Integration of Landsat-8, Sentinel-2, and Aster Satellite Imagery, Geomorphometry, and Machine Learning for Lithological Mapping of the Coastal Batholith Between San Gregorio and Chepén

Lithological mapping is essential for geological exploration, though fieldwork-based mapping is often limited by time, cost, and accessibility. This study proposes an integrated methodology to support lithological mapping of the Coastal Batholith between San Gregorio and Chepén, using Landsat 8, ASTER, and Sentinel-2 imagery, DEM-derived morphometric variables, and the Random Forest algorithm, applying dimensionality reduction techniques (PCA, MNF, ICA) and field-based validation. Results showed robust performance across all three sensors, with Landsat 8 achieving the highest accuracy (Overall Accuracy = 76.1 %, Kappa = 0.736, F1-score = 76.8 %), followed by Sentinel-2 (OA = 75.3 %) and ASTER (OA = 73.0 %). Morphometric variables, particularly elevation, contributed 44–48 % of the model's total importance, proving key for identifying Quaternary de-posits. Field verification confirmed the reliability of the resulting mapping, demonstrating that integrating multisensor data, morphometric variables, and Random Forest is an effective method-ology for geological exploration.