The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent monitoring, since quality and productivity are the backbone of the economic potential. Regional climate indicators and meteorological information are essential to winemakers to assure proper vineyard management. Satellite data are very useful in this process since they imply low costs and are easily accessible. This work proposes a statistical modelling approach based on parameters obtained exclusively from satellite data to simulate annual wine production. The study has been developed for the Douro Demarcated Region (DDR) due to its relevance in the winemaking industry. It is the oldest demarcated and controlled winemaking region of the world and listed as one of UNESCO’s World Heritage regions. Monthly variables associated with Land Surface Temperatures (LST) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR), which is representative of vegetation canopy health, were analysed for a 15-year period (2004 to 2018), to assess their relation to wine production. Results showed that high wine production years are associated with higher than normal FAPAR values during approximately the entire growing season and higher than normal values of surface temperature from April to August. A robust linear model was obtained using the most significant predictors, that includes FAPAR in December and maximum and mean LST values in March and July, respectively. The model explains 90% of the total variance of wine production and presents a correlation coefficient of 0.90 (after cross validation). The retained predictors’ anomalies for the investigated vegetative year (October to July) from 2017/2018 satellite data indicate that the ensuing wine production for the DDR is likely to be below normal, i.e., to be lower than what is considered a high-production year. This work highlights that is possible to estimate wine production at regional scale based solely on low-resolution remotely sensed observations that are easily accessible, free and available for numerous grapevines regions worldwide, providing a useful and easy tool to estimate wine production and agricultural monitoring.

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The mesoscale Weather Research and Forecasting (WRF) Model is used over the Iberian Peninsula to generate 60 years (1950-2010) of climate data, at 5 km resolution, in order to evaluate and characterize the incident shortwave downward radiation at the surface (SW), in present climate. The simulated values of SW in the period 2000-2009 were compared with data measured in Spanish and Portuguese meteorological stations before and a statistical BIAS correction was applied using data from Clouds and the Earth's Radiant Energy System (CERES), on board four different satellites. The spatial and temporal comparison between WRF results and observations show a good agreement for the analyzed period, although the model overestimates observations. This overestimation has a mean normalized bias of about 7% after BIAS correction (or 17% for original WRF output). Additionally, the present simulation was confronted against another previously validated WRF simulation performed with different resolution and set of parametrizations, showing comparable results. WRF adequately reproduces the observational features of SW with correlation coefficients above 0.8 in annual and seasonal basis. 60 years of simulated SW over the Iberian Peninsula were produced, which showed annual mean values that range from 130 W/m 2 , in the northern regions, to a maximum of around 230 W/m 2 in the southeast of the Iberian Peninsula (IP). SW over IP shows a positive gradient from north to south and from west to east, with local effects influenced by topography and distance to the coast. The analysis of the simulated cloud fraction indicates that clear sky days are found in more than 30% of the period at the southern area of IP, particularly in the Algarve (Portugal) and Andalusia (Spain), and this value increases significantly in the summer season for values above 80%.

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