1. J. Rentschler, M. Salhab, and B. A. Jafino, 2022, "Flood exposure and poverty in 188 countries," Nature Communications, vol. 13, no. 1, pp. 3527, doi: 10.1038/s41467-022-30727-4. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240081/. [DOI:10.1038/s41467-022-30727-4]

2. Wallemacq, P., and R. House, 2018, "Economic Losses, Poverty & Disasters 1997-2017," UNISDR, vol. 1, pp. 6-20, doi: 10.13140/RG.2.2.35610.08643.

3. J. Paz, F. Jimenez, and B. Sánchez, 2018, "Urge un manejo sustentable del agua en Tabasco," Technical report, Universidad Nacional Autónoma de México y Asociación Mexicana de Ciencias para el Desarrollo Regional A.C., Ciudad de México.

4. B. D. Keim and R. A. Müller, 2009, Hurricanes of the Gulf of Mexico, Baton Rouge, LA, USA: Louisiana State University Press.

5. K. Jafarzadegan, D. F. Muñoz, H. Moftakhari, J. L. Gutenson, G. Savant, and H. Moradkhani, 2022, "Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers," Natural Hazards and Earth System Sciences, vol. 22, no. 4, pp. 1419-1435, Apr., doi: 10.5194/nhess-22-1419-2022. [DOI:10.5194/nhess-22-1419-2022]

6. M. Labrador-García, J. Evora Brondo, and M. Arbelo, 2012, Satélites de teledetección para la gestión del territorio. Proyecto SATELMAC., [Online].

7. Dana Saeedi, Ehsan Khankeshizadeh, Tayebe Managhebi, Mohammad Karimi, and Ali Mohammadzadeh, 2023, "Land cover classification based on machine learning and deep learning methods using Sentinel-2 satellite images: A case study of the urban area in West Tehran," Journal of Geomatics Science and Technology, vol. 14, no. 2, pp. xx-xx, doi: 10.61186/jgst.14.2.55. [DOI:10.61186/jgst.14.2.55]

8. C. Cleve, M. Kelly, F. R. Kearns, and M. Moritz, 2008, "Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography," Computers, Environment and Urban Systems, vol. 32, no. 4, pp. 317-326, Jul., doi: 10.1016/j.compenvurbsys.2007.10.001. [DOI:10.1016/j.compenvurbsys.2007.10.001]

9. M. Gianinetto, P. Villa, and G. Lechi, 2006, "Postflood damage evaluation using Landsat TM and ETM+ data integrated with DEM," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 1, pp. 236-243, Jan., doi: 10.1109/TGRS.2005.859952. [DOI:10.1109/TGRS.2005.859952]

10. V. S. K. Vanama, Y. S. Rao, and C. M. Bhatt, 2021, "Change detection based flood mapping using multi-temporal Earth observation satellite images: 2018 flood event of Kerala, India," European Journal of Remote Sensing, vol. 54, no. 1, pp. 42-58, Jan., doi: 10.1080/22797254.2020.1867901. [DOI:10.1080/22797254.2020.1867901]

11. A. Mohsenifar, A. Mohammadzadeh, and S. Jamali, 2025, "Unsupervised Rural Flood Mapping from Bi-Temporal Sentinel-1 Images Using an Improved Wavelet-Fusion Flood-Change Index (IWFCI) and an Uncertainty-Sensitive Markov Random Field (USMRF) Model," Remote Sensing, vol. 17, no. 6, p. 1024, Mar., doi: 10.3390/rs17061024. [DOI:10.3390/rs17061024]

12. M. Singha et al., 2020, "Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 166, pp. 278-293, Aug., doi: 10.1016/j.isprsjprs.2020.06.011. [DOI:10.1016/j.isprsjprs.2020.06.011]

13. M. H. A. Baig, L. Zhang, S. Wang, G. Jiang, S. Lu, and Q. Tong, 2013, "Comparison of MNDWI and DFI for water mapping in flooding season," in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul., pp. 2876-2879, doi: 10.1109/IGARSS.2013.6723425. [DOI:10.1109/IGARSS.2013.6723425]

14. A. S. Islam, S. K. Bala, and M. A. Haque, 2010, "Flood inundation map of Bangladesh using MODIS time-series images," Journal of Flood Risk Management, vol. 3, no. 3, pp. 210-222, Sep., doi: 10.1111/j.1753-318X.2010.01074.x. [DOI:10.1111/j.1753-318X.2010.01074.x]

15. L. Ji, L. Zhang, and B. Wylie, 2009, "Analysis of dynamic thresholds for the normalized difference water index," Photogrammetric Engineering and Remote Sensing, vol. 75, no. 11, pp. 1307-1317, doi: 10.14358/PERS.75.11.1307. [DOI:10.14358/PERS.75.11.1307]

