Surveys in Geophysics

, Volume 32, Issue 3, pp 255–269 | Cite as

Timely Low Resolution SAR Imagery To Support Floodplain Modelling: a Case Study Review

  • Giuliano Di Baldassarre
  • Guy Schumann
  • Luigia Brandimarte
  • Paul Bates


It is widely recognised that remote sensing can support flood monitoring, modelling and management. In particular, satellites carrying Synthetic Aperture Radar (SAR) sensors are valuable as radar wavelengths can penetrate cloud cover and are insensitive to daylight. However, given the strong inverse relationship between spatial resolution and revisit time, monitoring floods from space in near real time is currently only possible through low resolution (about 100 m pixel size) SAR imagery. For instance, ENVISAT-ASAR (Advanced Synthetic Aperture Radar) in WSM (wide swath mode) revisit times are of the order of 3 days and the data can be obtained within 24 h at no (or low) cost. Hence, this type of space-borne data can be used for monitoring major floods on medium-to-large rivers. This paper aims to discuss the potential for, and uncertainties of, coarse resolution SAR imagery to monitor floods and support hydraulic modelling. The paper first describes the potential of globally and freely available space-borne data to support flood inundation modelling in near real time. Then, the uncertainty of SAR-derived flood extent maps is discussed and the need to move from deterministic binary maps (wet/dry) of flood extent to uncertain flood inundation maps is highlighted.


Hydrology Remote sensing Flood monitoring Inundation modelling Floodplain mapping 



The authors are extremely grateful to the European Space Agency (ESA) for allowing access to the flood images used in this study (Category 1 Project ID: 5739), the Environment Agency of England and Wales for the LiDAR data and the River Po Authority. Guy Schumann is funded by a Great Western Research fellowship.


