Mapping Fuel Categories in Mediterranean Regions with Sentinel-2 and PlanetScope Satellite Acquisitions

Abstract. Accurate and spatially explicit mapping of forest fuel types is essential for developing integrated wildfire risk management strategies, estimating fire danger, and modeling fire propagation in fire-prone Mediterranean ecosystems. While time series of Copernicus Sentinel-2 multispectral imagery at 10 m spatial resolution have proven effective for regional fuel mapping, severe spectral and spatial ambiguities persist in distinguishing complex canopy structures, heterogeneous shrublands, and pastures. This study explores the synergistic integration of Sentinel-2 time series and high-resolution PlanetScope satellite acquisitions (3 m spatial resolution) to produce a 10 m fuel type map (9 classes) over a pilot area in Sardinia, Italy. A total of 114 features were synthesized, combining dynamic Sentinel-2 metrics (kNDVI, FAPAR, NDMI, ReNDVI harmonics), PlanetScope seasonal kNDVI zonal statistics and Gray-Level Co-occurrence Matrix (GLCM) textural features, and static terrain/canopy parameters. Using an XGBoost machine learning classifier trained on 245 photo-interpreted Regions of Interest (80/20 spatial split), three sets of features were compared. The combined model achieved a test overall accuracy of 74.46%, compared to 74.05% for Sentinel-2 alone and 74.66% for PlanetScope alone. Notably, incorporating PlanetScope textural features significantly improved classification metrics for dense canopy classes, increasing the F1-score of broadleaf evergreen trees and needleleaf evergreen trees. The PlanetScope GLCM kNDVI autumn entropy emerged as the most important feature overall. Conversely, PlanetScope performed poorly on the grass class. These findings highlight the critical trade-offs and high complementarity between fine spatial texture and broad spectral coverage for operational wildfire management.