Article

From Flood Mapping to Planning-Relevant Flood Intelligence: A Sentinel-1 SAR-Based Framework for Urban Planning and Climate Adaptation in Valencia, Spain

Arezoo Salamatnia ORCID
    Received: 12 February 2026 | Revised: 16 May 2026 | Accepted: 23 May 2026 | Published Online: 30 May 2026

    Abstract

    Flooding is among the most destructive natural hazards affecting urban environments worldwide, with its frequency and severity increasingly worsened by climate change and rapid urban expansion. This study presents an operational flood detection framework based on multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery, applied to the severe flooding event in Valencia, Spain, in late 2024. The proposed workflow integrates pre- and post-event SAR acquisitions within a systematic processing chain that includes radiometric calibration, speckle noise reduction, and multi-temporal change detection. Flood delineation was achieved through threshold-based segmentation and post-classification refinement and independently validated against the Copernicus Emergency Management Service (EMS) rapid-mapping product, achieving an overall agreement of approximately 87%. The analysis identified an inundation extent of 4.96 km2, primarily in the western and northwestern sectors of Valencia. To enhance the practical relevance of the remote sensing results, this study develops a conceptual framework linking satellite-derived flood information with urban resilience planning and disaster risk management objectives. Rather than focusing solely on flood detection performance, the study emphasizes the translation of satellite-derived flood information into planning-relevant knowledge through measurable SDG-oriented planning indicators that support resilience-oriented urban governance. The framework demonstrates how spatial flood intelligence can support monitoring and decision-making processes related to Sustainable Development Goal (SDG) Targets 11.5, 11.b, and 13.1. By connecting satellite-based flood observations with policy-oriented urban planning frameworks, the proposed workflow provides evidence-based support for risk-sensitive land-use planning, infrastructure prioritization, evacuation planning, and post-disaster recovery while offering a transferable framework for municipal disaster risk management and urban resilience.

    Keywords

    Flood Intelligence, Sentinel-1 SAR, Urban Flood Mapping, Climate Adaptation, Conceptual Framework

