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

Year 2025, Volume: 11 Issue: 2, 95 - 105, 25.12.2025

Sohaib K M Abujayyab

https://doi.org/10.33904/ejfe.1618178

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

• 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
• 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
• 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.
• 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).
• 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.
• 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.
• 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
• 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.
• 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.
• 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
• Jain, P., Coogan, S.C P., Subramanian, S.G., Crowley, M., Taylor, S., Flannigan, M.D. 2020. A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4): 478–505. https://www.jstor.org/stable/26998636
• De Luca, G., M.N. Silva, J., Di Fazio, S., Modica, G. 2022a. Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region, European Journal of Remote Sensing, 55(1): 52–70. https://doi.org/10.1080/22797254.2021.2018667.
• De Luca, G., Silva, J.M.N., Modica, G. 2022b. Regional-scale burned area mapping in Mediterranean regions based on the multitemporal composite integration of Sentinel-1 and Sentinel-2 data, GIScience and Remote Sensing, 59(1): 1678–1705. Available at: https://doi.org/10.1080/15481603.2022.2128251.
• De Luca, G., Silva, J.M.N., Modica, G. 2021. A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems, GIScience and Remote Sensing, 58(4): 516–541. https://doi.org/10.1080/15481603.2021.1907896.
• Pan, L., Xia, H., Jia Wenhui N.Y., Wang, R., Song, H., Guo, Y., Qin, Y. 2021. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine, International Journal of Applied Earth Observation and Geoinformation, 102(2021): 102376. https://doi.org/https://doi.org/10.1016/j.jag.2021.102376.
• Pham-Duc, B., Prigent, C., Aires, F. 2017. Surface water monitoring within cambodia and the Vietnamese Mekong Delta over a year, with Sentinel-1 SAR observations, Water (Switzerland), 9(6): 366. https://doi.org/10.3390/w9060366.
• Saadat, M., Seydi, S.T., Hasanlou, M., Homayouni, S. 2022. A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery. Agriculture, 12(12): 2083. https://doi.org/10.3390/agriculture12122083
• Sari, F. 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS, Forest Ecology and Management, 480(1): 118644. https://doi.org/10.1016/j.foreco.2020.118 644.
• Sayad, Y.O., Mousannif, H., Al Moatassime, H. 2019. Predictive modeling of wildfires: A new dataset and machine learning approach, Fire Safety Journal, 104: 130–146. https://doi.org/https://doi.org/10.1016/j. firesaf.2019.01.006.
• Sobrino, J.A., Llorensa, R., Fernándezb, C., Fernández-Alonsob, J.M., Vegab, J.A. 2024. Methodology for Burned Areas Delimitation and Fire Severity Assessment Using Sentinel-2 Data. A Case Study of Forest Fires Occurred in Spain Between 2018 and 2023, Recent Advances in Remote Sensing, 5(27):1-13. https://doi.org/10.62880/rars240002.
• Tanase, M.A., Belenguer-Plomer, M.A., Roteta, E., Bastarrika, A., Wheeler, J., Fernández-Carrillo, Á., Tansey, K., Wiedemann, W., Navratil, P., Lohberger, S., Siegert, F., Chuvieco, E. 2020. Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa. Remote Sensing, 12(2): 334. https://doi.org/ 10.3390/rs12020334
• Tariq, A., Shu, H., Li, Q., Altan, O., Khan, M.R., Baqa, M.F., Lu, L. 2021. Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data. Remote Sensing, 13(12): 2386. https://doi.org/ 10.3390/rs13122386
• Tariq, A., Jiango, Y., Lu, L., Jamil, A., Al-ashkar, I., Kamran, M., Sabagh, A.E. 2023. Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars. Geomatics, Natural Hazards and Risk, 14(1). https://doi.org/10.1080/19475705.2023.2190856
• Tariq, S., ul‐Haq, Z., Mariam, A., Mehmood, U., Ahmed, W. 2023. Assessment of air quality during worst wildfires in Mugla and Antalya regions of Turkey, Natural Hazards, 115(2):1235–1254. https://doi.org/10.1007/s11069-022-05592-5.
• Tuna Tuygun, G., İşsever, G., Elbir, T. 2023. Satellite Monitoring of Burned Forest Areas and Atmospheric Aerosols Originated from Major Forest Fires in Turkey in 2021. The Dokuz Eylul University Faculty of Engineering Journal of Science and Engineering, 25(74): 351-369. https://doi.org/10.21205/deufmd. 2023257408 Wu, B., Zheng, H., Xu, Z., Wu, Z., Zhao, Y. 2022. Forest Burned Area Detection Using a Novel Spectral Index Based on Multi-Objective Optimization. Forests, 13(11): 1787. https://doi.org/10.3390/f13111787
• Yoshioka, M., Fujinaka, T., Omatu, S. 2007. SAR image classification by support vector machine, Image Processing for Remote Sensing. CRC Press Boca Raton, FL, USA, pp. 624–637. https://doi.org/ 10.1201/9781420066654.ch15.
• Yücer, E. 2023. Detection of Burned Areas with Sentinel-2 MSI and Landsat-9 OLI Satellite Images: 2022 Muğla/Marmaris Forest Fire, Kahramanmaras Sutcu Imam University Journal of Engineering Sciences, 26(4): 866–880. https://doi.org/ 10.17780/ksujes.1303299.
• Yüksel, K. 2022. Evaluation of Different Remote Sensing Indices in Detection of Forest Burned Area: A Case Study of 2022 Mersin (Gülnar) Wildfire, ArtGRID - Journal of Architecture Engineering and Fine Arts, 4(2): 160–171. https://doi.org/ 10.57165/artgrid.1179074.
• Zhang, P., Hu, X., Ban, Y., Nascetti, A., Gong, M. 2024. Assessing Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 Data for Large-Scale Wildfire-Burned Area Mapping: Insights from the 2017–2019 Canada Wildfires. Remote Sensing, 16(3): 556. https://doi.org/10.3390/rs16030556