Abstract
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.
Highlights
Sensed data provided by Earth Observation from satellites has been a key enabler of the analysis and monitoring of vegetation dynamics on a global scale
The Normalized Difference Vegetation Index (NDVI) has been frequently used to address vineyards conditions [8,9] and wine production/productivity [10,11,12,13], the availability of Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) estimates from satellite data opens the possibility to assess the state of the vineyard health via a variable closer to photosynthesis processes
Gouveia et al [12] have analysed the vegetative cycle of vineyards and the vulnerability of Douro wine production to climate variability and change using NDVI data obtained from satellite and Remote Sens. 2018, 10, x FOR PEER REVIEW
Summary
Sensed data provided by Earth Observation from satellites has been a key enabler of the analysis and monitoring of vegetation dynamics on a global scale. The NDVI has been frequently used to address vineyards conditions [8,9] and wine production/productivity [10,11,12,13], the availability of FAPAR estimates from satellite data opens the possibility to assess the state of the vineyard health via a variable closer to photosynthesis processes. Gouveia et al [12] have analysed the vegetative cycle of vineyards and the vulnerability of Douro wine production to climate variability and change using NDVI data obtained from satellite and Remote Sens. Douro wine production to climate variability and change using NDVI data obtained from satellite amnedtemoreotelogroicloalgipcaarlapmaeratemrsetferrosmfrtohme CthleimCaltiimc aRteicseRaerscehaUrcnhitUdnaitadsaettas.seTths.eTrheesurletsuslhtsowsheodwtehdatththaet tphreodpurocdtiuocntivoanrivaabriilaitbyiliistyliniskleidnkteodlattoelwatienwteirn-etear-lyeasrplyrisnpgrpinrgecpipreitcaitpioitnataionndasnpdrinspgrtienmg pteemraptueraestu.
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