In Detail: Flood Mapping and Damage Assessment using S2 Data

Rapid damage assessment following a flooding event and/or inland inundation is essential for disaster management to coordinate the first responders and other activities related to response and rehabilitation of damaged infrastructure in a quick manner. The use of Earth Observation (EO) data, specifically satellite data, significantly facilitates the determination of the flood extent for large areas and does not require field work, which would be highly time and labour-intensive.
Flood mapping also benefits from the large availability of satellite data free of charge, such as Sentinel data provided by the European Space Agency (ESA). On the Copernicus Open Access Hub, the user can download Optical as well as Radar satellite data. SAR (synthetic aperture radar) measurements can also be used for flood mapping irrespective of daytime and cloud cover of the scene (see UN-SPIDER's Recommended Practice on Radar-based Flood Mapping).
However, this recommended practice uses Optical satellite data and draws on the spectral reflectances within the Green and NIR channels to calculate the NDWI (Normalized Difference Water Index). The NDWI facilitates the differentiation between water and non-water inundated areas. In the method, a threshold is derived from the NDWI to binarize the image and determine the flood extent. The recommended practice further includes a damage assessment that can also be applied to other types of natural disasters.


Data requirements

  1. Sentinel 2A Imagery which can be downloaded from the Copernicus Open Access Hub

  2. Road Lines

  3. Local Wards or Districts

  4. Available Property boundaries such as from the City Administration or Open Street Maps (OSM)

  5. Extent of water bodies under average conditions

Software requirements

For image processing and further calculations:

  • QGIS 3.2.0 Bonn or previous versions
    • Freely available here

Skills requirements

Intermediate understanding of image processing and basic understanding of QGIS.

Hardware requirements

In terms of computing power, a potent machine is recommended for faster processing and depending on the size area of interest. The following recommendations are minimum (and tested) specifications:

  • 8GB of RAM (16GB of RAM)
  • 30GB to 1TB (500GB) of free disk space (heavily dependent on the size of the study area) the use of an external hard drive is possible!
  • Dual core processor (Intel i7-2600k, Intel i5)
  • An internet connection is required to run the script in order to download all necessary source data. The faster the connection, the better

The applications of flood extents include:

  • Operational estimation and detection of flooded areas in events with low cloud cover (within 6-12 h after data acquisition).
  • Damage assessment of flooded objects.
  • Calibration of hydrometeorological models.
  • Detection of water levels using high-resolution DEM.
  • Spatial extent: from villages to global scale.
  • Can be used for all stages: risk assessment, operational mapping and responserecovery.
  • Spatial resolution: from 1 m to 150 m.


The use of Sentinel-2 optical data has the following advantages:

  • High revisit time of every 10 days at the equator and every 5 days at mid-latitudes.
  • 10m-60m spatial resolution
  • Smaller datasets and respectively shorter download times


  • The presence of clouds hamper flood detection. Should the area of interest be covered with clouds throughout the whole period of the increased water level, Radar data (e.g. Sentinel-1) should be used for the assessment rather than Optical satellite data.
  • Potential false alarms from shadows
  • The NDWI is limited to rural areas, since the reflectance pattern of urban features is similar to that of water in the green band. Approaches using S2 imagery and calculating a modified NDWI (MNDWI) may be used when interested in urban flooding (Yang et al. 2017).

The difference between optical and SAR data, is that the former detects changes in the surface reflectance, while the SAR detects changes in vegetation structure and moisture.

(1) Sentinel- 2 Image Acquisition

(2) NDWI Calculation

(3) Clipping of extent to Study Area

(4) Binarization

(5) Polygonize to Vector

(6) Simplification of geometry

(7) Overlay with Road Lines, Districts and Property Boundaries

(8) Damage Assessment 

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Gao, Bo Cai. 1996. “NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” In Remote Sensing of Environment, edited by Michael R. Descour, Jonathan M. Mooney, David L. Perry, and Luanna R. Illing, 58:257–66. International Society for Optics and Photonics.

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Molinari, Daniela, Scira Menoni, and Francesco Ballio. 2017. Flood Damage Survey and Assessment : New Insights from Research and Practice. ISBN: 978-1-119-21792-3.

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Westerhoff, R. S., M. P.H. Kleuskens, H. C. Winsemius, H. J. Huizinga, G. R. Brakenridge, and C. Bishop. 2013. “Automated Global Water Mapping Based on Wide-Swath Orbital Synthetic-Aperture Radar.” Hydrology and Earth System Sciences 17 (2): 651–63.

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Yang, Xiucheng, and Li Chen. 2017. “Evaluation of Automated Urban Surface Water Extraction from Sentinel-2A Imagery Using Different Water Indices.” Journal of Applied Remote Sensing 11 (2): 026016.