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GCRI · Food Nexus

Earth observation for crop monitoring

Satellites observe cropland every few days and across borders: greenness, water stress, planted area, flooding, burnt land and land cover change. Earth observation underpins crop monitoring and food security early warning, filling gaps where ground statistics are late, sparse or unavailable.

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01Register

Earth observation for crop monitoring on the record.

Method recordSTEM method
  1. MethodEarth observation and remote sensing
  3. Applied inFood Nexus
  6. Canonical pageSTEM Commons
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 food and agriculture, trade, resources and supply chains and climate and hazards, 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.

Crop condition monitoring

Vegetation indices and land-surface temperature are compared with their historical range for each cropland area and season, then summarised by region for the consensus assessments of GEOGLAM partners.

Crop type and area mapping

Optical and radar time series are classified with ground samples to estimate planted area by crop, with an accuracy assessment that states commission and omission error.

Irrigation and water productivity

Satellite evapotranspiration and biomass estimates map irrigated fields and how much crop is produced per unit of water consumed, as in FAO WaPOR.

03Geography

Growing conditions in the Black Sea region.

Crop greenness, root-zone soil moisture, plant growth and true-colour imagery over the Black Sea region, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 4

Growing conditions, for this page

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.

Growing conditions, framed on the Black Sea region.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 HLS S30Sources 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
  • 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
  • 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
  • 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
  • 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 HLS S30 (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 (13 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

  • Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
  • Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
  • Gross primary production, 8-day total: MODIS Aqua GPP (MYD17), NASA LP DAAC · 30 Sep 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
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 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.

DataRoleLabel
Sentinel-2 and Landsat optical imageryCrop condition, type and areaOBSERVED
Sentinel-1 radarCrop mapping through cloud, flood extentOBSERVED
MODIS and VIIRSVegetation and temperature time seriesOBSERVED
FAO WaPOREvapotranspiration and biomassMODELLED
Ground reference samplesTraining and validationOBSERVED
05Simulator

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 Black Sea region; 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
0.74% of a year's output
Total over direct (multiplier)
1.79 times
Most affected other sector
Wholesale and retail
Its peak inoperability
3.2%
Numbers, methods and sources
SectorPeak inoperability (%)Output weight (%)
Farming31.3117.6
Fertiliser and inputs2.2612.2
Food processing0.4822.4
Transport1.9018.4
Wholesale and retail3.259.6
Energy1.8119.8

Methods

  1. Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
  2. Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
  3. Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. 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
A shock through an input-output economy. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).

Across the six pillars, led by Nexus Consortium

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.

CheckHow it is done
Accuracy assessmentMaps are scored against an independent probability sample of ground reference points, and area estimates are adjusted for classification error
Cross-sensor consistencyTrends are checked across sensors and processing versions before they are reported
Cloud and gap reportingMissing observations are reported as no data, never as normal conditions

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 crop monitoring as a system. Run the cascade.

Assembled from the Nexus Standards area profile Food security and nutrition risk: 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

Controls

  • NS-0 Constitution and principles
  • NS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NRS-H57 Hazard family: Humanitarian crises (NHS-1 soc.humanitarian)
  • NRS-H61 Hazard family: Cascading failure (NHS-1 sys.cascade)
  • NRS-H81 Hazard family: Malnutrition and micronutrient deficiency (NHS-1 health.nutrition)
  • NRS-H59 Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)
  • NRS-N3 Food and agriculture risk and resilience
  • NRS-N7 Water-energy-food-health-biodiversity-infrastructure nexus coupling

Observation

  • IPC acute food insecurity analyses (listed for reference)
  • FEWS NET outlooks and data (listed for reference)
  • FAO GIEWS Food Price Monitoring and Analysis tool (listed for reference)
  • CHIRPS quasi-global gridded precipitation (listed for reference)
  • Prevalence of undernourishment
  • Prevalence of moderate or severe food insecurity
  • Prevalence of stunting among children under 5 years of age
  • Prevalence of malnutrition among children under 5 years of age

Example geography

  • The Ganges and Brahmaputra Basin: 178 hexagonal cells
  • Surface soil moisture (SMAP), NASA GIBS
  • 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
08Connections

Topics that rely on this method.

09Sector signature

The sector signature of earth observation for crop monitoring.

The sectors earth observation for crop monitoring 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

Food and its partners

Food gives nutrition to health (0.7), land conversion to biodiversity (0.6) and withdrawals and runoff to water (0.3).

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
Teaching values for a typical city-region, not measurements for any place. Coupling is the summed strength of the relations in play, as a share of all fifteen.
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.

Canonical page: Earth observation and remote sensing

11Methods and evidence

How earth observation for crop monitoring 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 crop monitoring: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−++FoodTradeClimateCommunitiesEconomyR reinforcingB balancing
Causal loop diagram from the page's systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
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.

13Questions

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.
14Earth observation

Current events and the layer stack.

Recent events from open feeds, each with its measure, time, source and label, and the satellite layers read for this page; a committed snapshot, never a live feed.

