Flood extent from radar
Synthetic-aperture radar sees through cloud. Change detection between a dry reference and the event image maps open water at ten metres, within hours of acquisition.
GCRI · Water Nexus
Earth observation for water
Satellites see the whole basin at once and return every few days: surface water, soil moisture, snow, vegetation stress and, after an event, the extent of the flood. Earth observation fills the gaps that thinning gauge networks leave.
01Contents
Earth observation for water in five parts.
02Application
What earth observation and remote sensing does here.
Earth observation and remote sensing gather information about the Earth’s surface and atmosphere from airborne and spaceborne sensors. They provide consistent, repeatable measurements over areas that are hard to monitor on the ground. In water, climate and hazards and critical infrastructure, earth observation maps exposure, damage and environmental change across borders and sectors. It supports rapid assessment after disasters and long-term monitoring of risk drivers. The canonical description is on the STEM Commons.
Surface water and reservoir monitoring
Optical time series track reservoir area and seasonal wetlands over decades, giving a storage proxy where operators do not publish levels.
Drought stress from vegetation and soil moisture
Vegetation indices, land-surface temperature and radiometer soil moisture are combined into drought indicators that agree, or disagree, with rainfall indices.
03Geography
Water now in the Nile Basin.
Flood water, root-zone soil moisture and rain over the Nile Basin, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Water now, for this page
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled
- Source
- SMAP Level 4 (NASA NSIDC DAAC)
- Provider
- NASA NSIDC DAAC
- Licence
- NASA open data
- Vintage
- daily; Model with satellite data assimilated
- Newest at build
- 2026-10-11
- Resolution
- about 2 km grid
- Uncertainty
- Model with satellite data assimilated. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled
- Source
- SMAP Level 4 (NASA NSIDC DAAC)
- Provider
- NASA NSIDC DAAC
- Licence
- NASA open data
- Vintage
- daily; Model with satellite data assimilated
- Newest at build
- 2026-10-11
- Resolution
- about 2 km grid
- Uncertainty
- Model with satellite data assimilated. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Water now, framed on the Nile Basin.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DSWx-S1 · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DSWx-S1 (contains modified Copernicus Sentinel-1 data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Surface soil moisture, 0 to 5 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
04Data
What the method consumes.
Datasets and records used by earth observation and remote sensing on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Sentinel-1 radar | Flood extent, soil moisture | OBSERVED |
| Sentinel-2 and Landsat optical | Surface water, vegetation stress | OBSERVED |
| SMAP and SMOS radiometers | Soil moisture | MODELLED |
| GRACE-FO gravimetry | Terrestrial water storage anomaly | MODELLED |
| MODIS and VIIRS | Snow cover, land-surface temperature | OBSERVED |
05Monitor
Earth observation run.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Earth observation run
Water · Earth observation for water · Run R-8937
Scene processingGCRI · Water Nexus
Acquisition
revisit and swath
Accuracy assessment
stratified sample
| Water | 95 | 91 | 159 |
| Cropland | 79 | 79 | 174 |
| Built-up | 81 | 78 | 122 |
| Forest | 94 | 89 | 190 |
Processing chain
- AcquireL1
- Calibrateradiometric
- Correctatmospheric
- Maskcloud · shadow
- ClassifyEarth observation
- Validateground truth
06Validation
How results are checked.
Products are assessed against ground reference data using accuracy matrices and independent samples. Cross-calibration between sensors checks consistency over time.
| Check | How it is done |
|---|---|
| Ground reference | Flood masks are scored against surveyed high-water marks and gauge-derived extents |
| Cross-sensor consistency | Products from different satellites are compared over the same period before a trend is quoted |
| Cloud and revisit gaps | Every product states its coverage; missing scenes are reported as no data, not as no change |
Clouds, revisit gaps and mixed pixels limit what can be observed. Changes in sensors and processing can create artificial trends.
07Twin and cascade simulator
Model earth observation for water as a system. Run the cascade.
