Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Here, we estimate CO2 emission rates from a small number of collocated observations from the Orbiting Carbon Observatory-3 (OCO-3) and the Earth Surface Mineral Dust Source Investigation (EMIT), both onboard the International Space Station (ISS). These near-simultaneous measurements allow for an unprecedented comparison of two unique space-based CO2 sensors over both isolated coal-fired power plants and multi-source scenes in China. We estimate CO2 emission rates using integrated mass enhancement and a Gaussian plume model. Where validation data is available, 15 of the 19 estimated emission rates have errors less than 37%. For the multi-source scenes, EMIT can estimate emissions from individual facilities but its aggregate emissions are 33% lower than OCO-3, likely because it cannot detect small sources or diffuse emissions. OCO-3, with its excellent precision, may better constrain CO2 emissions over the entire scene.

NASA’s Orbiting Carbon Observatory-2 (OCO-2) has the goal of accurately measuring column-averaged dry-air mole fractions of carbon dioxide (XCO2). In order to fit the measured radiances, many parameters besides CO2 are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational XCO2 retrieval algorithm (V11) solves for a multiplicative scaling factor on an a priori water vapor profile and an additive offset on an a priori temperature profile. However, simulations have indicated that water vapor and temperature each have 1.5-3 degrees of freedom in the vertical column. This means that the retrieval is limited in its ability to fit the true profiles of temperature and water vapor. Here, we use singular value decomposition (SVD) to determine the three most explanatory profile ”shapes” of water vapor and temperature error, then retrieve a single scaling factor applied to each shape. We assess retrieval errors by comparing to the Total Carbon Column Observing Network (TCCON) and a collection of CO2 models. We find that after applying quality filtering using Data Ordering Genetic Optimization (DOGO) and a custom bias correction, the scatter of the XCO2 error versus TCCON is reduced from 1.02 to 1.01 ppm (2.3% reduction in variance) for land glint observations, 1.04 to 0.96 ppm (14.5% reduction in variance) for land nadir observations, and 0.68 to 0.66 ppm (4.7% reduction in variance) for ocean glint observations. We also see a small improvement in the agreement between OCO-2 XCO2 and CO2 models over oceans and the Amazon.