Synergistic Multi-instrument Remote Sensing for Greenhouse Gas Monitoring

Greenhouse gases (CO₂ and CH₄) are key drivers of climate change, and the accuracy of their emission inventories directly determines the credibility of carbon-peaking and carbon-neutralization pathways. Traditional bottom-up methods suffer from coarse spatiotemporal resolution and often miss abrupt releases, urgently calling for multi-scale, high-timeliness observation-inversion systems. Focusing on CO₂ and CH₄, this study: (1) builds a payload-level end-to-end simulation platform for the DQ-2 wide-swath imager and BK-1 high-resolution point-source monitoring satellites to evaluate their capability to detect greenhouse-gas emission hotspots; (2) employs large-eddy simulation to generate high-fidelity plume scenarios over key regions and tests satellite monitoring performance; and (3) combines multi-sensor international data (TROPOMI, EMIT, etc.) with a Gaussian plume inversion model to estimate point-source emissions and compare them with inventory data. The results demonstrate that multi-sensor, multi-scale synergy can significantly reduce facility-level emission biases, providing timely and accurate emission information for China’s carbon-peaking actions.