DOI: https://doi.org/10.14710/jwl.14.2.197-219

Rapid Flood Mapping in Surabaya and Gresik using SAR and Optical Satellites: Leveraging Google Earth Engine for Timely Disaster Response

*Zulfikar Adlan Nadzir orcid scopus  -  Geomatics Engineering, Institut Teknologi Sumatera, Lampung, Indonesia, Indonesia
Mutia Rahma Apriani  -  Geomatics Engineering, Institut Teknologi Sumatera, Lampung, Indonesia, Indonesia
Muhammad Rohmaneo Darminto  -  Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia

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Abstract
Flooding in urban and semi-urban areas of Indonesia, particularly in Surabaya and Gresik, has become an ongoing challenge, exacerbated by rapid urbanization and climate change. This study explores the application of Sentinel-1 Dual-Polarization Water Index (SDWI) and Normalized Difference Water Index (NDWI) derived from Sentinel-2 and Landsat 9 imagery to map flood extents during three major flood events in 2022, 2023 and 2024. The analysis is conducted on a cloud-based Google Earth Engine (GEE) platform to enable near real-time flood monitoring with significantly faster processing time. The results indicate that NDWI is useful in detecting large-scale water bodies and coastal flooding, whereas SDWI performs better in urban and vegetated environments, capturing inundation in built-up areas that NDWI may overlook. The comparison of satellite-derived flood maps with ground-truth data from news reports confirms that both indices can provide indicative flood assessments where SDWI performs slightly better than NDWI. However, discrepancies between remote sensing data and ground observations suggest the need for a hybrid approach combining satellite data with ground-based observations to enhance flood mapping accuracy. The integration of these indices on cloud-based platforms like Google Earth Engine (GEE) offers a scalable solution for rapid flood mapping, supporting emergency response and disaster risk reduction efforts in Indonesia, particularly in flood-prone regions.
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Keywords: Disaster Response; Flood Mapping; GEE; Indonesia; Optical; SAR

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  1. Abidin, H. Z., Andreas, H., Djaja, R., Darmawan, D., & Gamal, M. (2008). Land subsidence characteristics of Jakarta between 1997 and 2005, as estimated using GPS surveys. GPS Solutions, 12(1), 23–32. https://doi.org/10.1007/s10291-007-0061-0
  2. Abidin, H. Z., Andreas ,H., Gumilar ,I., Sidiq ,T.P., & and Fukuda, Y. (2013). Land subsidence in coastal city of Semarang (Indonesia): Characteristics, impacts and causes. Geomatics, Natural Hazards and Risk, 4(3), 226–240. https://doi.org/10.1080/19475705.2012.692336
  3. Aggarwal, A. (2016). Exposure, hazard and risk mapping during a flood event using open source geospatial technology. Geomatics, Natural Hazards and Risk, 7(4), 1426–1441. https://doi.org/10.1080/19475705.2015.1069408
  4. Ahmed, K. R., & Akter, S. (2017). Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data. Remote Sensing Applications: Society and Environment, 8, 168–181. https://doi.org/10.1016/j.rsase.2017.08.010
  5. Antunes da Silva, L., Rudorff, C., Ovando, A., Pimentel, A., Cuartas, L. A., & dos Santos Alvalá, R. C. (2024). Inundation mapping using hydraulic modeling with high-resolution remote sensed data: A case study in the Acre River Basin, Brazil. Modeling Earth Systems and Environment, 10(3), 3051–3066. https://doi.org/10.1007/s40808-024-01972-x
