The Tool Desk
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Choose the product that fits your question, then check its coverage, quality information, processing version and access terms before drawing conclusions. The main options include Copernicus satellite products, the Copernicus Atmosphere Monitoring Service (CAMS), NASA-accessible GHGSat data, and Carbon Mapper’s portal.
Choose the quantity that answers your question
Greenhouse-gas maps can look similar while representing different quantities. Read the variable name and product description before interpreting colors, comparing locations or calculating emissions.
| Product or quantity | What it represents | Useful for | Key interpretation limit |
|---|---|---|---|
| XCO₂ (Copernicus satellite-derived CO₂ catalogue) | Column-averaged carbon dioxide mixing ratio, retrieved from reflected short-wave infrared radiation. | Studying atmospheric CO₂ across observed areas and comparing column measurements over time. | Observations effectively require cloud-free daylight conditions; XCO₂ is a column quantity, not a facility emissions rate. |
| MTCO₂ (Copernicus satellite-derived CO₂ catalogue) | Mid-tropospheric CO₂ derived from thermal-infrared measurements. | Examining CO₂ in the middle troposphere, including observations that can be made at night. | It has limited sensitivity to near-surface CO₂, so it is not a direct view of ground-level emissions. |
| TROPOMI Level-2 methane (Sentinel-5P product documentation) | Retrieved methane column concentration for individual satellite observations, with a column averaging kernel and diagnostic information. | Broad-area methane mapping and identifying areas for closer investigation. | A methane column observation is not automatically a source-specific emission rate; quality flags and retrieval diagnostics matter. |
| CAMS net surface fluxes (CAMS catalogue) | Flux estimates inferred by atmospheric inversion, using observed concentrations and atmospheric transport modelling. | Analysing estimated net surface exchanges of CO₂, CH₄ or N₂O at the scope and scale of the selected product. | Net flux can combine natural and human sources and sinks. It is not automatically an industry’s or facility’s emissions total. |
| Plume or facility-oriented observations (NASA’s CSDA GHGSat vendor page; Carbon Mapper portal) | Source-oriented observations, which can include plume or abundance information from satellite, space-based or airborne sensors. | Investigating a particular source or location at a finer scale than broad-coverage mapping may provide. | Check whether the selected product reports a plume observation, abundance or a derived emission rate; these are not interchangeable. |
Copernicus describes its XCO₂ and MTCO₂ products as complementing surface-observation networks. A satellite view adds a different kind of coverage; it does not make ground measurements or other observing systems irrelevant.
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Where to find the data
TROPOMI methane
The TROPOMI methane product documentation directs users to the Copernicus Data Space Ecosystem. Its granule names distinguish near-real-time (NRTI), offline (OFFL) and reprocessed offline (RPRO) products. These labels describe processing streams, so record which one you used. The product page also provides citation guidance for the applicable product version; its cited Version 02 form is “Copernicus Sentinel-5P (processed by ESA), 2021, TROPOMI Level 2 Methane products. Version 02. European Space Agency.” Use the citation that matches the files and version you actually downloaded.
Satellite-derived CO₂ products
The Copernicus catalogue lists XCO₂ products from SCIAMACHY/ENVISAT, GOSAT, GOSAT-2 and OCO-2, and MTCO₂ products from IASI and AIRS. It includes Level-2 orbit-track products and Level-3 gridded products. Check the live catalogue entry for the dataset’s observation period, processing algorithm and version rather than assuming every listed product has the same coverage or update schedule.
Global flux and concentration products
The CAMS catalogue provides net surface fluxes, atmospheric mixing ratios at model levels and column means for CO₂, CH₄ and N₂O. Updates, reprocessing and versions vary by dataset. For methane fluxes, check whether the specific stream uses surface observations alone or surface observations together with satellite observations.
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Facility-oriented data
NASA’s CSDA GHGSat vendor page explains product types and the access process. Users must request authorization, use NASA’s Satellite Data Explorer and follow the applicable CSDA license. NASA’s page reports typical spatial resolution below 30 m for the described GHGSat observations, above 50 m for GHGSat-D, and an approximately 12 km × 12 km field of view. These are figures from that vendor page, not a universal specification for every GHGSat product.
