The Limits of RGB-Based Vegetation Indexes under Canopy Degradation: Insights from UAV Monitoring of Harvested Cereal Fields

Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras are increasingly used as low-cost tools for crop monitoring, offering a range of vegetation indexes in the visible spectral range. These indexes have often been reported to correlate with other multispectral indexes such as the Normalized Difference Vegetation Index (NDVI) during active growth stages. However, still efforts should be done about their performance under conditions of canopy degradation. In this study, UAV flights were conducted over a cereal field immediately after harvest, when the canopy consisted mostly of bare soil and dry residues. RGB-based indexes were calculated from the orthomosaic, normalized to a [0–1] scale, and compared to NDVI derived from a multispectral sensor. Data preprocessing included ground control point (GCP) georeferencing, removal of NoData pixels, and raster alignment. Results revealed very weak correlations between RGB indexes and NDVI (Pearson r < 0.15), with Visible Atmospherically Resistant Index (VARI) showing almost no variability across the field. Although the Leaf Index (GLI), yielded the lowest error values, all RGB indexes failed to reproduce the variability of NDVI under post-harvest conditions. These findings highlight a critical methodological limitation: RGB indexes are unsuitable for vegetation monitoring when canopy cover is severely reduced. While they remain useful during active growth, their reliability diminishes in degraded or post-harvest scenarios, thereby limiting their application in assessing abiotic stress in cereals.