  6. Aprillya, M. R., & Chasanah, U. (2021). Analisis Lahan Pertanian Rawan Banjir Menggunakan Metode Multi Atribut Utility Theory Berbasis Sistem Informasi Geografis. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 16(2), 148. https://doi.org/10.30872/jim.v16i2.6554
  7. Arlisa, S. D., & Handayani, H. H. (2023). Flood Vulnerability Analysis using Random Forest Method in Gresik Regency, Indonesia. IOP Conference Series: Earth and Environmental Science, 1127(1), 012023. https://doi.org/10.1088/1755-1315/1127/1/012023
  8. Assumpção, T. H., Popescu, I., Jonoski, A., & Solomatine, D. P. (2018). Citizen observations contributing to flood modelling: Opportunities and challenges. Hydrology and Earth System Sciences, 22(2), 1473–1489. https://doi.org/10.5194/hess-22-1473-2018
  9. Atefi, M. R., & Miura, H. (2022). Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan. Remote Sensing, 14(15), Article 15. https://doi.org/10.3390/rs14153647
  10. Aubrecht, C., Özceylan, D., Steinnocher, K., & Freire, S. (2013). Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Natural Hazards, 68(1), 147–163. https://doi.org/10.1007/s11069-012-0389-9
  11. Aulady, M. F. N., Nuciferani, F. T., & Agustin, N. (2022). PENYEBAB DAN ALTERNATIF MITIGASI BENCANA BANJIR DI GRESIK SELATAN. Bearing : Jurnal Penelitian Dan Kajian Teknik Sipil, 7(3), Article 3. https://doi.org/10.32502/jbearing.4648202273
  12. Avila-Aceves, E., Plata-Rocha, W., Monjardin-Armenta, S. A., & Rangel-Peraza, J. G. (2023). Geospatial modelling of floods: A literature review. Stochastic Environmental Research and Risk Assessment, 37(11), 4109–4128. https://doi.org/10.1007/s00477-023-02505-1
  13. Cahyono, B. E., Cahyatri, P., Purwandari, E., Misto, & Febrianti, N. (2021). Mapping flooded risk area in East Java Indonesia using remote sensing data. Journal of Physics: Conference Series, 1825(1), 012081. https://doi.org/10.1088/1742-6596/1825/1/012081
  14. Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P. W., Trisos, C., Romero, J., Aldunce, P., Barrett, K., Blanco, G., Cheung, W. W. L., Connors, S., Denton, F., Diongue-Niang, A., Dodman, D., Garschagen, M., Geden, O., Hayward, B., Jones, C., … Péan, C. (2023). IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. (First). Intergovernmental Panel on Climate Change (IPCC). https://doi.org/10.59327/IPCC/AR6-9789291691647
  15. Cao, A., Esteban, M., Valenzuela, V. P. B., Onuki, M., Takagi, H., Thao, N. D., & Tsuchiya, N. (2021). Future of Asian Deltaic Megacities under sea level rise and land subsidence: Current adaptation pathways for Tokyo, Jakarta, Manila, and Ho Chi Minh City. Current Opinion in Environmental Sustainability, 50, 87–97. https://doi.org/10.1016/j.cosust.2021.02.010
  16. Castaneda, R., Garcia-Sucerquia, J., & Doblas, A. (2021). Speckle noise reduction in coherent imaging systems via hybrid median–mean filter. Optical Engineering, 60(12), 123107. https://doi.org/10.1117/1.OE.60.12.123107
  17. Chaussard, E., Amelung, F., Abidin, H., & Hong, S.-H. (2013). Sinking cities in Indonesia: ALOS PALSAR detects rapid subsidence due to groundwater and gas extraction. Remote Sensing of Environment, 128, 150–161. https://doi.org/10.1016/j.rse.2012.10.015
  18. Chen, S.-W. (2020). SAR Image Speckle Filtering With Context Covariance Matrix Formulation and Similarity Test. IEEE Transactions on Image Processing, 29, 6641–6654. https://doi.org/10.1109/TIP.2020.2992883
  19. Chini, M., Hostache, R., Giustarini, L., & Matgen, P. (2017). A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6975–6988. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2017.2737664
