1 Viet Nam institute of Meteorology, Hydrology & Climate Change;

*Correspondence:; Tel.: +84–913212325


Using Sentinel-1 image series with 10m resolution from the past to present by Principal Component Analysis (PCA) method for Ho Chi Minh City area, helping to assess flood risk due to canal leveling, slitting in some complicated times, narrows the space for water regulation. In addition, Climate change causes sea level rise, thereby increasing the existing water level along with that in large rivers and also causing storm surge, which coincides with the time of flood discharge at Dau Tieng and Tri An reservoirs. The situation of groundwater exploitation, subsidence of the existing ground is continuous and increasing. With the accumulated settlement estimated to date about 100 cm, the current settlement rate is about 2-5 cm per year. Particularly in concentrated areas such as commercial works, the subsidence rate is about 7-8 cm per year. The rate of land subsidence is about twice as high as sea level rise. Therefore, Ho Chi Minh City is one of the cities affected by flooding due to high tide, especially in the current climate change conditions. According to the statistics of flood-prone areas due to high tides from 2014 to 2022, Can Gio district has the highest risk of flooding, with a flooded area of up to 3.713.236 hectares. The districts with an extremely high risk of flooding after Can Gio district are Cu Chi district, Binh Chanh district, and Nha Be district, with 1,764,564 ha, and 1,296,246 ha, and 1,012,550 hectares.


Cite this paper

Phong, D.H. Flood risk assessment from high tide based on principal component analysis (PCA) of Sentinel-1 satellite images sequence for Ho Chi Minh City. J. Hydro-Meteorol. 2023, 16, 65-76. 


1. Ulaby, F.; Moore, R.; Fung, A. Microwave Remote Sensing. Active and Passive, Artech House: Norwood, MA, USA, 1986, 3, pp. 608.

2. Boni, G.; Ferraris, L.; Pulvirenti, L.; Squicciarino, G.; Pierdicca, N.; Candela, L.; Pisani, A.R.; Zoffoli, S.; Onori, R.; Proietti, C.; et al. A Prototype System for Flood Monitoring Based on Flood Forecast Combined with COSMO-SkyMed and Sentinel-1 Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 20169, 2794–2805.

3. Chen, Y.; Fan, R.; Yang, X.; Wang, J.; Latif, A. Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water 201810, 585.

4. Martinis, S.; Twele, A.; Strobl, C.; Kersten, J.; Stein, E. A multi-scale flood monitoring system based on fully automatic MODIS and terraSAR-X processing chains. Remote Sens. 20135, 5598.

5. Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L. An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Nat. Hazards Earth Syst. Sci. 201111, 529–540.

6. Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L. Monitoring flood evolution in vegetated areas using cosmo-skymed data: The tuscany 2009 case study. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 20136, 1807–1816.

7. Uddin, K.; Matin, M.A.; Meyer, F.J. Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh. Remote Sens. 201911, 1581.

8. Liang, J.; Liu, D. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery. ISPRS J. Photogramm. Remote Sens. 2020159, 53–62.

9. Schlaffer, S.; Matgen, P.; Hollaus, M.; Wagner, W. Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int. J. Appl. Earth Obs. Geoinf. 201538, 15–24.

10. Wendleder, A.; Wessel, B.; Roth, A.; Breunig, M.; Martin, K.; Wagenbrenner, S. TanDEM-X water indication mask: Generation and first evaluation results. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 20136, 171–179.

11. Chini, M.; Pulvirenti, L.; Pierdicca, N.; Guerriero, L. Multi-temporal segmentation of Cosmo-SkyMed SAR data for flood monitoring. In Proceedings of the 2011 Joint Urban Remote Sensing Event—JURSE 2011, Munich, Germany, 11–13 April 2011.

12. Chini, M.; Hostache, R.; Giustarini, L.; Matgen, P. A hierarchical split-based approach for parametric thresholding of SAR images: Flood inundation as a test case. IEEE Trans. Geosci. Remote Sens. 201755, 6975–6988.

13. Giustarini, L.; Hostache, R.; Matgen, P.; Schumann, G.J.P.; Bates, P.D.; Mason, D.C. A change detection approach to flood mapping in Urban areas using TerraSAR-X. IEEE Trans. Geosci. Remote Sens. 201351, 2417–2430.

14. Giustarini, L.; Hostache, R.; Kavetski, D.; Chini, M.; Corato, G.; Schlaffer, S.; Matgen, P. Probabilistic Flood Mapping Using Synthetic Aperture Radar Data. IEEE Trans. Geosci. Remote Sens. 201654, 6958–6969.

15. Greifeneder, F.; Wagner, W.; Sabel, D.; Naeimi, V. Suitability of SAR imagery for automatic flood mapping in the Lower Mekong Basin. Int. J. Remote Sens. 201435, 2857–2874.

16. Manjusree, P.; Prasanna Kumar, L.; Bhatt, C.M.; Rao, G.S.; Bhanumurthy, V. Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. Int. J. Disaster Risk Sci. 20123, 113–122.

