Combining Multispectral Data from Unmanned Aerial System and Sentinel-2 for Kiwifruit and Cotton Monitoring and Yield Assessment

Remote Sensing data coupling is critical for innovative farming applications, crop monitoring, and yield estimation, especially in fields characterized by high heterogeneity. Precision Agriculture methods using novel multispectral and multitemporal satellite and Unmanned Aerial Systems (UAS) data are essential tools for agricultural management. In the present study, Sentinel-2 and UAS data were exploited and compared in perennial and arable cultivations to monitor and evaluate the crop spatial variation using selected vegetation indices. The study was conducted on kiwifruit and cotton crops in Northern Greece. Four vegetation indices (VIs) were calculated and tested to assess the correlation between the outputs of the different sensing methods and the yield measurements. The results revealed the usefulness of remote and airborne sensing-derived VIs, especially NDRE for the cotton field and OSAVI for Kiwifruit, in monitoring crop conditions and yield assessment. Evaluating the different multispectral data and vegetation indices provides valuable information for future yield estimation and its increment through improving crop treatments.