  20. Djalante, R. (2018). Review article: A systematic literature review of research trends and authorships on natural hazards, disasters, risk reduction and climate change in Indonesia. Natural Hazards and Earth System Sciences, 18(6), 1785–1810. https://doi.org/10.5194/nhess-18-1785-2018
  21. Eccles, R., Zhang, H., & Hamilton, D. (2019). A review of the effects of climate change on riverine flooding in subtropical and tropical regions. Journal of Water and Climate Change, 10(4), 687–707. https://doi.org/10.2166/wcc.2019.175
  22. Erkens, G., Bucx, T., Dam, R., de Lange, G., & Lambert, J. (2015). Sinking coastal cities. Proceedings of IAHS, 372, 189–198. https://doi.org/10.5194/piahs-372-189-2015
  23. Farhadi, H., Ebadi, H., Kiani, A., & Asgary, A. (2024). A novel flood/water extraction index (FWEI) for identifying water and flooded areas using sentinel-2 visible and near-infrared spectral bands. Stochastic Environmental Research and Risk Assessment, 38(5), 1873–1895. https://doi.org/10.1007/s00477-024-02660-z
  24. Farhadi, H., Ebadi, H., Kiani, A., & Asgary, A. (2025). Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI). Computers & Geosciences, 194, 105742. https://doi.org/10.1016/j.cageo.2024.105742
  25. Fristyananda, M. A., & Idajati, H. (2017). Tingkat Bahaya Bencana Banjir di Kali Lamong Kabupaten Gresik. Jurnal Teknik ITS, 6(1), 56–59. https://doi.org/10.12962/j23373539.v6i1.22103
  26. Grimaldi, S., Li, Y., Pauwels, V. R. N., & Walker, J. P. (2016). Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges. Surveys in Geophysics, 37(5), 977–1034. https://doi.org/10.1007/s10712-016-9378-y
  27. Guo, J., Luan, Y., Li, Z., Liu, X., Li, C., & Chang, X. (2021). Mozambique Flood (2019) Caused by Tropical Cyclone Idai Monitored From Sentinel-1 and Sentinel-2 Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8761–8772. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2021.3107279
  28. Hariadi, M. H., Van Der Schrier, G., Steeneveld, G.-J., Sutanto, S. J., Sutanudjaja, E., Ratri, D. N., Sopaheluwakan, A., & Klein Tank, A. (2024). A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia. Hydrology and Earth System Sciences, 28(9), 1935–1956. https://doi.org/10.5194/hess-28-1935-2024
  29. Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D.,
  30. Watanabe, S., Kim, H., & Kanae, S. (2013). Global flood risk under climate change. Nature Climate Change, 3(9), 816–821. https://doi.org/10.1038/nclimate1911
  31. Karnaji, K., Susanti, E., Ariadi, S., & Saud, M. (2024). Social impacts and post-disaster management in disaster-prone areas of East Java, Indonesia. Jamba : Journal of Disaster Risk Studies, 16(1), 1747. https://doi.org/10.4102/jamba.v16i1.1747
  32. Kim, J., Kim, H., Kim, D., Song, J., & Li, C. (2022). Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing. Remote Sensing, 14(24), Article 24. https://doi.org/10.3390/rs14246373
  33. Klemas, V. (2014). Remote Sensing of Floods and Flood-Prone Areas: An Overview. Journal of Coastal Research, 31(4), 1005–1013. https://doi.org/10.2112/JCOASTRES-D-14-00160.1
  34. Kuenzer, C., Guo, H., Huth, J., Leinenkugel, P., Li, X., & Dech, S. (2013). Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses. Remote Sensing, 5(2), Article 2. https://doi.org/10.3390/rs5020687
  35. Kupidura, P. (2016). COMPARISON OF FILTERS DEDICATED TO SPECKLE SUPPRESSION IN SAR IMAGES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7, 269–276. XXIII ISPRS Congress, Commission VII (Volume XLI-B7) - 12–19 July 2016, Prague, Czech Republic. https://doi.org/10.5194/isprs-archives-XLI-B7-269-2016
