Terrain Controls on NDVI Spatial Variability under Post-Harvest Conditions: A UAV-Based Geomorphometric and Machine Learning Approach in Mediterranean Croplands

Jesús Rodrigo-Comino; María Teresa González-Moreno; Lucía Moreno-Cuenca; Laura Cambronero-Ruiz; Clemente Irigaray; Francisco Serrano Bernardo; Vı́ctor Hugo Durán Zuazo; Jesús Fernández‐Gálvez; Andrés Caballero-Calvo; Victor Rodriguez-Galiano

doi:10.32604/rig.2026.081503

January 1, 2026Revue internationale de géomatiqueOpen Access

Terrain Controls on NDVI Spatial Variability under Post-Harvest Conditions: A UAV-Based Geomorphometric and Machine Learning Approach in Mediterranean Croplands

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Key Points

To understand how terrain influences NDVI spatial variability in Mediterranean agricultural systems.
Utilized UAV-based remote sensing for data collection
Employed geomorphometric analysis and machine learning algorithms
Focused on post-harvest conditions in Mediterranean croplands
Identified significant relationships between terrain factors and NDVI variability
Demonstrated the utility of UAV and machine learning for assessing soil degradation
Highlighted the importance of topography and water redistribution in crop monitoring

Implication

Evaluates NDVI variability influenced by terrain factors in Mediterranean crops, suggesting optimization for agricultural practices.

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 st... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Rodrigo-Comino et al. (2026) studied this question.

synapsesocial.com/papers/6a2e47f9b1cc60ccdea8c567 https://doi.org/10.32604/rig.2026.081503

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Terrain Controls on NDVI Spatial Variability under Post-Harvest Conditions: A UAV-Based Geomorphometric and Machine Learning Approach in Mediterranean Croplands

Key Points

Implication

Abstract

Discussion

Cite This Study

Terrain Controls on NDVI Spatial Variability under Post-Harvest Conditions: A UAV-Based Geomorphometric and Machine Learning Approach in Mediterranean Croplands

Key Points

Implication

Abstract

Discussion

Cite This Study

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