Fusion of Satellite and UAV Imagery for Crop Monitoring

Abstract. Crop monitoring is crucial for precision agriculture, providing insights for optimizing yield and managing resources effectively. This study explores the fusion of Unmanned Aerial Vehicle (UAV) and Sentinel-2 (S2) satellite imagery for monitoring the crop by analyzing vegetation indices and canopy height information from the temporal dataset. Brovey Transform (BT) and Principal Component Analysis (PCA) fusion techniques are used to fuse the UAV and satellite images, aiming to leverage the high spatial resolution of UAV imagery with the broader spectral range of S2 data. Five key vegetation indices, including NDVI, GNDVI, SAVI, EVI, and LAI, were calculated from UAV, S2, and fused imagery in various temporal dates. Canopy height was derived from UAV data, and statistical analyses, including coefficient of determination (R²), Pearson correlation coefficient, and Root Mean Square Error (RMSE), were performed to assess relationships between canopy height and vegetation indices across the fused images and UAV and S2 images. Results indicate that fused imagery significantly enhances crop health metrics' accuracy and spatial relevance, with high R² values and strong correlations between vegetation indices of fused images and UAV images, suggesting enhanced predictive power in monitoring crop health. Our findings highlight the advantages of fusing UAV and S2 imagery for comprehensive crop condition assessment, demonstrating that fused images provide a robust tool for monitoring crop vigor and stress levels. This approach offers valuable support for timely, data-driven decisions in crop management practices.