The lack of data on water consumption by agriculture in the context of climate change led the authors to estimate the water balance of the Bekaa Valley. Plots were mapped according to the normalized difference vegetation index profiles of spring and summer crop with the Sentinels-2 images. Surface energy balance model python (PYSEBAL) estimated the seasonal evapotranspiration (ETseason) and net irrigation requirements (NIRseason). Spring and summer crop maps were accurate: 90% and 93%. The results of PYSEBAL were validated by the comparison between averaged daily ET of PYSEBAL and FAO-56 method (root mean square error from 0.38 to 1.93 and the mean average error from 0.33 to 1.64 mm / day). The water balance was negative: 0.16 km3 of groundwater was extracted against 0.10 km3 of rainfall available for aquifer recharge in 2017. This is explained by ET/ha/season (alfalfa: 1094 mm / ha / season, orchards 881, corn 719, tobacco 606, cucumber 558, late potatoes 487, summer vegetables 483, early potatoes 467, vineyard 390, spring vegetables 237, and wheat 313). ETseason varies according to temperature (strong correlation between daily ET and temperature, Pearson: 0.74), relative humidity (Pearson: −0.65), surface radiation (Pearson: 0.75), and percentage of sand in the soil and its depth (strong negative Pearson: −0.62 and −0.57).

中文翻译：

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蒸散量和地下水压力的空间变异性：黎巴嫩贝卡平原平原按作物类型进行的遥感监测

在气候变化的背景下，缺少农业用水量的数据，导致作者估计了贝卡谷地的水平衡。使用Sentinels-2图像，根据春季和夏季作物的归一化差异植被指数剖面图绘制地块。地表能量平衡模型python（PYSEBAL）估计了季节性蒸散量（ETseason）和净灌溉需求（NIRseason）。春季和夏季作物图准确无误：分别为90％和93％。通过比较PYSEBAL的平均每日ET和FAO-56方法（均方根误差从0.38到1.93，平均平均误差从0.33到1.64 mm /天），对PYSEBAL的结果进行了验证。水量平衡为负数：2017年提取的0.16 km3的地下水与0.10 km3的可用于含水层补给的降雨相比。ET /公顷/季节（苜蓿：1094毫米/公顷/季节，果园881，玉米719，烟草606，黄瓜558，晚马铃薯487，夏季蔬菜483，早马铃薯467，葡萄园390，春季蔬菜237，和小麦313）。ET季节随温度（每日ET和温度之间的强相关性，Pearson：0.74），相对湿度（Pearson：-0.65），表面辐射（Pearson：0.75）以及土壤中沙的百分比及其深度（强烈的Pearson负数）而变化。 ：-0.62和-0.57）。