A Step Forward to Global Segment CO₂ Flux Estimation Benefiting from Large Swath of Coordinated CO₂ and Solar-Induced Fluorescence Measurements from the TanSat-2 Mission

To improve understanding of the global carbon cycle, China is preparing to launch the next-generation carbon monitoring satellite, TanSat-2, which will collect column measurements of carbon dioxide (CO₂) and methane (CH₄). We investigate the potential of TanSat-2 observations for assessing anthropogenic carbon emissions and ecosystem carbon sinks. To achieve this objective, we introduce a new carbon flux estimation method that combines atmospheric measurements of CO₂ and solar-induced fluorescence (SIF) to jointly constrain net primary productivity (NPP) and fossil fuel combustion (FF). We apply EOF (empirical orthogonal function) analysis to the NPP and FF inventories to identify dominant spatiotemporal patterns to reduce the size of the state vector and to help disaggregate the natural and FF sources of CO₂. Using observing system simulation experiments (OSSEs)—we find that TanSat-2 CO₂ and SIF observations lead to an NPP error reduction of up to 95% over Siberia and the Amazon, and to error reductions of about 80% for FF emissions over Siberia, North Aisa, the United States, and South Africa, if measurement bias could be eliminated. However, our OSSEs show that the presence of even a small XCO₂ bias can cause serious distortion or misallocation of CO₂ sources and sinks. Therefore, systematic observation bias needs to be properly addressed in retrieval and data application. We also explore the importance of adopting a large across-track swath in delivering robust CO₂ flux estimates and develop an error matrix tool that could be used in future mission assessment.