  36. Li, Z., Xu, S., & Weng, Q. (2024). Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data. ISPRS Journal of Photogrammetry and Remote Sensing, 216, 185–199. https://doi.org/10.1016/j.isprsjprs.2024.07.022
  37. Loo, Y. Y., Billa, L., & Singh, A. (2015). Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geoscience Frontiers, 6(6), 817–823. https://doi.org/10.1016/j.gsf.2014.02.009
  38. Luu, C., Tran, H. X., Pham, B. T., Al-Ansari, N., Tran, T. Q., Duong, N. Q., Dao, N. H., Nguyen, L. P., Nguyen, H. D., Thu Ta, H., Le, H. V., & Meding, J. von. (2020). Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability, 12(7), Article 7. https://doi.org/10.3390/su12073058
  39. Malinowski, R., Groom, G., Schwanghart, W., & Heckrath, G. (2015). Detection and Delineation of Localized Flooding from WorldView-2 Multispectral Data. Remote Sensing, 7(11), Article 11. https://doi.org/10.3390/rs71114853
  40. Marfai, M. A., Sekaranom, A. B., & Ward, P. (2015). Community responses and adaptation strategies toward flood hazard in Jakarta, Indonesia. Natural Hazards, 75(2), 1127–1144. https://doi.org/10.1007/s11069-014-1365-3
  41. Martinis, S., & Rieke, C. (2015). Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany. Remote Sensing, 7(6), Article 6. https://doi.org/10.3390/rs70607732
  42. Munawar, H. S., Hammad, A. W. A., & Waller, S. T. (2022). Remote Sensing Methods for Flood Prediction: A Review. Sensors, 22(3), Article 3. https://doi.org/10.3390/s22030960
  43. Muñoz, D. F., Muñoz, P., Moftakhari, H., & Moradkhani, H. (2021). From local to regional compound flood mapping with deep learning and data fusion techniques. Science of The Total Environment, 782, 146927. https://doi.org/10.1016/j.scitotenv.2021.146927
  44. Nadzir, Z. A., Simarmata, N., & Aliffia, A. (2020). PENGEMBANGAN ALGORITMA IDENTIFIKASI SAWAH PADI BERDASARKAN SPEKTRA FASE PADI (STUDI KASUS: LAMPUNG SELATAN) (Paddy Field Identification Algorithm Development Using Spectral Value of Paddy Field (Case Study: South Lampung)). JURNAL SAINS INFORMASI GEOGRAFIS, 3(1), 23. https://doi.org/10.31314/jsig.v3i1.537
  45. Nasution, B. I., Saputra, F. M., Kurniawan, R., Ridwan, A. N., Fudholi, A., & Sumargo, B. (2022). Urban vulnerability to floods investigation in jakarta, Indonesia: A hybrid optimized fuzzy spatial clustering and news media analysis approach. International Journal of Disaster Risk Reduction, 83, 103407. https://doi.org/10.1016/j.ijdrr.2022.103407
  46. Nguyen, T. H., Ricci, S., Fatras, C., Piacentini, A., Delmotte, A., Lavergne, E., & Kettig, P. (2022). Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–22. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2022.3147429
  47. Nicholls, R. J., Lincke, D., Hinkel, J., Brown, S., Vafeidis, A. T., Meyssignac, B., Hanson, S. E., Merkens, J.-L., & Fang, J. (2021). A global analysis of subsidence, relative sea-level change and coastal flood exposure. Nature Climate Change, 11(4), 338–342. https://doi.org/10.1038/s41558-021-00993-z
  48. Pusat Krisis Kesehatan. (2022). Informasi Awal Banjir di 1 Kecamatan, GRESIK, JAWA-TIMUR. Kementerian Kesehatan Republik Indonesia. https://pusatkrisis.kemkes.go.id/Banjir-di-GRESIK-JAWA-TIMUR-06-07-2022-90
  49. Rahman, Md. S., & Di, L. (2017). The state of the art of spaceborne remote sensing in flood management. Natural Hazards, 85(2), 1223–1248. https://doi.org/10.1007/s11069-016-2601-9
