GCRI · Space and Earth Observation
Earth observation sensors
Earth observation sensors measure reflected sunlight, emitted heat, radar echoes and laser returns from orbit. Each sensor type sees some things well and others not at all: optical sensors are blocked by cloud, radar sees through it, thermal sensors measure heat. This page sets out how the main sensor types work, how their resolution and revisit shape what they can detect, and what evidence serves the users choosing data for a task.
Earth observation sensors in seven parts.
- 01The questionEarth observation sensors: how the risk develops
- 02Across the domainsEach sensor answers questions in specific domains
- 03IndicatorsWhat is measured, and how well
- 04SourcesWhere the evidence comes from
- 05DecisionsWho decides, and what the evidence serves
- 06Core questionsWhat experts ask
- 07Mechanisms and driversWhat each sensor sees
Earth observation sensors: how the risk develops.
Optical, radar, thermal and hyperspectral methods. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Optical sensors need light and clear skies
Multispectral and hyperspectral sensors measure reflected sunlight in many bands, revealing vegetation, water and materials. Cloud and darkness block them.
Radar sees through cloud
Synthetic aperture radar sends its own microwave pulses and records echoes, day or night and through cloud, sensing surface roughness, moisture and movement.
Resolution trades against coverage
Spatial, spectral and temporal resolution compete. Very high resolution images cover small areas; wide-swath sensors revisit often but see less detail.
Orbits set revisit
Sun-synchronous orbits give consistent lighting; constellations of many small satellites shorten revisit times but vary in calibration.
Earth observation in the Arctic.
Land cover and vegetation 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 2
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
- 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
- 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
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 · U.S. Geological Survey · 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.
- Live: U.S. Geological Survey 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
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, 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
Each sensor answers questions in specific domains.
- WaterCouplingRadar maps floods through cloud.What to watchDetection under vegetation and in cities
- FoodCouplingOptical and radar series track crop condition.What to watchCloud gaps in growing seasons
- BiodiversityCouplingOptical, radar and lidar measure forest change and structure.What to watchAccuracy against field plots
Sensor evidence is designed for the Observatory Signal series. Copernicus and Landsat provide open data; CEOS Analysis Ready Data sets processing expectations.
- Calibration and validation data.
- Field reference observations.
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
Earth observation sensors · Flood basin AOI · Activation ACT-815
Passes over the area
next 7 passes
Acquisitions
| P-1 | Radar | n/a | Processed |
| P-2 | Optical | 84 | Cloudy |
| P-3 | Radar | n/a | Downlinking |
| P-4 | Optical | 30 | 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 |
|---|---|---|---|---|
| Spatial resolution | metres per pixel | per sensor | Mission specifications | DECLARED |
| Revisit interval | days | per mission or constellation | Mission specifications | DECLARED |
| Cloud-free observation frequency | observations per month | per location | Archive analysis | OBSERVED |
| Radiometric calibration uncertainty | percent | per sensor | Calibration reports | REPORTED |
| Spectral bands | count | per sensor | Mission specifications | DECLARED |
| Data latency | hours from acquisition to availability | per product | Data provider records | OBSERVED |
Model earth observation sensors 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 |
|---|---|---|
| Copernicus Sentinel missions (ESA and European Commission) | Radar, optical, ocean and atmospheric data | Free and open, Copernicus terms |
| Landsat archive (USGS and NASA) | Optical and thermal imagery since the 1970s | Public domain, US government data |
| CEOS Missions, Instruments and Measurements database | Characteristics of Earth observation missions | Public, CEOS terms |
| WMO OSCAR database | Observing system capabilities and requirements | Public, WMO terms |
| NASA Earthdata | MODIS, VIIRS, GEDI and other mission data | Open, NASA data policy |
How earth observation sensors 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 earth observation sensors, 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 |
|---|---|---|
| Agencies and analysts using EO | Which sensors and products suit a task | Sensor selection guides; coverage analyses |
| Space agencies | Which missions and instruments to fund next | Gap and continuity evidence |
| Procurement bodies | When commercial data add value over free data | Resolution and revisit comparisons |
Outputs this topic produces
- Sensor selection guides by application
- Cloud-free coverage analyses by region
- Calibration and accuracy summaries
- Evidence notes on mission continuity
What experts ask.
Remote-sensing scientists approach any application with a set of sensor questions.
- Which physical property does the question require: reflectance, roughness, moisture, temperature or composition?
- What spatial, temporal and spectral resolution does the application need, and what trade-offs follow?
- How are sensors calibrated and cross-calibrated so that records from different missions can be combined?
- How do clouds, atmosphere, terrain and illumination affect the measurement?
- When does combining sensors add information, and when does it add error?
What each sensor sees.
Optical sensors record reflected sunlight in visible and infrared bands, revealing vegetation, water, soils and built surfaces. They need daylight and clear skies. Thermal sensors record emitted heat, useful for surface temperature, fires and urban heat. Synthetic aperture radar sends its own microwave pulses and measures the return, so it works day and night and through cloud; it is sensitive to surface roughness, structure and moisture, and interferometric techniques can measure ground movement of very small magnitude.
Hyperspectral sensors record many narrow bands, allowing identification of minerals, plant stress and water quality, at the cost of large data volumes and often coarser coverage. Every sensor involves trade-offs between spatial detail, revisit frequency, spectral range and swath width. No single sensor answers every question.
How the evidence is built, and where it stops.
- 01
Radiometric calibration
Converting raw signals to physical units using onboard references and ground sites, grounded in physics. Calibration drifts over a mission’s life.
- 02
Atmospheric correction
Removing atmospheric effects to estimate surface properties. Errors are largest over water, in haze and in mountainous terrain.
- 03
Radar processing
Speckle filtering, terrain correction and interferometry, within Earth observation. Interpretation of radar signals needs specialist expertise.
- 04
Sensor fusion
Combining data from multiple sensors. Mismatched timing and resolution can introduce artefacts.
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
- 02Physics and quantum scienceRadiative transfer for atmospheric correction · Microwave scattering · Orbit determinationApplied here
- 03Space systems and satellite servicesMission continuity analysis · Space segment resilience · Debris compliance assessmentApplied 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.
Where this page leads across the Consortium.
Questions and answers.
What is the difference between optical and radar satellites?
What does spatial resolution mean?
Is satellite data free?
Why use radar if optical images are easier to interpret?
Is higher resolution always better?
Can records from old and new satellites be compared?
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