Other Space Activities

Floods

Last updated:Mar 18, 2025

Applications

Sentinel-1 Imagery on 30 August, 2022 captured the extent of floods in Pakistan. (Image credit: ESA)

Flood Monitoring

Flooding is often a natural process by which water overflows normal limits and inundates neighbouring floodplains. This process can sustain ecosystem services and biodiversity by transporting nutrients and sediment, supporting agriculture, recharging groundwater, creating habitats, and triggering species spawning and migration. 1) 2)

However, extreme flooding can cause widespread fatalities and property loss, making them one of the most costly natural disasters. The occurrence of such damaging flood events is increasing due to more frequent extreme rainfall events linked to climate change, affecting populations globally. 1) 3) 4) 5)

Flooding events vary greatly, affecting vast areas for weeks or striking suddenly and lasting only hours, making them difficult to predict, respond to, and recover from. Hence, studying the temporal and spatial extent and severity of floods through in-situ and remote observations is crucial for informing emergency response efforts and improving flood prediction and prevention. 1) 5) 6)

Figure 1: Imagery acquired by Planet’s SuperDove satellites shows the Teesta III Dam in Sikkim, India collapsing. The dam’s collapse and devastating flood that followed came after the Lhonak Lake received five times its average rainfall (Image credit: Planet Labs PBC)

Flood Monitoring Techniques

Extreme flooding events from tropical storms and hurricanes can occur over spatial and temporal scales that exceed the coverage of in-situ techniques such as water level gauges, float gauges, current meters, Acoustic Doppler Current Profilers (ADCPs), pressure transducers, and sediment sampling. These sensors often collect parameters such as the depth, pressure, and flow velocity of water as well as sediment concentration at specific locations, only allowing flood predictions at the reach scale. In-situ techniques can also be rendered unavailable by high flows and the measurement infrastructure being destroyed by the flooding. Furthermore, the global river gauge network is in decline due to high maintenance and operational costs. Despite these limitations, in-situ data remains valuable for the calibration and validation of flood prediction models, particularly when supplemented with satellite data. 1) 5) 6)

Optical and Synthetic Aperture Radar (SAR) satellite imagery are often used to track the onset, duration and retreat of floodwaters, providing information on flood extent, water level, depth, and volume for flood response and mitigation. SAR instruments, unlike optical instruments, can image regardless of cloud cover or illumination, making them valuable for flood mapping in areas with persistent smoke coverage due to infrastructure damage. 6) 7) 3) 1)

Table 1: Comparison of satellite flooding measurement types
Satellite Sensor Type	Spectral Range	Coverage
Optical	400 nm - 2500 nm (visible, NIR, SWIR)	Global, impacted by cloud cover
SAR	Microwave (L-band, C-band, X-band)	Global, unimpacted by cloud cover

While the potential of optical satellite imagery for flood mapping has been demonstrated since the early 1970s, the recent advancements in numerical modeling, data processing, the Internet of Things (IoT), and open-access EO data have expanded the utility of remote sensing in flood modelling and prediction. Remote sensing datasets have advanced sufficiently to effectively assist flood response teams, risk management efforts, and preventative planning (see Figure 2). Data concerning precipitation patterns, soil moisture, surface runoff, snowfall and seasonal water runoff, and topography allow risk assessments of an area's susceptibility to flooding, which can aid preventative planning. 1) 3)

Figure 2: Flood extent estimation from remote sensing data and geo-referenced data can be combined to derive individual risk level damage estimates and vulnerability curves for flood risk modeling, allowing estimations of insurance portfolio loss (Image credit: Swiss Re Institute)

An example of large-scale flood modelling and assessment is ESA's FAME (Flood risk and damage Assessment using Modelling and Earth observation techniques) project, which aimed to improve flood modelling performances by incorporating EO-derived products to calibrate flood models. Data from the Thematic Mapper (TM) onboard Landsat-4 and -5, the Advanced SAR (ASAR) onboard EnviSat (Environmental Satellite), and SAR imagery from ERS-1 and -2 (European Remote Sensing Satellite) were used in this project to map flood extent, create accurate flood risk maps, and carry out post-flood damage assessment. 9) 10)

Example Products

Normalised Difference Water Index (NDWI) and Normalised Difference Vegetation Index (NDVI)