  50. Rehman, S., Sahana, M., Hong, H., Sajjad, H., & Ahmed, B. B. (2019). A systematic review on approaches and methods used for flood vulnerability assessment: Framework for future research. Natural Hazards, 96(2), 975–998. https://doi.org/10.1007/s11069-018-03567-z
  51. Rosser, J. F., Leibovici, D. G., & Jackson, M. J. (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards, 87(1), 103–120. https://doi.org/10.1007/s11069-017-2755-0
  52. Sadiq, R., Akhtar, Z., Imran, M., & Ofli, F. (2022). Integrating remote sensing and social sensing for flood mapping. Remote Sensing Applications: Society and Environment, 25, 100697. https://doi.org/10.1016/j.rsase.2022.100697
  53. Santoso, E. B. (2013). MANAJEMEN RISIKO BENCANA BANJIR KALI LAMONG PADA KAWASAN PERI-URBAN SURABAYA-GRESIK MELALUI PENDEKATAN KELEMBAGAAN. Jurnal Penataan Ruang, 8(2)
  54. Sari, O. L., Yanti, R. M. K., Fauzi, A., & Patabang, A. M. P. (2021). Effect of flood risk management methods on urban flooding: Article review. Teknika: Jurnal Sains Dan Teknologi, 17(2), 301. https://doi.org/10.36055/tjst.v17i2.12455
  55. Schnebele, E., & Cervone, G. (2013). Improving remote sensing flood assessment using volunteered geographical data. Natural Hazards and Earth System Sciences, 13(3), 669–677. https://doi.org/10.5194/nhess-13-669-2013
  56. Schumann, G. J.-P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts A/B/C, 83–84, 84–95. https://doi.org/10.1016/j.pce.2015.05.002
  57. See, L. (2019). A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding. Frontiers in Earth Science, 7. https://doi.org/10.3389/feart.2019.00044
  58. Shofwan, M., & Prabhaswara, L. M. (2023). Karakteristik Kerentanan Banjir Di Kecamatan Driyorejo. COMPACT: Spatial Development Journal, 2(1), Article 1. https://doi.org/10.35718/compact.v2i1.850
  59. Sholichin, M., Prayogo, T. B., Hendrawan, A. P., Fidari, J. S., Hidayati, A. S. D. S. N., & Sulilo, A. A. (2024). Using hecras modeling to analysis of flood disaster at Kali Lamong, East Java. AIP Conference Proceedings, 3043(1), 030002. https://doi.org/10.1063/5.0206590
  60. Simarmata, N., Nadzir, Z. A., & Sari, D. N. (2023). Analisis Perubahan Garis Pantai menggunakan Metode Sentinel-1 Dual-Polarized Water Index (SDWI) berbasis Data Multitemporal pada Google Earth Engine. Geomatika, 29(2), 107–120
  61. Singh, S., & Kansal, M. L. (2022). Chamoli flash-flood mapping and evaluation with a supervised classifier and NDWI thresholding using Sentinel-2 optical data in Google earth engine. Earth Science Informatics, 15(2), 1073–1086. https://doi.org/10.1007/s12145-022-00786-8
  62. Siswanto, R. D., & Puspaningtyas, A. (2023). Implementasi Kebijakan Penanggulangan Bencana dan Mitigasi Banjir di Kabupaten Gresik. Policy and Maritime Review, 21–31. https://doi.org/10.30649/pmr.v2i1.42
  63. Soria-Ruiz, J., Fernandez-Ordoñez, Y. M., Ambrosio-Ambrosio, J. P., Escalona-Maurice, M. J., Medina-García, G., Sotelo-Ruiz, E. D., & Ramirez-Guzman, M. E. (2022). Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms. Atmosphere, 13(11), Article 11. https://doi.org/10.3390/atmos13111852
  64. Sun, Z., Luo, J., Yang, J., Yu, Q., Zhang, L., Xue, K., & Lu, L. (2020). Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine. Remote Sensing, 12(18), Article 18. https://doi.org/10.3390/rs12183086
  65. Sy, B., Frischknecht, C., Dao, H., Consuegra, D., & Giuliani, G. (2019). Flood hazard assessment and the role of citizen science. Journal of Flood Risk Management, 12(S2), e12519. https://doi.org/10.1111/jfr3.12519