  1. Abbott MB (1999) Introducing Hydroinformatics. J Hydroinformat 1:3–19Google Scholar
  2. Alsdorf DE, Smith LC, Melack JM (2001) Amazon floodplain water level changes measured with interferometric SIR-C radar. IEEE Trans on Geosci Remote Sensing 39(2):423–431CrossRefGoogle Scholar
  3. Alsdorf DE, Bates PD, Melack J, Wilson MD, Dunne T (2007) The spatial and temporal complexity of the Amazon flood measured from space. Geophys Res Lett 34:L08402CrossRefGoogle Scholar
  4. Apel H, Aronica GT, Kreibich H, Thieken AH (2009) Flood risk analyses—how detailed do we need to be? Nat Hazards 49(1):79–98CrossRefGoogle Scholar
  5. Aplin P, Atkinson PM, Tatnall AR, Cutler ME, Sargent I (1999) SAR imagery for flood monitoring and assessment. In Proceedings RSS99 Remote Sensing Society Earth Observation from Data to Information, Cardiff, 557–563Google Scholar
  6. Aronica G, Hankin BG, Beven KJ (1998) Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data. Adv Water Resour 22(4):349–365CrossRefGoogle Scholar
  7. Aronica G, Bates PD, Horritt MS (2002) Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE. Hydrol Process 16(10):2001–2016CrossRefGoogle Scholar
  8. Bates PD (2004) Remote sensing and flood inundation modelling. Hydrol Process 18:2593–2597CrossRefGoogle Scholar
  9. Bates PD, De Roo APJ (2000) A simple raster based model for flood inundation simulation. J Hydrol 236:54–77CrossRefGoogle Scholar
  10. Bates PD, Horritt MS, Aronica G, Beven KJ (2004) Bayesian updating of flood inundation likelihoods conditioned on flood extent data. Hydrol Process 18:3347–3370CrossRefGoogle Scholar
  11. Bates PD, Wilson MD, Horritt MS, Mason D, Holden N, Currie C (2006) Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery: data analysis and modelling. J Hydrol 328:306–318CrossRefGoogle Scholar
  12. Blyth K (1997) FLOODNET: a telenetwork for acquisition, processing, and dissemination of earth observation data for monitoring and emergency management of floods. Hydrol Process 11:1359–1375CrossRefGoogle Scholar
  13. Bonnet MP, Barroux G, Martinez JM, Seyler F, Moreira-Turcq P, Cochonneau G, Melack J-M, Boaventura G, Maurice-Bourgoin L, León JG, Roux E, Calmant S, Kosuth P, Guyot JL, Seyler P (2008) Floodplain hydrology in an Amazon floodplain lake (Lago Grande de Curuaí). J Hydrol 349(1–2):18–30CrossRefGoogle Scholar
  14. Brakenridge GR, Tracy BT, Knox JC (1998) Orbital SAR remote sensing of a river flood wave. Int J Remote Sens 19(7):1439–1445CrossRefGoogle Scholar
  15. Brandimarte L, Brath A, Castellarin A, Di Baldassarre G (2009) Isla Hispaniola: a trans-boundary flood risk mitigation plan. Phys Chem Earth, Special Issue on Integrated water resources assessment, with special focus on developing countries 34:209–218Google Scholar
  16. Castellarin A, Di Baldassarre G, Bates PD, Brath A (2009) Optimal cross-section spacing in Preissmann scheme 1D hydrodynamic models. ASCE J Hydraul Engineer 135(2):96–105CrossRefGoogle Scholar
  17. Cobby DM, Mason DC, Davenport IJ (2001) Image processing of airborne scanning laser altimetry data for improved river flood modeling. ISPRS J Photogramm Remote Sensing 56(2):121–138CrossRefGoogle Scholar
  18. Deshmukh KS, Shinde GN (2005) An adaptive color image segmentation. Elect Lett Comp Vision Image Anal 5(4):12–23Google Scholar
  19. Di Baldassarre G, Schumann G, Bates PD (2009a) Near real time satellite imagery to support and verify timely flood modelling. Hydrol Process 23:799–803CrossRefGoogle Scholar
  20. Di Baldassarre G, Schumann G, Bates PD (2009b) A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent. J Hydrol 367:276–282CrossRefGoogle Scholar
  21. Di Baldassarre G, Montanari A, Lins H, Koutsoyiannis D, Brandimarte L, Blöschl G (2010) Flood fatalities in Africa: from diagnosis to mitigation. Geophys Res Lett 37:L22402. doi: 10.1029/2010GL045467 CrossRefGoogle Scholar
  22. Gubbels T, Brakenridge R (2004) Flood disaster hits Hispaniola. Nasa Earth Observatory.
  23. Gupta RP, Banerji S (1985) Monitoring of reservoir volume using LANDSAT data. J Hydrol 77:159–170CrossRefGoogle Scholar
  24. Henry JB, Chastanet P, Fellah K, Desnos JL Envisat multi-polarized ASAR data for flood mapping, International Journal of Remote Sensing 27(10):1921–1929Google Scholar
  25. Horritt MS (2006) A methodology for the validation of uncertain flood inundation models. J Hydrol 326:153–165CrossRefGoogle Scholar
  26. Horritt MS, Bates PD (2001) Effects of spatial resolution on a raster based model of flood flow. J Hydrol 253:239–249CrossRefGoogle Scholar