16. E. Khankeshizadeh, S. Tahermanesh, A. Mohsenifar, A. Moghimi, and A. Mohammadzadeh, "FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images, https://doi.org/10.1016/j.ecolind.2024.112589 [DOI:10.1016/j.ecolind.2024.112589\]

17. M. Ohki, K. Yamamoto, T. Tadono, and K. Yoshimura, 2020, "Automated processing for flood area detection using ALOS-2 and hydrodynamic simulation data," Remote Sensing, vol. 12, no. 17, doi: 10.3390/rs12172709. [DOI:10.3390/rs12172709]

18. S. U. Lee, S. Yoon Chung, and R. Hong Park, 1990, "A comparative performance study of several global thresholding techniques for segmentation," Computer Vision, Graphics, and Image Processing, vol. 52, no. 2, pp. 171-190, doi: 10.1016/0734-189X(90)90053-X. [DOI:10.1016/0734-189X(90)90053-X]

19. C. Sirirattanapol, N. Tamkuan, M. Nagai, and M. Ito, 2020, "Apply deep learning techniques on classification of single band SAR satellite images," in Geoinformatics for Sustainable Development in Asian Cities, Springer International Publishing, pp. 1-11, doi: 10.1007/978-3-030-33900-5_1. [DOI:10.1007/978-3-030-33900-5_1]

20. A. Y. A. Gharbia, M. Amin, A. E. Mousa, N. Aboualy, G. M. El Banby, and F. E. A. El-Samie, 2020, "Registration-based change detection for SAR images," NRIAG Journal of Astronomy and Geophysics, vol. 9, no. 1, pp. 106-115, Jan., doi: 10.1080/20909977.2020.1723199. [DOI:10.1080/20909977.2020.1723199]

21. Y. Bazi, L. Bruzzone, and F. Melgani, 2005, "An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 874-887, Apr., doi: 10.1109/TGRS.2004.842441. [DOI:10.1109/TGRS.2004.842441]

22. F. Cian, M. Marconcini, and P. Ceccato, 2018, "Normalized difference flood index for rapid flood mapping: Taking advantage of EO big data," Remote Sensing of Environment, vol. 209, pp. 712-730, Oct., doi: 10.1016/j.rse.2018.03.006. [DOI:10.1016/j.rse.2018.03.006]

23. UN-SPIDER, "United Nations Platform for Space-Based Information for Disaster Management and Emergency Response," United Nations Office for Outer Space Affairs, Vienna, Austria. [Online]. Available: https://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-google-earth-engine-flood-mapping/in-detail.

24. F. Bovolo and L. Bruzzone, 2005, "A detail-preserving scale-driven approach to change detection in multitemporal SAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 12, pp. 2963-2972, Nov., doi: 10.1109/TGRS.2005.857987. [DOI:10.1109/TGRS.2005.857987]

25. T. Hame, I. Heiler, and J. S. Miguel-Ayanz, 1998, "An unsupervised change detection and recognition system for forestry," International Journal of Remote Sensing, vol. 19, no. 6, pp. 1079-1099, doi: 10.1080/014311698215612. [DOI:10.1080/014311698215612]

26. M. K. Ridd and J. Liu, 1998, "A comparison of four algorithms for change detection in an urban environment," Remote Sensing of Environment, vol. 63, no. 2, pp. 95-100, doi: 10.1016/S0034-4257(97)00112-0. [DOI:10.1016/S0034-4257(97)00112-0]

27. H. Hu and Y. Ban, 2014, "Unsupervised change detection in multitemporal SAR images over large urban areas," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 8, pp. 3248-3261, Aug., doi: 10.1109/JSTARS.2014.2344017. [DOI:10.1109/JSTARS.2014.2344017]

28. F. Bovolo, C. Marin, and L. Bruzzone, 2013, "A hierarchical approach to change detection in very high resolution SAR images for surveillance applications," IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4, pp. 2042-2054, Apr., doi: 10.1109/TGRS.2012.2223219. [DOI:10.1109/TGRS.2012.2223219]

29. B. DeVries, C. Huang, J. Armston, W. Huang, J. W. Jones, and M. W. Lang, 2020, "Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine," Remote Sensing of Environment, vol. 240, Apr., Art. no. 111664, doi: 10.1016/j.rse.2020.111664. [DOI:10.1016/j.rse.2020.111664]

30. X. Zhang, C. E. Jones, T. Oliver-Cabrera, M. Simard, and S. Fagherazzi, 2022, "Using rapid repeat SAR interferometry to improve hydrodynamic models of flood propagation in coastal wetlands," Advances in Water Resources, vol. 159, Jan., Art. no. 104088, doi: 10.1016/j.advwatres.2021.104088. [DOI:10.1016/j.advwatres.2021.104088]

