FAQs

What does the SPECT tool show?

SPECT is an open resource designed to help users understand how current satellite technologies compare according to a metric of “completeness.” The concept of completeness, originally published in a 2022 paper by Daniel Jacob et al. and described as “observing system completeness,” evaluates satellites’ ability to detect large point sources of methane emissions globally. The tool visualizes the components of completeness, including spatial, temporal, and detection coverage. It offers preset comparisons about many existing and near-future satellites. It also allows users to enter and test their own instrument parameters (e.g., minimum detection limit) under different assumptions.

What does “satellite completeness” mean?

Satellite completeness is a metric that allows for a more direct comparison between satellites related to the percentage of high-emission methane sources they are able to detect. It does not evaluate the ability of a satellite to detect all global methane, which would include area sources that occur over broad regions and are distinct from point sources. Completeness combines a satellite’s detection sensitivity, spatial coverage, and sampling frequency to calculate the portion of detectable emissions from a given population of large methane point sources.

Why is satellite completeness important?

Large industrial sources of methane are responsible for 20 to 50 percent of total methane emissions from oil and gas systems, coal mines, landfills, and livestock operations in the United States and globally. Detecting “super-emitters,” or large point sources of methane (>25 kg/hr), represents our biggest near-term opportunity to mitigate this superpotent greenhouse gas in our crisis-relevant timeframe.

The best way to view methane super-emitters is with satellites, especially those capable of global observations — and satellites are playing a growing and important role in making invisible methane visible, including in places that are otherwise off-limits or difficult to reach for measurement. However, there is no “one size fits all” solution when it comes to satellites, and there is synergy between point source imagers and others that make high-precision methane measurements over broad areas.

Who is this tool designed for?

SPECT is designed to help policymakers, regulators, NGOs, and scientists who are actively designing programs and policies to use remote monitoring to help achieve their climate goals, such as the Paris Agreement or the Global Methane Pledge. The metric of completeness offers a way to compare detection across different satellites and can help operators identify which instruments offer the best prospect of quantifying leak detection and assessing the success of implemented mitigation strategies. This metric can also help regulators design and implement methane monitoring programs and enable civil society to compare satellites to other measurement technologies to maximize the mitigation potential of abatement programs.

Because the SPECT tool is specifically designed to assess the completeness of satellite technologies, it is not well-suited to address other methane-related measurement challenges, like measuring emissions from abandoned oil wells (which leak methane but not enough to qualify as super-emitters) or creating a total global inventory of methane emissions.

How is satellite completeness calculated?

Calculating completeness requires knowledge of satellite parameters, including daily spatial coverage per satellite and minimum detection limits. It also requires assumptions about the global conditions in which the satellite is operating, such as the distribution and persistence of methane emissions.

The metric is the product of three sub-scores, as follows:

Completeness = C_MDL × C_spatial × C_temporal

Where:

C_MDL = the percentage of detectable emissions, based on the minimum detection limit, assuming perfect sampling

C_spatial = the percentage of total area of interest imaged,

C_temporal = the probability that an emissions source is detected, based on sample frequency and source intermittency, accounting for atmospheric conditions

This equation comes from Daniel Jacob et al., “Quantifying Methane Emissions from the Global Scale Down to Point Sources Using Satellite Observations of Atmospheric Methane,” preprint in Atmospheric Chemistry and Physics, April 11, 2022.

What data were used to develop the completeness metrics presented in this tool?

Satellite parameters, namely detection limits, spatial coverage, and descriptions, were sourced to the best of the authors’ ability by citing open literature and websites that detail instrument performance. A full list of sources can be found in the Appendix of the affiliated Satellite Completeness Report. Land areas of methane-emitting sectors were also estimated from a variety of open-source data sets, and the full methodology is outlined in the Appendix. It is often difficult to source technical specifications for an apples-to-apples comparison, so please contact the RMI team to alert us to mistakes or with suggestions for improving the data.

What other assumptions went into the completeness metrics presented in this tool?

The authors make necessary assumptions based on the best available information about satellite parameters and the global conditions in which they operate. For example, the minimum detection completeness score for any instrument is set to 10 percent, in alignment with this study on the contribution of ultra-emitters to global methane emissions. Additionally, for some instruments, daily satellite coverage is cited based on available resources, but these areas may be an overestimate, given that few instruments are focused on observing only methane, and many satellites (e.g., PRISMA, Worldview-3) are often tasked with looking at other types of targets. Further information about uncertainty in emissions distribution curves and emissions source persistence can be found in the report.

Who do I contact with questions about the tool or the data?

Contact RMI’s Climate Intelligence team by email at climateintelligence@rmi.org.