Several Surface Energy Balance (SEB) models are currently used to unleash the boundless potential of Geographic Information System (GIS) and Remote Sensing (RS) techniques. Their main output, namely the actual evapotranspiration (ETa), is required for the assessment of water budget at basin, regional and national levels. Still, the lack of the required expertise coupled with sometimes missing input data limits their usage by researchers, policy makers, water managers and farmers. In this study, a novel comprehensive monthly adjusted crop coefficients (aKc) list was produced, in which these coefficients are usually used to retrieve the much-needed ETa using the climate-sensitive reference crop evapotranspiration (ET₀). More particularly, the ETa is calculated for more than sixty different crop types present in the Mediterranean-climate California, United States. It is based on the two consecutive years of 2018 and 2019, which were wet and dry, respectively. The Google Earth Engine (GEE) version of the Surface Energy Balance Algorithm for Land-Improved (SEBALI), surnamed SEBALIGEE, with an Absolute Mean Error (AME) of 9.56 mm/month in the study area was used along the annual crop map from the United States Department of Agriculture (USDA). The main results showed that the average monthly aKc values ranged between 0.45 and 1.53. More specifically, October and November presented the lowest aKc values averaged among all crop types with aKc values around 0.76. On the other hand, March, April and June had the largest average aKc values at nearly 1.17. The monthly aKc values of the 60 crops studied in the plain of California, would potentially assist authorities and hydrologists to better calculate the actual monthly plant water consumption, in a simpler way over the upcoming years.

目前使用多种表面能量平衡 (SEB) 模型来释放地理信息系统 (GIS) 和遥感 (RS) 技术的无限潜力。它们的主要输出，即实际蒸散量 (ETa)，是评估流域、区域和国家层面的水预算所必需的。尽管如此，缺乏所需的专业知识以及有时缺少输入数据限制了研究人员、政策制定者、水资源管理者和农民的使用。在这项研究中，产生了一个新的综合月度调整作物系数 (aKc) 列表，其中这些系数通常用于使用气候敏感的参考作物蒸散量 (ET ₀）。更具体地说，ETa 是针对美国加利福尼亚州地中海气候中存在的 60 多种不同作物类型进行计算的。它基于2018年和2019年连续两年，分别是潮湿和干燥。谷歌地球引擎 (GEE) 版本的土地改良 (SEBALI) 表面能量平衡算法，姓 SEBALIGEE，研究区域的绝对平均误差 (AME) 为 9.56 毫米/月，沿年度作物图使用美国农业部 (USDA)。主要结果表明，月平均 aKc 值介于 0.45 和 1.53 之间。更具体地说，10 月和 11 月在所有作物类型中呈现最低的 aKc 值，aKc 值约为 0.76。另一方面，3 月、4 月和 6 月的平均 aKc 值最大，接近 1.17。