Normalised Difference Water Index (NDWI) is a remote sensing measurement that highlights bodies of water in satellite images, making inundated areas easier to distinguish. Vegetation loss and damage during flooding events can be captured using satellite imagery, and Normalised Difference Vegetation Index (NDVI) can be calculated to reference flood extent. NDVI is typically derived from red and NIR spectra, whereas NDWI is derived from the green and NIR spectra. 4) 11) 12)

Near real-time data provided by NASA's Land, Atmosphere Near real-time Capability for Earth observation (LANCE) includes flood and surface water measurements, above-ground biomass density, land cover, leaf area indices, surface reflectance, and vegetation greenness, which can be used to develop NDWI and NDVI. 3)

Figure 3: Optical image by Sentinel-2 (left) and SAR image from Sentinel-1 (right) of flooding in Pakistan in August, 2022. The middle image shows NDWI which highlights inundated areas in blue (Image credit: ESA)

Figure 4: Images of flooding around the Red River, USA from Landsat-8 on April 17, 2020. NDWI in the lower image classifies water in blue and farmland in green. (Image credit: NASA)

Categorical Scale Map (CSM)

Flood maps derived from EO datasets can be used to evaluate and improve flood model performance as they provide a quantitative, location-specific measure of model accuracy that can be used to target specific improvements. Skill scores can be calculated for model validation, which vary depending on the size of the flood, the spatial scale of the model (as shown in Figure 5), and the accuracy of the forecast. This accuracy can be plotted on a Categorical Scale Map (CSM), which colour-codes each location according to the degree of under-prediction (missed areas) or over-prediction (false alarms) (as shown in Figure 6). 6)

These flood maps derived from EO data give vital information to emergency response teams as well as support urban planning and infrastructure development to manage stormwater runoff. They can be used by flood forecasting agencies to assess and improve the performance of hydrometeorological models used to predict the evolution of flood extent. 6) 4)

Figure 5: Impact of spatial scale on skill scores for the flood edge location (Image credit: Hooker, 2022)

Figure 6: Categorical Scale Maps (CSM) for predictions of flood extent compared to EO observed flood extents by Sentinel-1 in Bangladesh in July 2020. Correctly predicted flooding is shown in grey, under-prediction are red and over-prediction areas are blue (Image credit: Hooker, 2022)

Related Missions

Copernicus: Sentinel-1, -2, -3, and -6

Copernicus, the European Commission’s EO Programme, consists of the Sentinel family of satellite missions which is jointly coordinated through the European Space Agency (ESA) and COM. Sentinel-1, -2, and -3 each consist of two satellites launching from 2014, 2015, and 2016, respectively, to collect data that can be used to detect and map flooding.

Sentinel-1 and -2 provide high-resolution imagery for the Copernicus Emergency Management Services (EMS) Flood Awareness System, available Globally (GloFAS) or in Europe (EFAS). These systems support preparatory measures as well as aid emergency response by developing flood maps and providing damage assessments.

Sentinel-6 Michael Freilich, launched in November 2020, uses SAR imaging to measure sea level rise, ocean circulation, and atmospheric temperature and humidity, which aids understanding of how these factors impact coastlines and flooding.

SMOS (Soil Moisture and Ocean Salinity) Mission

Launched November 2009, SMOS (Soil Moisture and Ocean Salinity) is a microwave imaging satellite operated by ESA as part of the Earth Explorer programme. The onboard Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) provides near real-time global observations on soil moisture and ocean salinity, which are essential for applications in weather prediction and flood forecasting.

Moderate Resolution Imaging Spectroradiometer (MODIS)

MODIS (Moderate Resolution Imaging Spectroradiometer), developed by NASA’s Goddard Space Flight Centre, is an instrument onboard NASA's Aqua (EOS/PM-1) and Terra (EOS/AM-1) satellites. Aqua and Terra, launched May 2002 and December 1999 respectively, are joint missions within NASA's ESE (Earth Science Enterprise) program. MODIS collects extensive data on Earth’s atmosphere, land surface, ocean, and cryosphere. Wide-area imagery is used in flood tracking, especially large-scale flooding events caused by hurricanes or tropical storms. A water detection algorithm is applied to each MODIS observation which are composited to reduce errors and allow flood water to be differentiated from expected surface water. The MODIS Near Real-Time Global Flood Product is a daily, near-global, ~250 m resolution product showing flood and surface water detected from the twice-daily overpass of the MODIS optical sensors, which is processed by NASA's LANCE. MODIS has now been replaced by VIIRS.

