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.
The Global Centre for Risk and Innovation (GCRI)
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.
GCRI · Established · service level R0
Earth observation for crop monitoring is the use of satellite imagery to track crop condition, planted area, crop type, irrigation and flood or fire damage across whole regions every few days. It underpins food security early warning where ground statistics are late or sparse, and every map is checked against ground reference samples.
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.
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.
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.
Satellite evapotranspiration and biomass estimates map irrigated fields and how much crop is produced per unit of water consumed, as in FAO WaPOR.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| NDVI | The normalised difference vegetation index, a ratio of red and near-infrared reflectance that rises with green vegetation cover and vigour. |
| Crop type map | A classification of satellite imagery into crops, trained and tested on field-collected reference samples. |
| Ground reference sample | A field observation of what is actually on the ground at a location, used to train and validate satellite maps. |
Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one, shown for Thailand as an example geography for this subject.
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.
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-2 and Landsat optical imagery | Crop condition, type and area | OBSERVED |
| Sentinel-1 radar | Crop mapping through cloud, flood extent | OBSERVED |
| MODIS and VIIRS | Vegetation and temperature time series | OBSERVED |
| FAO WaPOR | Evapotranspiration and biomass | MODELLED |
| Ground reference samples | Training and validation | OBSERVED |
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 |
|---|---|
| Accuracy assessment | Maps are scored against an independent probability sample of ground reference points, and area estimates are adjusted for classification error |
| Cross-sensor consistency | Trends are checked across sensors and processing versions before they are reported |
| Cloud and gap reporting | Missing 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.
Space and Earth Observation stewards earth observation and remote sensing for the Consortium and reviews how it is applied across platforms.
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.