The Global Centre for Risk and Innovation (GCRI)
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.
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For eo in water and agriculture, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, crop greenness and land cover, framed on the Arctic; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
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Satellite crop monitoring is the use of Earth observation to track crop condition, area and water use across whole countries. Vegetation indices show stress, thermal data estimate evapotranspiration, and small fields need high resolution, with all estimates calibrated and validated against field surveys and official statistics.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
EO in water and agriculture · Flood basin AOI · Activation ACT-079
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| P-1 | Radar | n/a | Processed |
| P-2 | Optical | 79 | Cloudy |
| P-3 | Radar | n/a | Downlinking |
| P-4 | Optical | 10 | Scheduled |
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 indices and their changes through the season indicate crop vigour and stress, which are compared with past seasons to flag problems.
Thermal and optical data estimate evapotranspiration, showing how much water crops use and where irrigation occurs.
Small fields and mixed cropping need high-resolution data and local training samples; coarse data blend fields and misclassify crops.
Yield and area estimates from space need field surveys and statistics to calibrate and validate them.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Vegetation index | A combination of spectral bands that indicates the amount and vigour of green vegetation. |
| Water productivity | The amount of crop produced per unit of water consumed. |
| Crop type mapping | Classifying satellite images to show which crops grow where in a given season. |
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 official statistics | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
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 |
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 |
Applied scientists working on water and agriculture ask practical questions of satellite data.
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.
Optical and radar mapping of surface water, using Earth observation. Vegetation-covered water and urban flooding are hard to detect.
Vegetation indices and classification for crop type and condition. Early-season estimates are uncertain and need ground calibration.
Combining satellite and climate data with spatial statistics. Indicators measure stress, not impact on livelihoods.
Field surveys, gauges and farm data. Coverage is sparse where it matters most.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.