This article introduces an application of the Granger causality test in precision agriculture. Originally developed to address economic issues, this statistical test aims at determining whether one time series is useful to forecast another time series. In this study, the test was applied to two time series of data available at within field level in viticulture: sentinel-2 satellite images and Apex growth monitoring (iG-ApeX method). Results show that time series obtained by Sentinel-2 satellite imagery can be used to predict vegetation growth both at the field scale and at the within-field scale. The Granger causality test could find many applications in precision agriculture, especially with the development of high temporal resolution data acquisition methods.