Reference Evapotranspiration (ET0) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET0 computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute ET0 using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET0 estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET0 estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors' previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET0 estimation by an ANN model, and (ii) estimate SR by ANN model, and then use that estimation for ET0 computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R2) ranging between 0.893 and 0.667, when considering forecasts up to 15 days.

翻译：参考蒸散量（ET0）是设计智能灌溉调度的关键参数，因为它通过一个系数与作物的水分需求相关联。联合国粮农组织（FAO）提出了一种基于Penman-Monteith方程参数化的ET0计算标准方法（FAO56-PM），该方法被文献广泛采用。采用FAO56-PM方法计算ET0需要四个主要气象参数：温度、湿度、风速和太阳辐射（SR）。利用免费可用的天气预报服务（WFSs）获取未来多天的日ET0估计值是一种可行方案，这些服务通常可提供未来15天内多个气象参数的预测值。但该方法存在一个瓶颈：目前大多数在线免费天气预报服务不提供太阳辐射参数，或此类预测通常需要支付相应费用。为此，文献中已出现多种基于机器学习和深度学习的ET0估计模型，这些模型采用精心筛选的简化气象参数作为输入特征，与常见免费WFSs兼容。然而，现有研究多数仅利用气象站（WSs）数据评估模型性能，未考虑使用天气预报数据的影响。本研究在两种场景下评估了作者先前模型使用两个在线WFSs天气预报数据时的表现：（i）通过ANN模型直接估计ET0，以及（ii）先通过ANN模型估计SR，再基于该估计值采用FAO56-PM方法计算ET0。利用葡萄牙Vale do Lobo地区两个WFSs和一个WS收集的数据，第二种方法取得了最佳效果：在考虑未来15天预报时，决定系数（R2）介于0.893至0.667之间。