17. Marti-Cardona, B.; Dolz-Ripolles, J.; Lopez-Martinez, C. Wetland inundation monitoring by the synergistic use of ENVISAT/ASAR imagery and ancilliary spatial data. Remote Sens. Environ. 2013139, 171–184.

18. Martinis, S.; Kersten, J.; Twele, A. A fully automated TerraSAR-X based flood service. ISPRS J. Photogramm. Remote Sens. 2015104, 203–212.

19. Long, S.; Fatoyinbo, T.E.; Policelli, F. Flood extent mapping for Namibia using change detection and thresholding with SAR. Environ. Res. Lett. 20149, 035002.

20. Clement, M.A.; Kilsby, C.G.; Moore, P. Multi-temporal synthetic aperture radar flood mapping using change detection. J. Flood Risk Manag. 201811, 152–168.

21. Chini, M.; Pelich, R.; Pulvirenti, L.; Pierdicca, N.; Hostache, R.; Matgen, P. Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane harvey as a test case. Remote Sens. 201911, 107.

22. Pierdicca, N.; Chini, M.; Pulvirenti, L.; Macina, F. Integrating physical and topographic information into a fuzzy scheme to map flooded area by SAR. Sensors 20088, 4151.

23. Twele, A.; Cao, W.; Plank, S.; Martinis, S. Sentinel-1-based flood mapping: A fully automated processing chain. Int. J. Remote Sens. 201637, 2990–3004.

24. Grimaldi, S.; Xu, J.; Li, Y.; Pauwels, V.R.N.; Walker, J.P. Flood mapping under vegetation using single SAR acquisitions. Remote Sens. Environ. 2020237, 111582.

25. Tsyganskaya, V.; Martinis, S.; Marzahn, P. Flood monitoring in vegetated areas using multitemporal Sentinel-1 data: Impact of time series features. Water 201911, 1938.

26. Henderson, F.M.; Lewis, A.J. Radar detection of wetland ecosystems: A review. Int. J. Remote Sens. 200829, 5809–5835.

27. Hess, L.L.; Melack, J.M.; Simonett, D.S. Radar detection of flooding beneath the forest canopy: A review. Int. J. Remote Sens. 199011, 1313–1325.

28. Richards, J.A.; Sun, G.Q.; Simonett, D.S. L-Band Radar Backscatter Modeling of Forest Stands. IEEE Trans. Geosci. Remote Sens. 1987GE-25, 487–498.

29. Cohen, J.; Riihimäki, H.; Pulliainen, J.; Lemmetyinen, J.; Heilimo, J. Implications of boreal forest stand characteristics for X-band SAR flood mapping accuracy. Remote Sens. Environ. 2016186, 47–63.

30. Voormansik, K.; Praks, J.; Antropov, O.; Jagomagi, J.; Zalite, K. Flood mapping with terraSAR-X in forested regions in estonia. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 20147, 562–577.

31. Pierdicca, N.; Pulvirenti, L.; Chini, M.; Guerriero, L.; Candela, L. Observing floods from space: Experience gained from COSMO-SkyMed observations. Acta Astronaut. 201384, 122–133.

32. Townsend, P.A. Relationships between forest structure and the detection of flood inundation in forested wetlands using C-band SAR. Int. J. Remote Sens. 200223, 443–460.

33. Brisco, B.; Schmitt, A.; Murnaghan, K.; Kaya, S.; Roth, A. SAR polarimetric change detection for flooded vegetation. Int. J. Digit. Earth 20116, 103–114.

34. European Space Agency. Envisat overview. Online avaliable: Accessed 12 December 2020.

35. Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; Seal, D.; Shaffer, S.; Shimada, J.; Umland, J.; Werner, M.; Oskin, M.; Burbank, D.; Alsdorf, D.E. The shuttle radar topography mission. Rev. Geophys 2007, 45(2), RG2004.

36. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27.

37. Hirabayashi, Y.; Mahendran, R.; Koirala, S.; Konoshima, L.; Yamazaki, D.; Watanabe, S.; Kanae, S. Global flood risk under climate change. Nat. Clim. Change 2018, 3(9), 816–821.

38. Jones, B.; Lamb, R.M. Hazards data distribution system (HDDS) (No. 2015–3048). US Geological Survey, 2015.

39. Kawasaki, A.; Berman, M.L.; Guan, W. The growing role of web-based geospatial technology in disaster response and support. Disasters 2013, 37(2), 201–221.

40. Klein, T.; Nilsson, M.; Persson, A.; Håkansson, B. From open data to open analyses–New opportunities for environmental applications? Environments 2017, 4(2), 32.

41. Kumar, A.; Pandey, A.C.; Khan, M.L. Urban risk and resilience to climate change and natural hazards: a perspective from Million-Plus Cities on the Indian Subcontinent. Tech. Disaster Risk Manage. Mitigation 2020, 33–46.

42. Lal, P.; Prakash, A.; Kumar, A. Google Earth Engine for concurrent flood monitoring in the lower basin of Indo-Gangetic-Brahmaputra plains. Nat. Hazards 2020, 104(2), 1947–1952.