@Article{rig.2026.081503,
AUTHOR = {Jesús Rodrigo-Comino, María Teresa González-Moreno, Lucía Moreno-Cuenca, Laura Cambronero-Ruiz, Clemente Irigaray, Francisco Serrano Bernardo, Víctor Hugo Durán Zuazo, Jesús Fernández-Gálvez, Andrés Caballero-Calvo, Víctor Rodríguez-Galiano},
TITLE = {Terrain Controls on NDVI Spatial Variability under Post-Harvest Conditions: A UAV-Based Geomorphometric and Machine Learning Approach in Mediterranean Croplands},
JOURNAL = {Revue Internationale de Géomatique},
VOLUME = {35},
YEAR = {2026},
NUMBER = {1},
PAGES = {333--349},
URL = {http://www.techscience.com/RIG/v35n1/67591},
ISSN = {2116-7060},
ABSTRACT = {Soil degradation in Mediterranean agricultural systems is strongly conditioned by topography, water redistribution and solar exposure, factors that can be effectively studied using very high-resolution remote sensing. This study evaluates the potential of Unmanned Aerial Vehicle (UAV)-derived geomorphometry combined with machine learning techniques to analyse the spatial variability of the Normalized Difference Vegetation Index (NDVI) as a surface spectral response under post-harvest conditions in a Mediterranean cereal field affected by soil degradation and gully erosion, located near Casabermeja (Málaga, southern Spain). High-resolution RGB and multispectral UAV data were used to generate a Digital Terrain Model (DTM), multi-scale local relief metrics, hydrological indices and curvature derivatives, together with NDVI maps acquired after harvest under dry and compacted soil conditions. A Random Forest regression model was applied using 3000 sampling points to link geomorphometric variables with NDVI spatial patterns. Although predictive performance was moderate (R<sup>2</sup> = 0.31; RMSE = 0.04), variable-importance analysis identified the main terrain-related factors associated with spatial variability in the spectral signal, highlighting slope, solar exposure and erosion- and flow-convergence-related indices. The results demonstrate the usefulness of UAV-based geomorphometry and machine learning as diagnostic tools for analysing terrain-controlled surface spectral patterns and identifying areas potentially affected by soil degradation processes in Mediterranean agroecosystems.},
DOI = {10.32604/rig.2026.081503}
}