  66. Ta, L., Yu ,Chen, Li ,Zhenhong, Hu ,Xiaoning, Song ,Chuang, Huang ,Wubiao, & and Zhou, M. (2024). Dynamic flood mapping by a normalized probabilistic classification method using satellite radar amplitude time series. GIScience & Remote Sensing, 61(1), 2380125. https://doi.org/10.1080/15481603.2024.2380125
  67. Tanim, A. H., McRae, C. B., Tavakol-Davani, H., & Goharian, E. (2022). Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning. Water, 14(7), Article 7. https://doi.org/10.3390/w14071140
  68. Taslyanto, C. A. P., & Deviantari, U. W. (2024). Spatial-Based Flood Susceptibility Mapping Using Analytical Hierarchy Process Techniques (Case Study: Gresik Regency). IOP Conference Series: Earth and Environmental Science, 1418(1), 012037. https://doi.org/10.1088/1755-1315/1418/1/012037
  69. Triana, Y. T., & Hidayah, Z. (2020). KAJIAN POTENSI DAERAH RAWAN BANJIR ROB DAN ADAPTASI MASYARAKAT DI WILAYAH PESISIR UTARA SURABAYA. Juvenil:Jurnal Ilmiah Kelautan Dan Perikanan, 1(1), 141–150. https://doi.org/10.21107/juvenil.v1i1.6961
  70. Twele, A., Cao ,Wenxi, Plank ,Simon, & and Martinis, S. (2016). Sentinel-1-based flood mapping: A fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1192304
  71. Uddin, K., Matin, M. A., & Meyer, F. J. (2019). Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh. Remote Sensing, 11(13), Article 13. https://doi.org/10.3390/rs11131581
  72. Ward, P. J., Jongman, B., Weiland, F. S., Bouwman, A., van Beek, R., Bierkens, M. F. P., Ligtvoet, W., & Winsemius, H. C. (2013). Assessing flood risk at the global scale: Model setup, results, and sensitivity. Environmental Research Letters, 8(4), 044019. https://doi.org/10.1088/1748-9326/8/4/044019
  73. Xue, W., Yang, H., Wu, Y., Kong, P., Xu, H., Wu, P., & Ma, X. (2021). Water Body Automated Extraction in Polarization SAR Images With Dense-Coordinate-Feature-Concatenate Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 12073–12087. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2021.3129182
  74. Yagoub, M. M., Alsereidi, A. A., Mohamed, E. A., Periyasamy, P., Alameri, R., Aldarmaki, S., & Alhashmi, Y. (2020). Newspapers as a validation proxy for GIS modeling in Fujairah, United Arab Emirates: Identifying flood-prone areas. Natural Hazards, 104(1), 111–141. https://doi.org/10.1007/s11069-020-04161-y
  75. Yamamoto, K., Sayama, T., & Apip. (2021). Impact of climate change on flood inundation in a tropical river basin in Indonesia. Progress in Earth and Planetary Science, 8(1), 5. https://doi.org/10.1186/s40645-020-00386-4
  76. Yin, J., Jonkman, S., Lin, N., Yu, D., Aerts, J., Wilby, R., Pan, M., Wood, E., Bricker, J., Ke, Q., Zeng, Z., Zhao, Q., Ge, J., & Wang, J. (2020). Flood Risks in Sinking Delta Cities: Time for a Reevaluation? Earth’s Future, 8(8), e2020EF001614. https://doi.org/10.1029/2020EF001614
  77. Yu, Y., d’Alessandro, M. M., Tebaldini, S., & Liao, M. (2020). Signal Processing Options for High Resolution SAR Tomography of Natural Scenarios. Remote Sensing, 12(10), Article 10. https://doi.org/10.3390/rs12101638
  78. Zhao, B., Sui, H., Liu, J., Shi, W., Wang, W., Xu, C., & Wang, J. (2024). Flood inundation monitoring using multi-source satellite imagery: A knowledge transfer strategy for heterogeneous image change detection. Remote Sensing of Environment, 314, 114373. https://doi.org/10.1016/j.rse.2024.114373
  79. Zhu, X. X., & Bamler, R. (2014). Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion. IEEE Signal Processing Magazine, 31(4), 51–58. IEEE Signal Processing Magazine. https://doi.org/10.1109/MSP.2014.2312098

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