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Earth observation and remote sensing

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. Space and Earth Observation stewards this page for the Nexus Consortium.

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01Workspace

The workspace for earth observation and remote sensing.

Earth observation run

The Global Centre for Risk and Innovation (GCRI) · Earth observation and remote · Run R-7626 · Example interface, not a record

Scene processingGCRI · GCRI

Acquisition

revisit and swath

SAT-1SAT-2SAT-3SAT-4Swath over the area of interestN

Accuracy assessment

stratified sample

Water9197149
Cropland8575238
Built-up9594137
Forest9392124

Processing chain

  1. AcquireL1
  2. Calibrateradiometric
  3. Correctatmospheric
  4. Maskcloud · shadow
  5. ClassifyEarth observation
  6. Validateground truth
Earth observation run.Illustrative interface; not live data.

GCRI · Earth observation run. Earth observation run: example names and illustrative values, not a record.

02The method

What earth observation and remote sensing covers.

  1. 01

    Core methods

    image classification; spectral index analysis; synthetic aperture radar processing; change detection; radiometric and atmospheric correction; time-series compositing.

  2. 02

    Data and instruments

    optical multispectral satellites; synthetic aperture radar satellites; thermal infrared sensors; airborne lidar; drone imagery.

  3. 03

    Validation

    Products are assessed against ground reference data using accuracy matrices and independent samples. Cross-calibration between sensors checks consistency over time.

  4. 04

    Limits

    Clouds, revisit gaps and mixed pixels limit what can be observed. Changes in sensors and processing can create artificial trends.

  5. 05

    Use in risk decisions

    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.

03Geography

Earth observation worldwide.

Land cover and vegetation across the world, 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 (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
Observed (clouds and night leave gaps)
Source
VIIRS NOAA-20 NDSI (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite detection; clouds and night leave gaps
Newest at build
2026-10-14
Resolution
about 500 m grid
Uncertainty
Satellite detection; clouds and night leave gaps. 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
Observed
Source
GHRSST MUR (NASA JPL PO.DAAC)
Provider
NASA JPL PO.DAAC
Licence
NASA open data
Vintage
daily; Satellite analysis
Newest at build
2026-10-12
Resolution
about 1 km grid
Uncertainty
Satellite analysis. 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

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 world.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
  • GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); 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 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
  • 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
  • Snow cover: VIIRS NOAA-20 NDSI, NASA LANCE · 13 Oct 2026
  • Sea ice concentration: GHRSST MUR, NASA JPL PO.DAAC · 12 Oct 2026

04Reference practice

Earth observation practice is coordinated through CEOS and open missions.

ReferenceWhat it sets
CEOS Analysis Ready DataProcessing standards so data can be used without specialist correction
Copernicus and LandsatOpen-access optical and radar missions
International Charter Space and Major DisastersRapid satellite data for disaster response
STAC specificationCataloguing of spatiotemporal assets
05Process

How the process runs.

Process

Earth observation and remote sensing: Catastrophe model pipeline

Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, average annual loss, exceedance curves, PML and TVaR.

  1. 01 Hazard

    1. 01Event catalogueDataset · Model developerThe hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.
    2. 03Hazard moduleModel · Model developerFor each event the hazard module gives the local hazard intensity that the vulnerability module reads.

  2. 02 Exposure and damage

    1. 02ExposureDataset · Independent reviewerThe exposure module houses the portfolio data, such as location-specific information.
    2. 04Vulnerability moduleModel · Model developerThe vulnerability module quantifies the expected damage to the exposures from an event.

  3. 03 Financial

    1. 05Financial moduleModel · Independent reviewerThe financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.
    2. 06Event loss tableDataset · Independent reviewerAn event loss table lists each event with its annual occurrence probability and its loss for the portfolio.
    3. 07Year loss tableDataset · Independent reviewerSome models also sample simulated years from event losses. This outline does not describe their format.

  4. 04 Metrics

    1. 08Exceedance probability curvesModel · Independent reviewerThe occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.
    2. 09AAL, PML and TVaRModel · Independent reviewerAverage annual loss sums event probability times loss. PML is one point on the occurrence curve.

  5. 05 Use of results

    1. 10Use of resultsDecision · Independent reviewerThe tail metrics are used when considering a portfolio’s exposure and the level of catastrophe reinsurance an insurer requires.

Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.

06In systemic risk work

Consistent observation across borders.

Satellites observe every country with the same instrument, which makes them uniquely useful for cross-border and data-scarce analysis. The risk is over-reading: a classification of flood extent or crop type has error rates that vary with cloud, terrain and season, and these errors must be carried into any downstream analysis.

  • Report accuracy assessments with confusion matrices for classifications.
  • Radar sees through cloud but misreads flooded vegetation and urban areas.
  • Revisit time limits what fast events can be observed.
  • Applications on Space and Earth Observation and Water Nexus.
07Applied in

Platforms that apply this method.

08Stewardship

Who maintains this page.

Space and Earth Observation stewards earth observation and remote sensing: it keeps this description current and reviews how the method is applied across the platforms.

09Sources

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.

10Questions

Questions and answers.

Why does one institution host every method page?
GCRI hosts the STEM Commons so that each method has one canonical description. Platforms link to it rather than repeat it, which keeps definitions consistent.
How do I suggest a correction to a method page?
Write to the integrity concern form with the passage and the reason.
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