Deep Learning-based Burned Area Mapping of California Wildfires Using Sentinel-2 and Landsat-8 Imagery Enhanced with Super-Resolution Techniques

Authors

• Youngmin Seo, Pukyong National University
• Seung Hee Kim, Chapman UniversityFollow
• Menas Kafatos, Chapman UniversityFollow
• Jinsoo Kim, Pukyong National UniversityFollow
• Yangwon Lee, Pukyong National UniversityFollow

Document Type

Article

Publication Date

8-26-2026

Abstract

The increasing frequency of wildfires under a changing climate has led to extensive ecosystem destruction, highlighting the need for reliable burned area assessment using satellite imagery. Single-satellite data are constrained by observation gaps and interference from smoke and clouds, whereas multi-satellite data fusion can mitigate these limitations. Nonetheless, the fusion techniques still encounter challenges such as spatial information loss from resolution differences and cross-satellite domain mismatch. This study presents a burned area mapping framework that integrates super-resolution (SR) with transfer learning to address spatial and domain gaps in multi-satellite data. Specifically, Landsat-8 imagery is super-resolved to 7.5 m resolution, and the pretrained model is subsequently fine-tuned using Sentinel-2 imagery. Additionally, we investigated spectral index expansion to improve detection performance. Applied to California wildfires from 2013 to 2023, the input band expansion achieved a Burned Intersection over Union (IoU) of 0.821 when multiple spectral indices were incorporated. The SR-based transfer learning approach further improved performance with a Burned IoU of 0.850, representing a 4.55 % enhancement over the original Landsat-8 pre-trained model (0.813). Validation on 2025 wildfires demonstrated consistent performance and model robustness. These results highlight that the proposed SR-based approach offers benefits beyond simple resolution enhancement in multi-satellite data fusion. Furthermore, they confirm that spectral index-based input expansion contributes to accuracy improvements. The proposed framework is expected to support disaster response and environmental monitoring applications.

Comments

This article was originally published in International Journal of Applied Earth Observation and Geoinformation, volume 153, in 2026. https://doi.org/10.1016/j.jag.2026.105526

Recommended Citation

Seo, Y., Kim, S.H., Kafatos, M., Kim, J., Lee, Y., 2026. Deep learning-based burned area mapping of California wildfires using Sentinel-2 and Landsat-8 imagery enhanced with super-resolution techniques. Int. J. Appl. Earth Obs. Geoinf. 153. https://doi.org/10.1016/j.jag.2026.105526

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.