Refining satellite imagery by using UAV imagery for vineyard environment: A CNN Based approach

Frequent vegetation monitoring of agriculture crops during their phenological cycle helps farmers/agronomist to perform their activities in efficient way to gain maximum yield and reduce the environmental noise caused by excessive use of chemicals. Various remote sensing platforms equipped with optical multispectral sensors such as satellite, airborne and unmanned air vehicles (UAVs) are being used in vegetation monitoring. Satellites equipped with multispectral sensors are popular due to their large coverage and temporal resolution. On the other hand, UAVs are preferred where more detailed imagery is needed while its expensive and time consuming if more frequent campaigns have to be performed. In this study, vineyard site is considered to assess the reliability of using satellite images for vegetation monitoring. Indeed, satellite imagery with decametric spatial resolution cannot describe the vegetation status at vine rows level due to the mixed nature of pixel, representing the cumulative effect of inter row terrain and vine rows. Therefore, a pixel refinement is needed to minimize this effect. In this work, a convolutional neural network (CNN) based approach is proposed to gain benefits from high resolution UAV images in order to refine the frequent moderate resolution satellite images over a vineyard.