Assembled from the Nexus Standards area profile Water risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Flash flood
- Meteorological drought
- Groundwater depletion
- Dam failure
- Debris flow (set off downstream)
- Agricultural drought (set off downstream)
- Hydrological drought (set off downstream)
- Wildfire (set off downstream)
- Peat and underground fire (set off downstream)
- Forest dieback (set off downstream)
- Water supply shortfall (set off downstream)
- Acute food crisis (set off downstream)
- Dust storm (set off downstream)
- Land subsidence (set off downstream)
- Saltwater intrusion (set off downstream)
- River flood (set off downstream)
- Mass displacement (set off downstream)
- Amazon rainforest dieback (set off downstream)
Systems and couplings
- Water risk standards
- Energy risk standards Common driver and cascade
- Food and agriculture risk standards Cascade
- Health risk standards Cascade
- Hydrometeorological hazard standards Shared hazard records
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-3bDataset and product metadata profile (STAC, DCAT, PROV-O)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-U28Universe platform profile: Water NexusNRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-K42Domain pack profile: Cross border basins (DOMAIN-042)NRS-N1Water risk and resilienceNRS-N7Water-energy-food-health-biodiversity-infrastructure nexus couplingNRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)
Observation
- USGS Water Data APIs (listed for reference)
- GloFAS global flood forecasts and reanalysis (listed for reference)
- GRACE/GRACE-FO terrestrial water storage mascons (listed for reference)
- Aqueduct 4.0 Water Risk Atlas (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
- Proportion of transboundary basin area with an operational arrangement for water cooperation
- Proportion of population using safely managed drinking water services
- Number of disruptions to basic services attributed to disasters
Example geography
- The Ganges and Brahmaputra Basin: 178 hexagonal cells
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
08Connections
Topics that rely on this method.
09Sector signature
The sector signature of earth observation for water.
The sectors earth observation for water touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Water and its partners
Water gives irrigation to food (0.8), cooling water and hydropower to energy (0.6) and environmental flows to biodiversity (0.6).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.43 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
10Stewardship
Who maintains the method.
Space and Earth Observation stewards earth observation and remote sensing for the Consortium and reviews how it is applied across platforms.
11Methods and evidence
How earth observation for water is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in earth observation for water: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
12Connected
Where this page leads across the Consortium.
13Process
How the process runs.
Process
Earth observation for water: Earth observation processing chain
Processing levels are mission specific; Sentinel-2 runs from Level-0 raw data to Level-2A, and CEOS Analysis Ready Data sets the minimum for analysis.
01 Ground segment
- 01AcquireSourceSensors acquire optical or radar data. For Sentinel-2 the Level-0 product is raw image data in Instrument Source Packet format.
- 03Level-1 radiometric and geometric processingStepLevel-1B gives calibrated top-of-atmosphere radiance. Level-1C adds geometric corrections, including orthorectification, for reflectance.
02 Pre-processing
- 04Level-2 atmospheric correctionStepLevel-2A applies an atmospheric correction to the Level-1C orthoimage. Its main output is a surface reflectance product.
- 05Scene classification and masksStepLevel-2A includes scene classification, and Level-1C also makes cloud, cirrus and snow or ice masks. Quality flags travel with the product.
- 06Analysis-ready dataDatasetCEOS Analysis Ready Data meet minimum processing requirements and are organised for immediate analysis and interoperability.
03 Analytics
- 02Reference dataDatasetField samples or other independent reference data are used to check the product.
- 07Change analysisModelClassification and time-series methods detect change in the analysis-ready data. The method is chosen by the analyst.
- 08ValidateGateThe product is compared with reference data and accuracy is reported, for example with a confusion matrix.
04 Delivery
- 09Product to the userDecision · Water utilityThe producer releases the product with its limits stated.
Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names. Level-1B and Level-1C add corrections; Level-2A adds atmospheric correction and scene classification.
14Questions
Questions and answers.
What are the limits of earth observation and remote sensing in this field?
Clouds, revisit gaps and mixed pixels limit what can be observed. Changes in sensors and processing can create artificial trends.
How are results checked?
Products are assessed against ground reference data using accuracy matrices and independent samples. Cross-calibration between sensors checks consistency over time.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Contribute evidence from this page.
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GCRIWater NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceObservatory Signal
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