@misc{10481/109296,
year = {2025},
month = {11},
url = {https://hdl.handle.net/10481/109296},
abstract = {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. RGBbased 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.},
organization = {This research was funded by the project “Desarrollo de productos basados en los nuevos sensores satelitales
hiperespectrales europeos e IA para la caracterización de estresores en tierras de cultivo (HIPROESTRES)” (grant
number PID2023-152656OB-I00), within the Programa Estatal de Investigación Científica, Técnica y de Innovación
(2021-2023) by the Ministerio de Ciencia, Innovación y Universidades.},
publisher = {SCIEPUblish},
keywords = {UAV remote sensing},
keywords = {RGB vegetation indexes},
keywords = {NDVI comparison},
keywords = {Post-harvest cereals},
keywords = {Abiotic stress monitoring},
title = {The Limits of RGB-Based Vegetation Indexes under Canopy Degradation: Insights from UAV Monitoring of Harvested Cereal Fields},
doi = {https://doi.org/10.70322/dav.2025.10021},
author = {Rodrigo Comino, Jesús and Gatea Al-Shammary, Ahmed Abed and Durán Zuazo, Víctor Hugo and Serrano Bernardo, Francisco Antonio and Caballero Calvo, Andrés and Rodríguez-Galiano, Víctor},
}