Carbon Mapper data
Carbon Mapper’s portal includes data from satellite, space-based and airborne sensors, with API and STAC API access. Carbon Mapper states that public portal data is available for non-commercial use by specified public-interest categories; commercial use or redistribution requires review of its terms and contact with the organization. Some Planet Tanager non-methane and CO₂ data is directed to Planet. Confirm the terms for the particular dataset before using or redistributing it.
Match spatial and temporal coverage to the job
A nominal pixel size is not the same as the width of the scene, the chance of observing a source, or how often it is revisited. Choose by the geographic extent and timing your analysis needs, not by resolution alone.
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- For broad-area mapping: TROPOMI Level-2 methane is a broad-coverage column product. Its product documentation gives a nadir resolution of approximately 7.0 × 7.0 km at mission start and approximately 7.0 × 5.5 km since 6 August 2019. These are historical specifications reported on the product page, not a like-for-like performance comparison with facility-oriented products.
- For finer-scale source investigation: NASA’s CSDA GHGSat page reports typical spatial resolution below 30 m (above 50 m for GHGSat-D) and an approximately 12 km × 12 km field of view. Check the exact sensor and product; the resolution and field-of-view figures describe different aspects of an observation.
- For a CO₂ column question: Compare XCO₂ and MTCO₂ with their different measurement approaches and vertical sensitivity in mind. Short-wave infrared XCO₂ relies on reflected sunlight and is effectively limited to cloud-free daytime observations; thermal-infrared MTCO₂ can be observed day or night but has limited near-surface sensitivity.
- For a trend or flux question: Check the product’s date range, temporal sampling, gridding and processing version. A global or gridded product may suit a broad analysis but cannot automatically answer a facility-level question.
The figures above come from different products and sources and should not be treated as a direct ranking of capability. Revisit interval, full coverage and validation performance depend on the specific product and are not established by a single resolution number.
Read quality metadata before interpreting a map
Retrieval quality varies across observations. For TROPOMI methane, the product documentation notes possible artifacts over inland water and some remaining bad pixels. Its Level-2 files include viewing geometry, fit residuals, retrieved albedo, aerosol properties and a column averaging kernel. Use the product’s quality information and diagnostics when filtering observations or explaining a pattern.
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For other products, inspect the relevant uncertainty estimates, quality flags, averaging kernels and validation information if supplied. Record any filters and thresholds used; excluding low-quality observations can change which locations or dates remain in an analysis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand what an atmospheric inversion adds
An atmospheric inversion uses a transport model to relate observed concentrations to estimated net surface fluxes. CAMS describes its inversions as accounting for winds, vertical diffusion and convection; chemical loss also matters for methane and nitrous oxide. The result is a model-based estimate informed by observations, not a raw satellite measurement of emissions at a point.
Because net surface fluxes may include natural biosphere sources and sinks as well as human sources, the geographic boundary and source scope matter. Before describing a flux as anthropogenic emissions, establish what sources the product includes and how its model defines the estimate. A regional net flux should not be relabelled as a facility’s emissions.
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Use a consistent checklist when comparing datasets
- Gas and variable: Is the product a column concentration, a mid-tropospheric concentration, plume abundance or inferred surface flux?
- Observation scale: What are the spatial resolution, scene width or field of view, and temporal sampling for this exact product?
- Product level and method: Is it a Level-2 orbit track or Level-3 gridded product? Which retrieval or inversion algorithm and version produced it?
- Quality and uncertainty: Which flags, uncertainty estimates, averaging kernels and validation information are available, and how will you use them?
- Source scope: Does the data describe a detected plume, or an estimate that may combine natural and anthropogenic fluxes?
- Access and reuse: Are authorization, an account, a particular platform, a license or restrictions on commercial use and redistribution involved?
These questions help distinguish a useful broad map from a dataset suitable for estimating fluxes or investigating a specific source. Cost, coverage, revisit interval and validation performance are product-specific; verify them against the exact live listing rather than assuming that one provider’s terms or specifications apply to another.
Document the dataset so results can be checked
For a reproducible analysis, save the provider and product name, gas and variable, product level, version, processing stream, observation period, download date, quality filters, uncertainty treatment and license terms. For an inferred flux, also document the input stream and model context. This record makes it possible to tell whether a later result differs because the atmosphere changed or because the dataset was updated or reprocessed.
Quick Recap
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