Research Article

A Comparative Assessment of Sentinel-1 SAR with Optical Indices for Cloud-resilient Wildfire Mapping

Volume: 11 Number: 2 December 25, 2025

A Comparative Assessment of Sentinel-1 SAR with Optical Indices for Cloud-resilient Wildfire Mapping

Abstract

Accurate and timely wildfire mapping is essential for effective post-fire management and mitigation. This study evaluates the potential of Sentinel-1 (S1) SAR VH cross-polarization data for burned area mapping in a Mediterranean forest ecosystem near Marmaris, Türkiye, and compares its performance with established optical indices from Sentinel-2 (S2) data. Post-fire imagery was analyzed using the NBR, NBRT1, and BAI indices, with accuracy assessed against Landsat 9 OLI data. The results showed that S2_NBR outperformed all other methods, achieving the highest overall accuracy (97.4%) and F1-score (0.97). S2_BAI and S2_NBRT1 also delivered strong results, while S1_SAR had a lower overall accuracy (69.2%) but achieved perfect precision (1), meaning it effectively avoided false positives. However, S1_SAR had limitations in detecting the full extent of burned areas (lower recall). SAR data, with its ability to penetrate clouds, highlights its value as a complement to optical methods by ensuring continuous monitoring when cloud-free optical imagery is unavailable. This study emphasizes the importance of combining data from multiple sensors for reliable wildfire monitoring and guide resource allocation, risk management, and recovery efforts.

Keywords

Wildfire mapping , Sentinel-1 SAR , NBR and NBRT1 , BAI , Mediterranean ecosystem , Cloud-resilient.

References

  1. Alarcon-Aguirre, G., Miranda Fidhel, R.F., Ramos Enciso, D., Canahuire-Robles, R., Rodriguez-Achata, L., Garate-Quispe, J. 2022. Burn Severity Assessment Using Sentinel-1 SAR in the Southeast Peruvian Amazon, a Case Study of Madre de Dios. Fire, 5(4): 94. https://doi.org/10.3390/fire5040094
  2. Alcaras, E., Costantino, D., Guastaferro, F., Parente, C., Pepe, M. 2022. Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery. Remote Sensing, 14(7): 1727. https://doi.org/10.3390/ rs14071727
  3. Alkayiş, M.H., Karslioğlu, A., Onur, M.İ. 2022. Determination of forest fires risk potential map of Menteşe region of Muğla with geographic information systems, Geomatik, 7(1): 10–16.
  4. ARSET, 2023. Techniques for Wildfire Detection and Monitoring. Available at: https://appliedsciences.nasa.gov/get-involved/trainin g/english/arset-techniques-wildfire-detection-and- monitoring (Accessed: 4 September 2023).
  5. Baltaci, U., Yildirim, F. 2021. Multi-criteria analysis and mapping of forest fire risk in Muğla Regional Directorate of Forestry, Turkish Journal of Forestry Research, 8(1): 1–11. https://doi.org/10.17568/ ogmoad.708385.
  6. Cigdem Cavdaroglu, G. 2021. Google Earth Engine Based Approach for Finding Fire Locations and Burned Areas in Muğla, Turkey, American Journal of Remote Sensing, 9(2): 72–77. https://doi.org/ 10.11648/j.ajrs.20210902.12.
  7. Ezzaher, F.E., Ben Achhab, N., Raissouni, N., Naciri, H., Chahboun, A. 2024. Normalized Burn Ratio and Land Surface Temperature Pre- and Post-Mediterranean Forest Fires. Environmental Sciences Proceedings, 29(1): 3. https://doi.org/10.3390/ECRS2023-15829
  8. Farhadi, H., Ebadi, H., Kiani, A. 2023. BADI: a novel burned area detection index for sentinel-2 imagery using google earth engine platform, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4/W1-2022, pp. 179–186. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-179-2023.
  9. Foody, G.M. 2020. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification, Remote Sensing of Environment, 239: 111630. https://doi.org/https://doi.org/10.1016/ j.rse.2019.111630.
  10. Gülci, S., Yüksel, K., Gümüş, S., Wing, M. 2021. Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry. European Journal of Forest Engineering, 7(2): 57-66. https://doi.org/10.33904/ejfe.1031090
APA
Abujayyab, S. K. M. (2025). A Comparative Assessment of Sentinel-1 SAR with Optical Indices for Cloud-resilient Wildfire Mapping. European Journal of Forest Engineering, 11(2), 95-105. https://doi.org/10.33904/ejfe.1618178