The availability of accurate information on the water consumed for crop irrigation is of vital importance to support compatible and sustainable environmental policies in arid and semi-arid regions. This has promoted several studies about the use of remote sensing data to monitor irrigated croplands, which are mostly based on statistical classification and/or regression techniques. The current paper proposes a new semi-empirical approach that relies on a water balance logic and does not require local tuning. The method stems from recent investigations which demonstrated the possibility of combining standard meteorological data and Sentinel-2 (S-2) Multi Spectral Instrument (MSI) NDVI images to estimate the actual evapotranspiration (ETa) of irrigated Mediterranean croplands. This ETa estimation method is adapted to drive a simplified site water balance which, for each 10-m S-2 MSI pixel, predicts the irrigation water (IW), i.e. the water which is consumed in addition to that naturally supplied by rainfall. The new method, fed with ground and satellite data from two years (2018–2019), is tested in a Mediterranean area around the town of Grosseto (Central Italy), that is covered by a particularly complex mosaic of rainfed and irrigated crops. The results obtained are first assessed qualitatively for some fields grown with known winter, spring and summer crops. Next, the IW estimates are evaluated quantitatively versus ground measurements taken over two irrigated fields, the first grown with processing tomato in 2018 and the second with early corn in 2019. Finally, the IW estimates are statistically analyzed against various datasets informative on local agricultural practices in the two years. All these analyses indicate that the proposed method is capable of predicting both the intensity and timing of the IW supply in the study area. The method, in fact, correctly identifies rainfed and irrigated crops and, in the latter case, accurately predicts the IW actually supplied. The results of the quantitative tests performed on tomato and corn show that over 50 % and 70 % of the measured IW variance is explained on daily and weekly bases, respectively, with corresponding mean bias errors below 0.3 mm/day and 2.0 mm/week. Similar indications are produced by the qualitative tests; reasonable IW estimates are obtained for all winter, springs and summer crops grown in the study area during 2018 and 2019.

关于作物灌溉用水的准确信息的获取对于支持干旱和半干旱地区的兼容和可持续的环境政策至关重要。这促进了有关使用遥感数据监测灌溉农田的几项研究，这些研究主要基于统计分类和/或回归技术。当前的论文提出了一种新的半经验方法，该方法依赖于水平衡逻辑并且不需要局部调整。该方法源于最近的调查，这些调查证明了将标准气象数据与Sentinel-2（S-2）多光谱仪（MSI）NDVI图像相结合以估算地中海灌溉农田的实际蒸散量（ETa）的可能性。该ETa估算方法适用于驱动简化的站点水平衡，对于每个10毫秒的S-2 MSI像素，它可以预测灌溉水（IW），即除降雨自然提供的水以外还消耗的水。这种新方法以两年（2018-2019年）的地面和卫星数据为基础，在格罗塞托镇（意大利中部）周围的地中海地区进行了测试，该地区覆盖了特别复杂的雨养和灌溉作物。首先对使用已知冬季，春季和夏季作物种植的某些田地进行定性评估。接下来，对IW估算值与两个灌溉田地的地面测量值进行定量评估，第一个灌溉田在2018年种植加工番茄，第二个灌溉田在2019年种植玉米。IW估计值是根据两年中有关当地农业实践的各种数据集进行统计分析的。所有这些分析表明，所提出的方法能够预测研究区域内水资源供应的强度和时间。实际上，该方法可以正确识别雨养和灌溉作物，在后一种情况下，可以准确预测实际供应的IW。在番茄和玉米上进行的定量测试结果表明，分别在每天和每周的基础上解释了超过50％和70％的IW方差，相应的平均偏差误差低于0.3 mm /天和2.0 mm /周。定性测试产生类似的指示。对于2018年和2019年研究区域种植的所有冬季，春季和夏季作物，均获得了合理的IW估算值。