31. L. Bruzzone and S. B. Serpico, 1997, "An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 4, pp. 858-867, Jul., doi: 10.1109/36.602528. [DOI:10.1109/36.602528]

32. D. Brunner, G. Lemoine, and L. Bruzzone, 2010, "Earthquake damage assessment of buildings using VHR optical and SAR imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 5, pp. 2403-2420, May, doi: 10.1109/TGRS.2009.2038274. [DOI:10.1109/TGRS.2009.2038274]

33. N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, 2017, "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote Sensing of Environment, vol. 202, pp. 18-27, Dec., doi: 10.1016/j.rse.2017.06.031. [DOI:10.1016/j.rse.2017.06.031]

34. NASA Earth Observatory, "Valencia floods," [Online]. Available: https://earthobservatory.nasa.gov/images/153533/valencia-floods. Accessed: [Insert Date].

35. E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari, and H. Moradkhani, "Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023, Art. no. 4201419, doi: 10.1109/TGRS.2023.3240097. [DOI:10.1109/TGRS.2023.3240097]

36. Md Tazmul Islam and Qingmin Meng, "An exploratory study of Sentinel-1 SAR for rapid urban flood mapping on Google Earth Engine," International Journal of Applied Earth Observations and Geoinformation, vol. 113, Art. no. 103002, pp. 1-13, Dec. 2022, doi: 10.1016/j.jag.2022.103002. [DOI:10.1016/j.jag.2022.103002]

37. S. Halder and S. Bose, 2024, "Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach," Natural Hazards Research, vol. xx, no. xx, pp. xxx-xxx, doi: 10.1016/j.nhres.2024.01.002. [DOI:10.1016/j.nhres.2024.01.002]

38. Rahaman, S. N., & Shermin, N. (2022). "Identifying the effect of monsoon floods on vegetation and land surface temperature by using Google Earth Engine," Urban Climate, vol. 43, Art. no. 101162, doi: 10.1016/j.uclim.2022.101162. [DOI:10.1016/j.uclim.2022.101162]

39. G. Singh and K. S. Rawat, 2024, "Mapping flooded areas utilizing Google Earth Engine and open SAR data: A comprehensive approach for disaster response," Discover Geoscience, vol. 2, Art. no. 5, doi: 10.1007/s44288-024-00006-4. [DOI:10.1007/s44288-024-00006-4]

40. H. Mehmood, C. Conway, and D. Perera, "Mapping of flood areas using Landsat with Google Earth Engine cloud platform," Atmosphere, vol. 12, no. 7, Art. no. 866, Jul. 2021, doi: 10.3390/atmos12070866. [DOI:10.3390/atmos12070866]

41. B. Demissie, S. Vanhuysse, T. Grippa, C. Flasse, and E. Wolff, "Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: A case of Dar es Salaam," Geomatics, Natural Hazards and Risk, vol. 14, no. 1, pp. 1-23, Apr. 2023, doi: 10.1080/19475705.2023.2202296. [DOI:10.1080/19475705.2023.2202296]

42. M. Moharrami, M. Javanbakht, and S. Attarchi, "Automatic flood detection using Sentinel-1 images on the Google Earth Engine," Environmental Monitoring and Assessment, vol. 193, Art. no. 248, Apr. 2021, doi: 10.1007/s10661-021-09037-7. [DOI:10.1007/s10661-021-09037-7]

43. Riyanto, I., Rizkinia, M., Arief, R., and Sudiana, D., "Three-Dimensional Convolutional Neural Network on Multi-Temporal Synthetic Aperture Radar Images for Urban Flood Potential Mapping in Jakarta," Applied Sciences, vol. 12, no. 3, Art. no. 1679, pp. 1-19, Feb. 2022, doi: 10.3390/app12031679. [DOI:10.3390/app12031679]

44. Hamidi, E., Peter, B. G., Munoz, D. F., Moftakhari, H., & Moradkhani, H. (2023). Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine. IEEE Transactions on Geoscience and Remote Sensing, 61, 4201419. DOI: 10.1109/TGRS.2023.3240097 [DOI:10.1109/TGRS.2023.3240097]

45. NASA Earth Observatory, "Valencia floods," [Online]. Available: https://earthobservatory.nasa.gov/images/153533/valencia-floods. Accessed: [Insert Date].

46. ArcGIS StoryMaps, "Flood mapping and analysis," [Online]. Available: https://storymaps.arcgis.com/stories/f30e3147d12b456e8c9a654861671743. Accessed: [Insert Date].

