## 2 Background  
Several satellites measure CO\({}_{2}\) concentrations globally, including OCO-2/3, the Greenhouse Gases Observing Satellite (GOSAT), and PRISMA. These satellites use spectroscopic methods based on the absorption of reflected sunlight to estimate the column-averaged dry-air mole fraction of CO\({}_{2}\), known as XCO\({}_{2}\). [PERSON] et al. used XCO\({}_{2}\) retrievals from OCO-2 to quantify the CO\({}_{2}\) emissions from large power plants, comprising seven in the US and seven in other countries [12]. The narrow swath width of OCO-2 and cloud cover significantly limit the effective revisit time and applicability of this technique to snapshots of emission estimates--a total of twenty snapshot estimates across all fourteen of the study's power plants between the years 2014 and 2018. Other studies have also used OCO-2 and/or OCO-3 but with similar coverage limitations: one observation of each of two plants [13], fourteen plumes from six plants [14], or 106 observations from 78 plants [15]. [PERSON] et al. used XCO\({}_{2}\) retrievals from the AVIRIS Next-Generation (AVIRIS-NG) and GAO, as well as PRISMA, to quantify CO\({}_{2}\) emissions from seventeen US coal- and gas-fired power plants [7]. While PRISMA was not designed to measure CO\({}_{2}\), research teams have used various retrieval algorithms for XCO\({}_{2}\), though it is not an official PRISMA data product. Several other researchers have also reported methods to use XCO\({}_{2}\) to estimate emissions from individual power plants [16; 17; 18; 19; 20; 21]. These studies show that it is possible to estimate the emissions of large power plants with remote sensing and can provide an independent way to verify reported estimates. However, the small number of observations cannot create a comprehensive global power plant emissions monitoring system, and the detection limits of the approaches restrict their applicability to only the largest of power plants. Future planned satellites, such as the Copernicus CO\({}_{2}\) Monitoring (CO2M) mission [22; 23], Carbon Mapper [24], and CO2 Image [25], aim to provide new opportunities for monitoring emissions at individual power plants. The launch of the first two satellites for the CO2M constellation is scheduled for 2026 with a planned operation of at least seven years. Each CO2M satellite will cover fifty times more land than OCO-2 or -3 and include an NO\({}_{2}\) imaging spectrometer to identify the source of the CO\({}_{2}\) emissions since NO\({}_{2}\) and CO\({}_{2}\) are co-emitted from industrial sources [8; 21; 22]. These future satellite missions will provide valuable information for the global stocktake and emission reduction goals. However, these satellites will lack the ability to track progress prior to their deployment, for instance, quantifying the effectiveness of policy changes since the Paris Agreement. Furthermore, despite the sensor- and orbit-specific differences of these studies, all methods that use satellite-retrieved XCO\({}_{2}\) face a similar set of challenges. First, elevated CO\({}_{2}\) concentrations at or near power plants are affected by the background concentration, which can be further impacted by the effect of wind and the spatial sampling from GHG monitoring satellites [21; 26]. Combined, these two factors can impact CO\({}_{2}\) emission retrievals and increase uncertainty. Additionally, the sensitivity of XCO\({}_{2}\)-based retrieval techniques limits their ability to detect relatively low rates of anthropogenic emissions, such as those from lower-capacity power plants. Second, resolving emissions from closely adjacent sources is problematic without high spatial resolution. Third, the variability of emissions from some industrial sources can be quite high, requiring high revisit rates to observe patterns. High-spatial-resolution (30 m or less) multi-spectral imagery is another potential route to monitoring GHG emissions from individual sources.