    References
    1. [1] Al-Wardy, M., Zarei, E., Nikoo, M.R., et al., 2026. Toward enhanced coastal flooding forecasting using deterministic and probabilistic models. Natural Hazards. 122(10), 453. DOI: https://doi.org/10.1007/s11069-026-08201-x
    2. [2] Amitrano, D., Di Martino, G., Di Simone, A., et al., 2024. Flood detection with SAR: A review of techniques and datasets. Remote Sensing. 16(4), 656. DOI: https://doi.org/10.3390/rs16040656
    3. [3] Zhao, J., Li, M., Li, Y., et al., 2024. Urban flood mapping using satellite synthetic aperture radar data: A review of characteristics, approaches, and datasets. arXiv preprint. arXiv:2411.04153. DOI: https://doi.org/10.48550/arXiv.2411.04153
    4. [4] United Nations Office for Disaster Risk Reduction (UNDRR), 2015. Sendai Framework for Disaster Risk Reduction 2015–2030. UNDRR: Geneva, Switzerland. Available from: https://www.undrr.org/media/16176/download?startDownload=20260819
    5. [5] Esmaiel, A., Abdrabo, K.I., Saber, M., et al., 2022. Integration of flood risk assessment and spatial planning for disaster management in Egypt. Progress in Disaster Science. 15, 100245. DOI: https://doi.org/10.1016/j.pdisas.2022.100245
    6. [6] Intergovernmental Panel on Climate Change (IPCC), 2023. Climate Change 2022—Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge, UK. DOI: https://doi.org/10.1017/9781009325844
    7. [7] Chini, M., Pelich, R., Pulvirenti, L., et al., 2019. Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and Hurricane Harvey as a test case. Remote Sensing. 11(2), 107. DOI: https://doi.org/10.3390/rs11020107
    8. [8] Konapala, G., Kumar, S.V., Ahmad, S.K., 2021. Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing. 180, 163–173. DOI: https://doi.org/10.1016/j.isprsjprs.2021.08.016
    9. [9] Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 9(1), 62–66. DOI: https://doi.org/10.1109/TSMC.1979.4310076
    10. [10] Tong, P., 2021. Characteristics, dimensions and methods of current assessment for urban resilience to climate-related disasters: A systematic review of the literature. International Journal of Disaster Risk Reduction. 60, 102276. DOI: https://doi.org/10.1016/j.ijdrr.2021.102276
    11. [11] Salamatnia, A., Jozi, S.A., Malmasi, S., et al., 2019. Assessment of the temporal and spatial variations of urban development using RS and GIS: A case study—Yasuj, Iran. Journal of the Indian Society of Remote Sensing. 47(7), 1163–1172. DOI: https://doi.org/10.1007/s12524-019-00973-z
    12. [12] Salamatnia, A., Jozi, S.A., 2021. Presenting a conceptual model for urban ecological resilience with a multi-scale approach (case study: Yasuj, Iran). Arabian Journal of Geosciences. 14(2), 104. DOI: https://doi.org/10.1007/s12517-020-06355-x
    13. [13] United Nations Statistics Division, United Nations Office for Disaster Risk Reduction (UNDRR), 2024. SDG Indicator Metadata. UNDRR: Geneva, Switzerland. Available from: https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-01.pdf
    14. [14] Sohn, W., Kim, J.-H., Li, M.-H., et al., 2020. How does increasing impervious surfaces affect urban flooding in response to climate variability? Ecological Indicators. 118, 106774. DOI: https://doi.org/10.1016/j.ecolind.2020.106774
    15. [15] European Space Agency, 2024. Devastating floods in Spain witnessed by satellites. Available from: https://www.esa.int/Applications/Observing_the_Earth/Devastating_floods_in_Spain_witnessed_by_satellites (cited 10 February 2026).
    16. [16] García, P.L.M., 2025. Fact-check: AEMET's reference to Cuenca did not imply that the storm was easing over Valencia despite Mazón's claim. Available from: https://www.newtral.es/serrania-cuenca-dana-factcheck/20251104/ (cited 10 February 2026). (in Spanish)
    17. [17] Rokkam, K.K., Rani, S., Kumar, K., et al., 2024. SAR-FM: SAR-based flood rapid mapping using multi-modal data fusion. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, Edinburgh, UK, 16–20 September 2024. DOI: https://doi.org/10.1117/12.3031427
    18. [18] Garshasbi, M., Alizadeh, H., Mojaradi, B., et al., 2025. Uncertainty-aware flood inundation mapping with a Bayesian deep learning framework using SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18, 26716–26726. DOI: https://doi.org/10.1109/JSTARS.2025.3610403
    19. [19] Pangi, P., Bagja, I.L., Budiati, L., et al., 2024. The evaluation of spatial planning for flood disasters using Sentinel-1 satellite imagery (case study: Central Java Province). IOP Conference Series: Earth and Environmental Science. 1418(1), 012050. DOI: https://doi.org/10.1088/1755-1315/1418/1/012050
    20. [20] Tupas, M.E., Roth, F., Bauer-Marschallinger, B., et al., 2024. Assessment of time-series-derived no-flood references for SAR-based Bayesian flood mapping. GIScience & Remote Sensing. 61(1), 2427304. DOI: https://doi.org/10.1080/15481603.2024.2427304
    21. [21] Lahsaini, M., Albano, F., Albano, R., et al., 2024. A synthetic aperture radar-based robust satellite technique for timely mapping of floods. Remote Sensing. 16(12), 2193. DOI: https://doi.org/10.3390/rs16122193
    22. [22] Jiang, X., Zeng, Z., 2025. Empowering multi-source SAR flood mapping with unsupervised learning. Environmental Research Letters. 20(1), 014006. DOI: https://doi.org/10.1088/1748-9326/ad9491
    23. [23] Tran, K.H., Menenti, M., Jia, L., 2022. Surface water mapping and flood monitoring in the Mekong Delta using Sentinel-1 SAR Time Series and Otsu Threshold. Remote Sensing. 14(22), 5721. DOI: https://doi.org/10.3390/rs14225721
    24. [24] Lacava, T., Albano, R., Lahsaini, M., et al., 2024. On a multitemporal analysis of Copernicus Sentinel data for a robust and near-real-time mapping of floods. In Proceedings of the 18th Plinius Conference on Mediterranean Risks, Chania, Greece, 30 September–3 October 2024. DOI: https://doi.org/10.5194/egusphere-plinius18-75
    25. [25] Twele, A., Cao, W., Plank, S., et al., 2016. Sentinel-1-based flood mapping: A fully automated processing chain. International Journal of Remote Sensing. 37(13), 2990–3004. DOI: https://doi.org/10.1080/01431161.2016.1192304
    26. [26] Copernicus Emergency Management Service, 2024. EMSR773: Flood in Valencia, Spain. Available from: https://mapping.emergency.copernicus.eu/activations/EMSR773/ (cited 10 February 2026).
    27. [27] Tarpanelli, A., Mondini, A.C., Camici, S., 2022. Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe. Natural Hazards and Earth System Sciences. 22(7), 2473–2489. DOI: https://doi.org/10.5194/nhess-22-2473-2022
    28. [28] Palomba, G., Farasin, A., Rossi, C., 2020. Sentinel-1 flood delineation with supervised machine learning. In Proceedings of the 17th International Conference on Information Systems for Crisis Response and Management, Blacksburg, VA, USA, 24–27 May 2020; pp. 1072–1083. Available from: https://idl.iscram.org/files/giuliopalomba/2020/2298_GiulioPalomba_etal2020.pdf
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