TY  - EJOU
AU  - Rodrigo-Comino, Jesús 
AU  - González-Moreno, María Teresa 
AU  - Moreno-Cuenca, Lucía 
AU  - Cambronero-Ruiz, Laura 
AU  - Irigaray, Clemente 
AU  - Bernardo, Francisco Serrano 
AU  - Zuazo, Víctor Hugo Durán 
AU  - Fernández-Gálvez, Jesús 
AU  - Caballero-Calvo, Andrés 
AU  - Rodríguez-Galiano, Víctor 

TI  - Terrain Controls on NDVI Spatial Variability under Post-Harvest Conditions: A UAV-Based Geomorphometric and Machine Learning Approach in Mediterranean Croplands
T2  - Revue Internationale de Géomatique

PY  - 2026
VL  - 35
IS  - 1
SN  - 2116-7060

AB  - 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.
KW  - High-resolution remote sensing; geomorphometry; machine learning; soil degradation; mediterranean agriculture; multi-scale analysis

DO  - 10.32604/rig.2026.081503