Earth events

Recent earthquakes and natural events

Snapshot of 2026-10-24 05:24 UTC. Not live: a committed copy of the feeds, refreshed when the site is rebuilt.

  1. Earthquake 191 km ENE of Nagqu, China Magnitude 7 mww, 2026-10-22 14:33 UTC, USGS, verified
  2. Earthquake 197 km WNW of Port-Olry, Vanuatu Magnitude 6.9 mww, 2026-10-24 02:31 UTC, USGS, verified
  3. Earthquake 170 km NW of Port-Olry, Vanuatu Magnitude 6.3 mb, 2026-10-24 02:35 UTC, USGS, verified
  4. Earthquake 197 km NW of Port-Olry, Vanuatu Magnitude 6.3 mww, 2026-10-20 04:20 UTC, USGS, verified
  5. Earthquake 186 km NW of Port-Olry, Vanuatu Magnitude 6 mww, 2026-10-19 07:49 UTC, USGS, verified
  6. Earthquake 203 km NW of Port-Olry, Vanuatu Magnitude 5.8 mb, 2026-10-24 04:12 UTC, USGS, verified
  7. Earthquake 205 km NW of Port-Olry, Vanuatu Magnitude 5.7 mb, 2026-10-24 04:05 UTC, USGS, verified
  8. Earthquake 61 km WNW of Leni, Italy Magnitude 5.7 mww, 2026-10-23 10:19 UTC, USGS, verified
  9. Earthquake 203 km WNW of Port-Olry, Vanuatu Magnitude 5.7 mb, 2026-10-19 18:55 UTC, USGS, verified
  10. Earthquake 198 km NW of Port-Olry, Vanuatu Magnitude 5.7 mww, 2026-10-19 18:52 UTC, USGS, verified
  11. Earthquake 192 km S of Burica, Panama Magnitude 5.4 mww, 2026-10-20 17:05 UTC, USGS, verified
  12. Earthquake 196 km NW of Port-Olry, Vanuatu Magnitude 5.4 mb, 2026-10-19 10:40 UTC, USGS, verified

Sources: U.S. Geological Survey (earthquakes; automatic solutions are preliminary); NASA Earth Observatory Natural Event Tracker (EONET); each event keeps its original source; Land: Natural Earth 1:110m (public domain). Indicative, not official warnings.

Earth observation

the world: the layers that describe it

11 layers over the same ground: licensed products committed with this site, and NASA imagery loaded on request. Each keeps its measure, unit, date, source and label; none is added to another.

  1. Satellite view true colour (corrected reflectance), daily, shown for 2026-10-28, MODIS Terra corrected reflectance (NASA), Observed
  2. Night lights night-time light (annual composite), radiance, as of 2016-01-01, NASA Black Marble (VIIRS, Suomi NPP), Observed
  3. Land cover land cover type (IGBP classes), class, as of 2024-01-01, MODIS Land Cover Type MCD12Q1 (NASA LP DAAC), Modelled
  4. Elevation elevation, colour index, m, undated reference, ASTER GDEM v3 (NASA, METI), Observed
  5. Relief and bathymetry shaded relief (imagery), undated reference, NASA Blue Marble (MODIS), Observed
  6. Heat exposure share of residents on ground at 45 C or more by day, % of residents, as of 2026-09-14, VIIRS NOAA-20 L3 8-day land surface temperature, day, via NASA GIBS, 8-day composite ending 2026-09-14, committed at build time (a snapshot, not a live feed). Ground skin temperature, not air temperature or a heat warning., Snapshot (observed 2026-09-14)
  7. Generating capacity capacity of plants of 300 MW and more, MW per cell, as of 2021-06-02, WRI Global Power Plant Database v1.3.0 (2021), World Resources Institute and data contributors; capacities as reported, summed per hexagon resolution-2 cell by the Global Centre for Risk and Innovation (GCRI)., Reported
  8. Agricultural water risk overall water risk, agriculture weighting, category, as of 2023-07-05, WRI Aqueduct 4.0 (2023), World Resources Institute, CC BY 4.0; summarised to hexagon resolution-2 cells by the Global Centre for Risk and Innovation (GCRI).., Modelled
  9. Baseline water stress baseline water stress, category, as of 2023-07-05, WRI Aqueduct 4.0 (2023), World Resources Institute, CC BY 4.0; summarised to hexagon resolution-2 cells by the Global Centre for Risk and Innovation (GCRI). Baseline 1979–2019., Modelled
  10. Residents residents per cell (lower bound), people, as of 2020-01-01, SEDAC GPWv4.11 population density 2020 via NASA GIBS, 2020; per hexagon resolution-2 cell, a lower bound (Density-class mid-points times area at 0.1 degree; the top density class counts at its lower bound, so dense cities are under-counted.), Estimated
  11. Land land and sea (basemap), undated reference, Natural Earth 1:110m land, Observed

Imagery layers: 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).

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Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRIFood NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceEvidence Brief

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