Evaluating Sentinel-2 Super-Resolution for Geospatial Information Extraction: A Spectral and Thematic Assessment
Abstract
Super-resolution (SR) of remote-sensing imagery is commonly assessed through spatial fidelity or visual sharpness, although index-based geomatics requires preservation of cross-band spectral relationships. We compare five Sentinel-2 full-band SR configurations (LDSR-S2, SRGAN, SPAN, Mamba, and SWIN) in two hazard-mapping cases: flood-water detection during the 2024 Valencia flood and burn-scar mapping after the 2025 Palisades wildfire. Each model refines four native RGB–NIR bands, while SEN2SR reconstructs the remaining bands to produce a ten-band product at 2.5 m. We evaluate native-grid reconstruction, the introduction of high-frequency details, and downstream thematic, boundary, and edge-region metrics against a bilinear-interpolation baseline. Dynamic-threshold MNDWI and dNBR detectors are applied independently to each output. Among the learned configurations, SWIN achieves the strongest native-grid reconstruction and task-specific spectral consistency and the strongest fire agreement, but adds the least high-frequency content. Flood full-ROI gains are modest, with LDSR-S2 increasing the F1-score from 0.085 for bilinear interpolation to 0.091. All learned configurations increase flood-edge recall, F1-score, and IoU while reducing edge precision and balanced accuracy. LDSR-S2 gives the strongest final edge F1-score and IoU in both tasks, Mamba gives the lowest learned-model symmetric flood-boundary distance. SPAN yields the best spatial consistency and lowest learned flood-edge spectral error and SRGAN adds the most high-frequency content and the largest combined edge-region gain, alongside the greatest task-specific spectral deviation and the largest symmetric boundary-distance increases. Thus, increased edge activation does not establish uniformly improved delineation or recovered sub-pixel detail. These cases demonstrate feasibility rather than generalization. Operational validation requires more diverse, time-synchronous, high-resolution, spectrally compatible references.