The employment of satellite data in remote sensing applications has become paramount for Precision Agriculture, particularly in woody crops. Satellite-based products are widely used in various applications to gather valuable information about crop conditions, providing potential valuable insights for a range of applications such as forecasting yield, predicting crop quality, and managing irrigation. However, the accuracy of satellite-derived products for precision agriculture purposes, such as leaf area and Normalized Difference Vegetation Index (NDVI) is often debated. This study aims to benchmark the satellite data accuracy against UAV LiDAR data and multispectral imagery. In this study, leaf area and NDVI were derived from Sentinel-2 images and compared with ground truth data estimated from the UAV LiDAR dataset collected in 2022 close to veraison, as this is a key phenological stage commonly used in remote sensing for precision viticulture. The UAV flights were conducted over a commercial vineyard, Vitis vinifera cv. Loureiro, located on northern Spain, using a DJI M300 multi-rotor platform equipped with a DJI Zenmuse L1 LiDAR sensor and a Micasense Altum camera. The results indicate a discrepancy in the leaf area estimated from satellite imagery, demonstrating its limited utility for assessing leaf area in woody crops conducted in hedgerow systems. Nevertheless, satellite data could discern spatial patterns and variability within the crop. Thus, while satellite-based remote sensing may not serve as the best tool for leaf area estimation in this context, its capacity to detect crop spatial heterogeneity remains valuable for overall field management and the creation of distinct zones for differentiated management. This research prompts a discussion about the suitability of solely relying on satellite imagery for managing agriculture and underscores the necessity to incorporate a range of remote sensing methodologies for efficiently managing vineyards cultivated in hedgerow systems.