GCRI · Space and Earth Observation
Spatial temporal evidence
Satellite images become evidence only after calibration, atmospheric correction, georeferencing, cloud masking and validation. Long records need consistency across sensors and decades. This page sets out how raw observations become analysis-ready data and validated products, how accuracy is assessed and reported, and what evidence serves the users who rely on maps and time series built from space.
Spatial temporal evidence on the record.
Spatial temporal evidence: how the risk develops.
Ground truth, resolution and uncertainty. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Corrections turn signals into measurements
Radiometric and atmospheric correction convert recorded signals into surface reflectance or backscatter. Errors in correction propagate into every derived product.
Geolocation must match
Images must align precisely with each other and with maps. Misregistration of even a pixel creates false change along edges.
Records span many sensors
Long time series combine sensors with different characteristics. Harmonisation is needed so that change reflects the ground, not the instrument.
Validation needs ground truth
Classified maps and estimates must be checked against independent reference data, and accuracy reported with confidence intervals.
Earth observation in the Arctic.
Land cover, night lights and population density 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
Earth observation, for this page
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
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (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 vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- 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
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.
Earth observation, 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 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
- 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 Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- 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 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).
- 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 (11 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
- 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
Space-based evidence must be accurate to be useful to any domain.
Methods are designed for the Methods and Technical series. Olofsson and colleagues (2014) set good practice for area and accuracy; products would carry STAC and NS-3b metadata under the proposed Nexus Standards.
- Reference samples for accuracy assessment.
- Method comparisons on the same area.
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
Spatial temporal evidence · Flood basin AOI · Activation ACT-531
Passes over the area
next 6 passes
Acquisitions
| P-1 | Radar | n/a | Processed |
| P-2 | Optical | 79 | Cloudy |
| P-3 | Radar | n/a | Downlinking |
| P-4 | Optical | 22 | 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 |
|---|---|---|---|---|
| Overall map accuracy | percent with confidence interval | per product | Accuracy assessment against reference data | ESTIMATED |
| Class-specific user’s and producer’s accuracy | percent | per class | Accuracy assessment | ESTIMATED |
| Geolocation error | metres | per product | Geometric validation | OBSERVED |
| Products meeting analysis-ready data specification | count | per assessment | CEOS ARD assessments | Published statistics |
| Reference samples per class | count | per assessment | Validation design | DECLARED |
| Time series gaps | percent of expected observations missing | per location | Archive analysis | OBSERVED |
Model spatial temporal evidence 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
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 |
|---|---|---|
| CEOS Analysis Ready Data specifications and assessments | Processing requirements and product status | Public, CEOS terms |
| Harmonized Landsat and Sentinel-2 (NASA) | Consistent surface reflectance from both missions | Open, NASA data policy |
| Good practice guidance on accuracy assessment, such as Olofsson and others | Methods for estimating area and accuracy | Licence per publication |
| Digital Earth Africa and Digital Earth Australia | Analysis-ready data cubes and validated products | Open, CC BY 4.0 |
| CEOS Working Group on Calibration and Validation resources | Calibration and validation protocols and test sites | Public, CEOS terms |
How spatial temporal evidence 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 spatial temporal evidence, 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
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 |
|---|---|---|
| Mapping and statistics agencies | Whether EO-derived statistics meet quality needs | Accuracy assessments; quality statements |
| Data providers | How to process and document products | ARD pipelines; harmonisation methods |
| Users of EO maps | How much confidence to place in a product | Quality statements with intervals |
Outputs this topic produces
- Accuracy assessment designs with stratified sampling
- Analysis-ready data pipelines
- Harmonisation methods for multi-sensor records
- Product quality statements
What experts ask.
Evidence specialists ask what a product can support and with what confidence.
- How was the product validated, against what reference data, and in which regions and seasons?
- What is the smallest feature or shortest event the product can reliably detect?
- How is uncertainty expressed for each pixel, region or estimate, and does it reach users?
- How do classification errors affect area estimates and trends derived from maps?
- When are satellite estimates fit for a decision, and when is additional ground evidence needed?
Where error comes from.
Error enters at every stage: sensor noise and calibration, atmospheric correction, geolocation, the algorithm that converts measurements into a variable, and the classification rules that turn continuous signals into categories. A pixel often mixes several land covers. Temporal sampling can miss short events, such as a flood peak that passes between overpasses. Validation data may come from places and seasons that differ from where the product is used.
A well-established finding in remote sensing is that simply counting classified pixels can give biased area estimates. Statistically rigorous approaches combine a map with a probability sample of reference observations to estimate area and its confidence interval. The same principle applies widely: evidence from space is strongest when paired with designed ground sampling.
How the evidence is built, and where it stops.
- 01
Accuracy assessment
Comparing products with independent reference data using sampling designs from spatial statistics. Reference data has its own errors.
- 02
Uncertainty propagation
Carrying error from inputs to outputs. Correlated errors are often ignored, understating uncertainty.
- 03
Ground observation
Field surveys, in-situ sensors and citizen observations. They are sparse and unevenly distributed.
- 04
Scale analysis
Testing how results change with resolution using geospatial information systems. Aggregation can hide local extremes.
How it is studied, and what the methods cannot say.
- 01Spatial statistics and geostatisticsArea estimation with error-adjusted counts · Stratified sampling designApplied here
- 02Earth observation and remote sensingChoosing sensors for a question · Change detection over time · Validating derived productsApplied here
- 03High-performance, cloud and edge computingAnalysis next to the archive · Reproducible processing chains · National data cubesApplied here
- 04Geospatial information systemsIntegrating EO with administrative data · Catalogues and interoperabilityApplied 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.
What is analysis-ready data?
How accurate are satellite maps?
Why combine Landsat and Sentinel-2?
Why not simply count pixels to measure area?
What is ground truth?
Should every map show uncertainty?
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