GCRI · Space and Earth Observation
EO in water and agriculture
Satellites observe rainfall, soil moisture, crop condition, water bodies, irrigation and evapotranspiration over whole countries. Ministries, early warning systems and insurers use this evidence to monitor harvests and water. This page sets out how Earth observation serves water and agriculture, how its estimates are validated, and what evidence serves the agencies and farmers who use it.
EO in water and agriculture on the record.
EO in water and agriculture: how the risk develops.
Water, crop and ecosystem applications. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Vegetation signals crop condition
Vegetation indices and their changes through the season indicate crop vigour and stress, which are compared with past seasons to flag problems.
Evapotranspiration measures water use
Thermal and optical data estimate evapotranspiration, showing how much water crops use and where irrigation occurs.
Field mapping needs resolution
Small fields and mixed cropping need high-resolution data and local training samples; coarse data blend fields and misclassify crops.
Ground data anchor estimates
Yield and area estimates from space need field surveys and statistics to calibrate and validate them.
Water now in the Arctic.
Root-zone soil moisture, crop greenness and rain over the Arctic, 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
- 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
- Observed (clouds leave gaps)
- Source
- MODIS Terra NDVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite greenness index over the 8 days to the date shown; an indicator of crop and pasture condition, not a yield estimate or a food security warning.
- 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
- 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 (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
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 Arctic.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 SMAP · 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
- 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
- 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
- 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
- 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
- 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 SMAP. 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
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 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
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
Earth observation links water and food monitoring.
Monitoring evidence is designed for the Country Quarterly series. The GEOGLAM Crop Monitor shows how satellite data and field reports combine in a consensus assessment.
- Field reports from extension services.
- Validation of drought indices.
EO tasking and products.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
EO tasking and products
EO in water and agriculture · Flood basin AOI · Activation ACT-079
Passes over the area
next 8 passes
Acquisitions
| P-1 | Radar | n/a | Processed |
| P-2 | Optical | 79 | Cloudy |
| P-3 | Radar | n/a | Downlinking |
| P-4 | Optical | 10 | Scheduled |
Product chain
- TaskAOI
- Acquireradar
- Downlinkstation 2
- ProcessARD
- Map floodchange
- Validatesamples
- Deliveruser agency
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Crop condition anomaly | difference of vegetation index from mean | dekadal | Satellite vegetation indices | OBSERVED |
| Cropland area by crop type | hectares | seasonal | Classified satellite maps | ESTIMATED |
| Actual evapotranspiration | millimetres per period | dekadal | Satellite-based energy balance models | MODELLED |
| Surface water extent | square kilometres | monthly | Water body mapping | OBSERVED |
| Irrigated area | hectares | seasonal | Satellite mapping | ESTIMATED |
| Agreement with agricultural statistics | percent difference | per season | Comparison with published statistics | ESTIMATED |
A shock through an input-output economy.
A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Arctic; the model itself is not tied to a place.
Controls
- Sector hit first (position in the list)
- 0
- Size of the shock
- 30% of its output
- Duration of the shock
- 30 days
- Recovery rate
- 0.08 per day
- Days simulated
- 180 days
Readouts
- Output lost over the run
- 0.91% of a year’s output
- Total over direct (multiplier)
- 1.54 times
- Most affected other sector
- Wholesale and retail
- Its peak inoperability
- 1.9%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Farming | 29.39 | 28.2 |
| Fertiliser and inputs | 1.82 | 14.3 |
| Food processing | 1.72 | 11.0 |
| Transport | 1.42 | 13.3 |
| Wholesale and retail | 1.94 | 13.5 |
| Energy | 1.07 | 19.7 |
Methods
- Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
- Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
- Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Seaports Natural Earth
- Maritime chokepoints Nexus (named straits and canals at their published coordinates)
- Railways Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Space and Earth Observation in Foundry
- Tested at Space and Earth Observation in Universe
- Codified in Space and Earth Observation in Standards
- Published in Space and Earth Observation in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| GEOGLAM Crop Monitor and Early Warning Crop Monitor | Monthly crop condition assessments | Public, GEOGLAM terms |
| FAO WaPOR | Water productivity and evapotranspiration data | Open, CC BY 4.0 (to check) |
| JRC Global Surface Water | Surface water occurrence and change | Free, licence to check |
| Copernicus Global Land Service | Vegetation, water and energy variables | Free, Copernicus terms |
| FEWS NET remote sensing products | Rainfall and vegetation for food security analysis | Public domain where US government |
Model EO in water and agriculture as a system. Run the cascade.
