL model to identify the influence of the spatial scale on ET estimation, and analyze the effects and causes of scale aggregation. Results indicated that: (1) the spatial distributions of ET estimated from the three satellite datasets were similar, with the MODIS-based ET having the largest uncertainty; and (2) aggregating input parameters had limited changes in the net radiation and soil heat fluxes. However, errors in the sensible heat and latent heat fluxes were relatively larger, which were caused by changes in the selection of hot and cold pixels and the NDVI and surface albedo parameters during scale aggregation. The scale errors caused by the model mechanisms were larger than those caused by the land use/cover pattern in the SEBAL model. Overall, this study highlights the impact of spatial scale on ET and provides a better understanding of the scale aggregation effect on ET estimation by RS. [ABSTRACT FROM AUTHOR]

An adjusted satellite-based model was proposed with the aim of improving spatially distributed evapotranspiration (ET) estimates under plant water stress conditions. Remote sensing data and near surface geophysics information, using electrical resistivity tomography (ERT), were used in a revised version of the original dual crop coefficient (Kc) FAO-56 approach. Sentinel 2-A imagery were used to compute vegetation indices (VIs) required for spatially estimating ET. The potentiality of the ERT technique was exploited for tracking the soil wetting distribution patterns during and after irrigation phases. The ERT-derived information helped to accurately estimate the wet exposed fraction (few) and therefore the water evaporated from the soil surface into the dual Kc FAO-56 approach. Results, validated by site-specific ET measurements (ETEC) obtained using the eddy covariance (EC) technique, showed that ERT-adjusted ET estimates (ETERT) were considerably reduced (15%) when compared with the original dual Kc FAO-56 approach (ETFAO), soil evaporation overestimation being the main reason for these discrepancies. Nevertheless, ETFAO and ETERT showed overestimations of 64% and 40% compared to ETEC. This is because both approaches determine ET under standard conditions without water limitation, whereas EC is able to determine ET even under soil water deficit conditions. From the comparison between ETEC and ETERT, the water stress coefficient was experimentally derived, reaching a mean value for the irrigation season of 0.74. The obtained results highlight how new technologies for soil water status monitoring can be incorporated for improving ET estimations, particularly under drip irrigation conditions. [ABSTRACT FROM AUTHOR]