47. Z. Malenovsky et al., "Sentinels for science: Potential of Sentinel-1, -2 and −3 missions for scientific observations of ocean, cryosphere, and land," Remote Sens. Environ., vol. 120, pp. 91-101, May 2012, doi: 10.1016/j.rse.2011.09.026. [DOI:10.1016/j.rse.2011.09.026]

48. F. Cian, M. Marconcini, and P. Ceccato, 2018, "Normalized difference flood index for rapid flood mapping: Taking advantage of EO big data," Remote Sensing of Environment, vol. 209, pp. 712-730, Oct., doi: 10.1016/j.rse.2018.03.006. [DOI:10.1016/j.rse.2018.03.006]

49. S. Long, T. E. Fatoyinbo, and F. Policelli, 2014, "Flood extent mapping for Namibia using change detection and thresholding with SAR," Environmental Research Letters, vol. 9, no. 3, Mar., Art. no. 035002, doi: 10.1088/1748-9326/9/3/035002. [DOI:10.1088/1748-9326/9/3/035002]

50. UN-SPIDER, "United Nations Platform for Space-Based Information for Disaster Management and Emergency Response," United Nations Office for Outer Space Affairs, Vienna, Austria. [Online]. Available: https://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-google-earth-engine-flood-mapping/in-detail. Accessed: [Insert Date].

51. S. Xiaolu and C. Bo, 2011, "Change Detection Using Change Vector Analysis from Landsat TM Images in Wuhan," Procedia Environmental Sciences, vol. 11, pp. 238-244, doi: 10.1016/j.proenv.2011.12.037. [DOI:10.1016/j.proenv.2011.12.037]

52. L. Bruzzone and D. F. Prieto, 2000, "Automatic analysis of the difference image for unsupervised change detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 3, pp. 1171-1182, May, doi: 10.1109/36.843009. [DOI:10.1109/36.843009]

53. M. A. Clement, C. G. Kilsby, and P. Moore, 2018, "Multi-temporal synthetic aperture radar flood mapping using change detection," Journal of Flood Risk Management, vol. 11, no. 2, pp. 152-168, Jun., doi: 10.1111/jfr3.12303. [DOI:10.1111/jfr3.12303]

54. R. Sivanpillai, K. M. Jacobs, C. M. Mattilio, and E. V. Piskorski, 2021, "Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images," Frontiers in Earth Science, vol. 15, no. 1, pp. 1-11, Mar., doi: 10.1007/s11707-020-0818-0. [DOI:10.1007/s11707-020-0818-0]

55. K. N. Markert, F. Chishtie, E. R. Anderson, D. Saah, and R. E. Griffin, 2018, "On the merging of optical and SAR satellite imagery for surface water mapping applications," Results in Physics, vol. 9, pp. 275-277, Jun., doi: 10.1016/j.rinp.2018.02.054. [DOI:10.1016/j.rinp.2018.02.054]

56. S. Long, T. E. Fatoyinbo, and F. Policelli, 2014, "Flood extent mapping for Namibia using change detection and thresholding with SAR," Environmental Research Letters, vol. 9, no. 3, Mar., Art. no. 035002, doi: 10.1088/1748-9326/9/3/035002. [DOI:10.1088/1748-9326/9/3/035002]

57. A. Ferral, E. Luccini, A. Aleksinkó, and C. M. Scavuzzo, 2019, "Flooded area satellite monitoring within a Ramsar wetland nature reserve in Argentina," Remote Sensing Applications: Society and Environment, vol. 15, Aug., Art. no. 100230, doi: 10.1016/j.rsase.2019.04.003. [DOI:10.1016/j.rsase.2019.04.003]

58. D. Munasinghe, S. Cohen, Y.-F. Huang, Y.-P. Tsang, J. Zhang, and Z. Fang, 2018, "Intercomparison of satellite remote sensing-based flood inundation mapping techniques," JAWRA Journal of the American Water Resources Association, vol. 54, no. 4, pp. 834-846, Aug., doi: 10.1111/1752-1688.12626. [DOI:10.1111/1752-1688.12626]

59. O. E. J. Wing, P. D. Bates, C. C. Sampson, A. M. Smith, K. A. Johnson, and T. A. Erickson, 2017, "Validation of a 30 m resolution flood hazard model of the conterminous United States," Water Resources Research, vol. 53, no. 9, pp. 7968-7986, Sep., doi: 10.1002/2017WR020917. [DOI:10.1002/2017WR020917]

60. P. Manjusree, L. P. Kumar, C. M. Bhatt, G. S. Rao, and V. Bhanumurthy, "Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images," Int. J. Disaster Risk Sci., vol. 3, no. 2, pp. 113-122, Jun. 2012, doi: 10.1007/s13753-012-0011-5. [DOI:10.1007/s13753-012-0011-5]