Assembled from the Nexus Standards area profile Climate 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
- Sea-level rise
- Coastal flood (set off downstream)
- Storm surge (set off downstream)
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Climate-driven migration and relocation (set off downstream)
- Mass displacement (set off downstream)
Systems and couplings
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-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H71Hazard family: Deep-time and cosmic catastrophes (NHS-1 planet.deep-time)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H75Hazard family: Climate-sensitive illness (NHS-1 health.climate-sensitive)NRS-H7Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)
Observation
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- CMIP6 climate projections (Copernicus Climate Data Store) (listed for reference)
- NGFS Scenario Explorer (listed for reference)
- INFORM Risk Index (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Total greenhouse gas emissions per year
- Direct economic loss attributed to disasters in relation to global GDP
Example geography
- Pacific Island states: 150 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
Who decides, and what the evidence serves.
Space and Earth Observation prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Ministries of agriculture | Where crops are under stress and harvests at risk | Crop monitoring; condition anomalies |
| Water resource agencies | How irrigation water is used and allocated | Evapotranspiration and water productivity |
| Statistics offices | How to integrate EO into agricultural statistics | Validation and comparison evidence |
Outputs this topic produces
- Crop monitoring workflows with validation
- Water use and productivity analyses
- Cropland and crop type mapping methods
- Evidence notes for agricultural statistics
The sector signature of EO in water and agriculture.
The sectors EO in water and agriculture touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Water and Food
Water gives food irrigation (0.8): the strongest relations between the sectors this subject joins.
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
- Food → Health · nutrition0.7
What experts ask.
Applied scientists working on water and agriculture ask practical questions of satellite data.
- How well can satellites estimate surface water extent, reservoir levels, soil moisture and evaporation?
- Can crop type, condition and yield be estimated early enough to inform food security decisions?
- How can satellite data detect drought stress and its impacts on farms and ecosystems?
- How reliable are estimates for smallholder farms and fragmented landscapes?
- How should satellite evidence be combined with ground data and local knowledge?
How EO in water and agriculture 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.
Earth observation chain
How a satellite or sensor record becomes evidence on EO in water and agriculture, and where each step adds uncertainty.
- 01 · Acquireorbit, sensor and revisitUncertainty: gaps from cloud and orbit
- 02 · Calibrateradiometric and geometricUncertainty: sensor drift
- 03 · Correctatmosphere and terrainUncertainty: aerosol and slope assumptions
- 04 · Retrieve or classifythe variable or the classUncertainty: model and training error
- 05 · Validateagainst ground truthUncertainty: sample design and size
- 06 · Publishwith error bars and lineageUncertainty: stated, never hidden
From signals to water and food indicators.
Water and vegetation have distinctive signatures. Open water absorbs infrared light and appears smooth to radar; soil moisture changes microwave emission and radar backscatter; healthy vegetation reflects strongly in the near infrared. Gravity missions detect changes in total water storage, including groundwater, over large regions. Thermal data supports estimates of evaporation and crop water use.
Converting these signals into decisions requires models. Crop yield estimates combine vegetation indices with weather and crop models; drought indicators combine rainfall, soil moisture and vegetation response. Accuracy depends on field size, cloud cover, crop calendars and the availability of local calibration data. Smallholder systems, with small, mixed and irregular fields, are among the hardest to monitor and among the most important for food security.
How the evidence is built, and where it stops.
- 01
Water mapping
Optical and radar mapping of surface water, using Earth observation. Vegetation-covered water and urban flooding are hard to detect.
- 02
Crop monitoring
Vegetation indices and classification for crop type and condition. Early-season estimates are uncertain and need ground calibration.
- 03
Drought indicators
Combining satellite and climate data with spatial statistics. Indicators measure stress, not impact on livelihoods.
- 04
Ground validation
Field surveys, gauges and farm data. Coverage is sparse where it matters most.
How it is studied, and what the methods cannot say.
- 01Earth observation and remote sensingChoosing sensors for a question · Change detection over time · Validating derived productsApplied here
- 02Spatial statistics and geostatisticsArea estimation with error-adjusted counts · Stratified sampling designApplied here
- 03Geospatial information systemsIntegrating EO with administrative data · Catalogues and interoperabilityApplied here
- 04High-performance, cloud and edge computingAnalysis next to the archive · Reproducible processing chains · National data cubesApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Can satellites predict harvests?
What is evapotranspiration?
What is WaPOR?
Can satellites measure groundwater?
How early can crop yields be estimated?
Is satellite data reliable for smallholder farms?
Contribute evidence from this page.
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