  27. Horritt MS, Bates PD (2002) Evaluation of 1-D and 2-D models for predicting river flood inundation. J Hydrol 268:87–99CrossRefGoogle Scholar
  28. Horritt MS, Mason D, Luckman AJ (2001) Flood boundary delineation from synthetic aperture radar imagery using a statistical active contour model. Int J Remote Sens 22:2489–2507Google Scholar
  29. Horritt MS, Mason DC, Cobby DM, Davenport IJ, Bates PD (2003) Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sensing of Environ 85(3):271–281CrossRefGoogle Scholar
  30. Horritt MS, Di Baldassarre G, Bates PD, Brath A (2007) Comparing the performance of 2-D finite element and finite volume models of floodplain inundation using airborne SAR imagery. Hydrol Process 21:2745–2759CrossRefGoogle Scholar
  31. Hostache R, Matgen P, Schumann G, Puech C, Hoffmann L, Pfister L (2009) Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods. IEEE Trans Geosci Remote Sensing 47:431–441CrossRefGoogle Scholar
  32. Hunter NM, Bates PD, Horritt MS et al (2007) Simple spatially-distributed models for predicting flood inundation: a review. Geomorphology 90:208–225CrossRefGoogle Scholar
  33. Irons JR, Petersen GW (1981) Texture transforms of remote sensing data. Remote Sensing Environ 11:359–370CrossRefGoogle Scholar
  34. Jonkman SN, Vrijling JK (2008) Loss of life due to floods. J Flood Risk Manag 1(1):43–56CrossRefGoogle Scholar
  35. Kokare M, Chatterji BN, Biswas PK (2003) Comparison of similarity metrics for texture image retrieval. In: Proceedings of the IEEE 10th Conference on Convergent Technologies for Asia-Pacific Region, October 2003, vol. 2. IEEE, Bangalore, India, pp 571–575Google Scholar
  36. Kussul N, Shelestov A, Skakun S (2008) Grid system for flood extent extraction from satellite images. Earth Sci Inf 1(3):105–117CrossRefGoogle Scholar
  37. Lane SN, James TD, Pritchard H, Saunders M (2003) Photogrammetric and laser altimetric reconstruction of water levels for extreme flood event analysis. Photogramm Record 18(104):293–307CrossRefGoogle Scholar
  38. Mason DC, Davenport IJ, Flather RA, Gurney C, Robinson GJ, Smith JA (2001) A sensitivity analysis of the waterline method of constructing a digital elevation model for intertidal areas in ERS SAR scene of Eastern England. Estuar Coast Shelf Sci 53:759–778CrossRefGoogle Scholar
  39. Mason DC, Cobby DM, Horritt MS, Bates PD (2003) Floodplain friction parameterization in two-dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry. Hydrol Process 17:1711–1732CrossRefGoogle Scholar
  40. Mason DC, Bates PD, Dall’Amico JT (2009) Calibration of uncertain flood inundation models using remotely sensed water levels. J Hydrol 368:224–236CrossRefGoogle Scholar
  41. Mason DC, Speck R, Devereux B, Schumann G, Neal J, Bates PD (2010) Flood detection in urban areas using TerraSAR-X, IEEE. Trans. Geosci Rem. Sens 48(2):882–894CrossRefGoogle Scholar
  42. Matgen P, Schumann G, Pappenberger F, Pfister L (2007a) Sequential assimilation of remotely sensed water stages in flood inundation models. Remote Sensing for Environmental Monitoring and Change Detection, Proceedings of Symposium HS3007 at IUGG2007, Perugia, July 2007. IAHS Publ 316:78–88Google Scholar
  43. Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007b) Integration of SAR-derived inundation areas, high precision topographic data and a river flow model toward real-time flood management. Int J Appl Earth Observ Geoinformat 9(3):247–263CrossRefGoogle Scholar
  44. Montanari A, Brath A (2004) A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resour Res 40:W01106CrossRefGoogle Scholar
  45. Montanari A, Grossi G, (2008) Estimating the uncertainty of hydrological forecasts: A statistical approach, Water Resources Research 44:W00B08Google Scholar
  46. Moussa R, Bocquillon C (1996) Criteria for the choice of flood-routing methods in natural channels. J Hydrol 186:1–30CrossRefGoogle Scholar
  47. Neal J, Schumann G, Bates PD, Buytaert W, Matgen P, Pappenberger F (2009) A data assimilation approach to discharge from space. Hydrol Processes 23:3641–3649CrossRefGoogle Scholar
  48. Oberstadler R, Hnsch H, Huth D (1997) Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany. Hydrol Process 10:1415–1425CrossRefGoogle Scholar
  49. Ohl C, Tapsell S (2000) Flooding and human health: the dangers posed are not always obvious. B Med J 321(7270):1167–1168CrossRefGoogle Scholar
  50. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE trans syst man Cybern 9:62–66CrossRefGoogle Scholar
  51. Pappenberger F, Matgen P, Beven KJ, Henry JB, Pfister L, de Fraipont P (2006) Influence of uncertain boundary conditions and model structure on flood inundation predictions. Adv Water Resour 29:1430–1449CrossRefGoogle Scholar
