Illustrative: satellite orbits, their ground swaths or the lines of sight a receiver keeps

Risk Academy, led by the Nexus Consortium · Course catalogue · Earth and Environmental Sciences

Climate and Ocean Sciences

Earth observation and Earth system data

Earth observation and Earth system data, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Earth observation and Earth system data: foundations and practice

This area covers how the Earth system is observed and assembled into data: satellites, in situ networks, reanalyses and the digital twins that combine them with models.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
Planned
Prerequisites
An L1 course in the area, or an entry review

Why now

Analysis-ready, cloud-native Earth observation has become the norm: harmonised Landsat and Sentinel-2 at 30 m, NASA OPERA products, the NISAR radar mission launched in July 2025, and geospatial foundation models such as Prithvi make continental analysis possible on open tooling. STAC community, SpatioTemporal Asset Catalog specification, 2025

Learning outcomes

  1. Define essential Climate Variable and the other key terms of the discipline and use them correctly in a written case
  2. Explain satellite missions and sensors (optical, radar, thermal, altimetry, gravimetry) and in situ observing networks with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against reanalysis datasets such as ERA5, stating evidence, date and limits

Modules

  1. Satellite missions and sensors (optical, radar, thermal, altimetry, gravimetry)
    • Satellite missions and sensors (optical, radar, thermal, altimetry, gravimetry)
    • In situ observing networks
    • Reanalysis datasets such as ERA5
  2. GCOS Essential Climate Variables
    • GCOS Essential Climate Variables
    • Earth system digital twins and Destination Earth
    • Geospatial foundation models and satellite embeddings
  3. Analysis-ready data and cloud-native formats
    • Analysis-ready data and cloud-native formats
    • Product validation and uncertainty
    • Copernicus and open data policies
  4. Lab: the discipline's method
    • Revisit interval and cloud cover set how many clear looks a sensor gets in a season; the classifier's user's and producer's accuracy set how far the mapped area of change is from the true area; a reference sample of stated size gives the adjusted area and its confidence interval, and only that interval supports a decision.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Open data

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. Olofsson et al., Good practices for estimating area and assessing accuracy of land change, 2014
  2. Drusch et al., Sentinel-2: ESA's optical high-resolution mission for GMES operational services, 2012
  3. Stehman, Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes, 2014
02Live map

Earth observation in Italy

Land cover, vegetation, snow and sea ice, with true-colour imagery one switch away, over Italy.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Earth observation

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 Italy.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 EONETSources 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 H3 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 H3 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 H3 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 H3 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 H3 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 H3 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 H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 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.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • 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.
  • 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 (10 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

  • 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
  • Snow cover: VIIRS NOAA-20 NDSI, NASA LANCE · 13 Oct 2026
  • Sea ice concentration: GHRSST MUR, NASA JPL PO.DAAC · 12 Oct 2026
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

What is changing, with standards and evidence

Earth observation and Earth system data: frontier methods

The methods, data, tools and studies that changed earth observation and Earth system data between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Earth observation and Earth system data: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Analysis-ready, cloud-native Earth observation has become the norm: harmonised Landsat and Sentinel-2 at 30 m, NASA OPERA products, the NISAR radar mission launched in July 2025, and geospatial foundation models such as Prithvi make continental analysis possible on open tooling. STAC community, SpatioTemporal Asset Catalog specification, 2025

Learning outcomes

  1. Explain what changed in earth observation and Earth system data since 2024 and cite the studies, data or rules behind each change
  2. Reproduce cloud-native data on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. Cloud-native data
    • STAC catalogues, Cloud-Optimized GeoTIFF and Zarr
    • Analysis-ready data and the CEOS ARD specifications
    • Reproducible pipelines from catalogue to result
  2. Optical and radar
    • Harmonised Landsat Sentinel-2 (HLS) surface reflectance
    • SAR backscatter, interferometry and change; NISAR L- and S-band
    • OPERA surface water and disturbance products
  3. Geospatial foundation models
    • Self-supervised pretraining on multispectral time series
    • Fine-tuning for floods, burn scars and crops
    • Benchmarks, label scarcity and bias across regions
  4. Digital twins of the Earth
    • Destination Earth and kilometre-scale simulation
    • Coupling observation, model and decision
    • Data volume, access and provenance
  5. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist
  6. Technology that helps and the risks it carries
    • Machine learning forecasting, carbon dioxide removal and climate data infrastructure.
    • The governance of solar radiation modification and the integrity of carbon removal.

Labs with Academy tools

Open data

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 6 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. STAC community, SpatioTemporal Asset Catalog specification, 2025
  2. CEOS, CEOS Analysis Ready Data, 2024
  3. NASA JPL, NISAR mission, 2025
  4. European Commission, Destination Earth, 2025
  5. Olofsson et al., Good practices for estimating area and assessing accuracy of land change, 2014
  6. Drusch et al., Sentinel-2: ESA's optical high-resolution mission for GMES operational services, 2012
  7. Stehman, Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes, 2014
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Essential Climate Variable

A physical, chemical or biological variable that GCOS defines as critical to characterising the Earth's climate.

Term

Reanalysis

A consistent reconstruction of past weather and climate that combines observations with a fixed model and data assimilation.

Term

Data assimilation

The combination of observations with a model to give the best estimate of the state of a system.

Term

Digital twin of the Earth

A system that combines observations and high-resolution models to simulate and explore the Earth system interactively.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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