Figure 7: A three day composite image of flooding in Central California on January 23, 2023 captured by the MODIS instruments aboard NASA's Terra and Aqua platforms. (Image credit: NASA)

SWOT (Surface Water Ocean Topography)

SWOT (Surface Water Ocean Topography) is a joint collaboration between NASA, CNES, CSA, and UKSA, launched in December 2022. This swath-based SAR altimetry mission collects high-resolution data on Earth’s surface water, enabling precise monitoring of flood extent and dynamics. SWOT provides detailed information on water levels in inland bodies, which can be used to calibrate and refine flood prediction models.

GPM (Global Precipitation Measurement) Mission

The GPM (Global Precipitation Measurement) mission is a multi-satellite constellation operated by NASA and JAXA. The constellation’s primary spacecraft, the GPM Core Observatory (built by NASA), was launched in February 2014 to study global precipitation, evaporation, and the water cycle. This global sampling of Earth's precipitation, using the onboard active microwave DPR (Dual-frequency Precipitation Radar) and the passive microwave GMI (GPM Microwave Imager), improves flood hazard prediction.

ICEYE Microsatellites Constellation

The commercial Earth Observation (EO) company ICEYE Ltd., based in Espoo, Finland, developed and built the X-band SAR satellite constellation, launching from December 2018. The constellation provides high-resolution SAR imagery for commercial flood analysis.

Other Missions

RISAT-1 and RISAT-2 (Radar Imaging Satellite)

EOS-01 (Earth Observation Satellite - 01)

GF-1 (Gaofen-1)

RADARSAT-1

NovaSAR-1

GeoXO (Geostationary Extended Observations)

References

1) Schumann G, Giustarini L, Tarpanelli A, Jarihani B, Martinis S. (2023) “Flood Modeling and Prediction Using Earth Observation Data,” URL: https://link.springer.com/article/10.1007/s10712-023-09725-7

2) “Environmental Aspects of Integrated Flood Management,” Associated Programme on Flood Management, Geneva, Switzerland, 2006, URL: https://www.floodmanagement.info

3) “Floods,” Earth Science Data Systems, NASA, 2024, URL: https://www.earthdata.nasa.gov/topics/human-dimensions/floods

4) Watson CS, Creed M, Gyawali J, Shadeed S, Dabbeek J, Subedi D (2024) “Earth Observation Informed Modelling of Flash Floods,” URL: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-21

5) “Access NASA GES DISC Meteorological and Hydrological Data for Flood Analyses,” Earth Science Data Systems, NASA, 2022, URL: https://www.earthdata.nasa.gov/learn/webinars/access-nasa-ges-disc-meteorological-hydrological-data-flood-analyses

6) “Improving Forecast Flood Maps Using Earth Observation Data,” JBA Trust, URL: https://www.jbatrust.org/about-the-jba-trust/how-we-help/publications-resources/rivers-and-coasts/improving-forecast-flood-maps-using-earth-observation-data/

7) “Improving Forecast Flood Maps Using Earth Observation Data,” JBA Trust, URL: https://www.jbatrust.org/about-the-jba-trust/how-we-help/publications-resources/rivers-and-coasts/improving-forecast-flood-maps-using-earth-observation-data/

8) Wheldon A. (2024) “Fire, Flood, Winds & Earthquakes: Satellite Imagery Reveals Climate Vulnerability,” Space Park Leicester, URL: https://www.space-park.co.uk/2024/01/fire-flood-winds-and-earthquakes-satellite-imagery-reveals-damage-wrought-by-changing-climate/

9) “Satellites Assist Planners Preventing Floods,” European Space Agency (ESA), URL: https://www.esa.int/Applications/Observing_the_Earth/Satellites_assist_planners_preventing_floods

10) Campling P. “FAME Service Development Plan,” URL: https://www.fame.org

11) “Normalized Difference Vegetation Index (NDVI) Products by Using OCM2-GAC Sensor Data,” SDAPSA, URL: https://bhuvan-app3.nrsc.gov.in/data/download/tools/document/bhuvan_gac_ndvi.pdf

12) Gao, B (1996), “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space,” Remote Sensing of Environment, URL: https://www.sciencedirect.com/science/article/pii/S0034425796000673