  52. Pappenberger F, Frodsham K, Beven KJ, Romanovicz R, Matgen P (2007) Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations. Hydrol Earth Syst Sci 11(2):739–752CrossRefGoogle Scholar
  53. Prestininzi P, Di Baldassarre G, Schumann G, Bates PD (2010) Selecting the appropriate hydraulic model structure using low-resolution satellite imagery. Advances in Water Resources, doi: 10.1016/j.advwatres.2010.09.016
  54. Raclot D (2006) Remote sensing of water levels on floodplains: a spatial approach guided by hydraulic functioning. Int J Remote Sens 27(12):2553–2574CrossRefGoogle Scholar
  55. Romanowicz R, Beven K (2003) Estimation of flood inundation probabilities as conditioned on event inundation maps. Water Resour Res 39(3):1073–1085CrossRefGoogle Scholar
  56. Sali E, Wolfson H (1992) Texture classification in aerial photographs and satellite data. Int J Remote Sens 13:3395–3408CrossRefGoogle Scholar
  57. Schumann G, Di Baldassarre G (2010) The direct use of radar satellites for event-specific flood risk mapping. Int J Remote Sens 1(2):75–84Google Scholar
  58. Schumann G, Hostache R, Puech C, Hoffmann L, Matgen P, Pappenberger F, Pfister L (2007) High-resolution 3D flood information from radar imagery for flood hazard management. IEEE Trans Geosci Remote Sensing 45(6):1715–1725CrossRefGoogle Scholar
  59. Schumann G, Matgen P, Cutler MEJ, Black A, Hoffmann L, Pfister L (2008) Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. ISPRS J Photogramm Remote Sensing 63:283–296CrossRefGoogle Scholar
  60. Schumann G, Di Baldassarre G, Bates PD (2009a) The utility of space-borne radar to render flood inundation maps based on multi-algorithm ensembles. IEEE Trans Geosci Remote Sensing 47(2):2801–2807CrossRefGoogle Scholar
  61. Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009b) Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models. Reviews of Geophysics 47: RG4001Google Scholar
  62. Schumann G, Di Baldassarre G, Alsdorf DE, Bates PD (2010) Near real-time flood wave approximation on large rivers from space: application to the River Po, Northern Italy. Water Resour Res 46:W05601CrossRefGoogle Scholar
  63. Smith LC (1997) Satellite remote sensing of river inundation area, stage and discharge: a review. Hydrol Process 11(10):1427–1439CrossRefGoogle Scholar
  64. Srinivasa RG, Brinda V, Manju P, Bhanumurthy V (2006) Advantage of multipolarized SAR data for flood extent delineation. Proceedings of the SPIE 6410Google Scholar
  65. Todini E (1999) An operational decision support system for flood risk mapping, forecasting and management. Urban Water 1:131–143CrossRefGoogle Scholar
  66. Uhlenbrook S (2009) Climate and man-made changes and their impacts on catchments. In: Kovar P., Maca P., Redinova J. (eds.): Water Policy 2009, Water as a Vulnerable and Exhaustible Resource. Proceedings of the Joint Conference of APLU and ICA, 23-26 June 2009, Prague, Czech Republic, page 81-87Google Scholar
  67. US Agency for International Development (2004) Dominican Republic and Haiti – Floods. USAID/OFDA Fact Sheet #3, June 17Google Scholar
  68. Verhoest NEC, De Baets B, Mattia F, Satalino G, Lucau C, Defourny P (2007) A possibilistic approach to soil moisture retrieval from ERS synthetic aperture radar backscattering under soil roughness uncertainty, Water Resources Research, 43(7): W07435Google Scholar
  69. Vorogushyn S, Merz B, Lindenschmidt KE, Apel H (2010) A new methodology for flood hazard assessment considering dike breaches, Water Resources Research  10.1029/2009WR008475
  70. Wilson MD, Bates PD, Alsdorf DE, Forsberg B, Horritt MS, Melack J, Frappart F, Famiglietti J (2007) Modeling large-scale inundation of Amazonian seasonally flooded wetlands. Geophys Res Lett 34:L15404CrossRefGoogle Scholar
  71. Wright NG, Asce M, Villanueva I et al (2008) Case study of the use of remotely sensed data for modeling flood inundation on the River Severn, UK. J Hydraul Engineer 134(5):533–540CrossRefGoogle Scholar
  72. Zwenzner H, Voigt S (2009) Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrol Earth Syst Sci 13:567–576CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Giuliano Di Baldassarre
    • 1
  • Guy Schumann
    • 2
  • Luigia Brandimarte
    • 3
  • Paul Bates
    • 2
  1. 1.Department of Hydroinformatics and Knowledge ManagementUNESCO-IHEDelftThe Netherlands
  2. 2.School of Geographical SciencesUniversity of BristolBristolUK
  3. 3.Department of Water EngineeringUNESCO-IHEDelftThe Netherlands

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