  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.
