A research team led by Dr. SHI Hailiang at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a novel infrared imaging payload and AI-based retrieval framework capable of detecting carbon dioxide (CO₂) and methane (CH₄) emissions from space at a spatial resolution of approximately 100 meters. 

The system addresses key challenges in complex surface backgrounds and weak emission plume segmentation, enabling high-accuracy identification and quantification of point source greenhouse gas emissions.

The results were published in International Journal of Applied Earth Observation and Geoinformation.

In response to the growing need for precise carbon emission inventories and verification, the researchers designed the "Hotspot Greenhouse Gas Monitoring Instrument" for an upcoming satellite mission. The payload features advanced imaging capabilities for CO₂ and CH₄ and represents a significant advancement in domestic high-resolution space-based GHG detection technology.

To overcome the difficulties caused by heterogeneous surface reflectance and high background variability, which often leading to false positives or missed detections, they proposed a Heterogeneous Surface Background Regulation Strategy, which utilizes multi-channel spectral fusion to significantly enhance detection performance over urban, desert, and high-aerosol regions. 

To accurately segment plumes, the researchers developed two innovative methods: FSDINet, a dual-domain network architecture that integrates spectral features in both frequency and spatial domains for joint global-local modeling, and kMetha-Mamba, a hybrid approach that combines spectral derivative-based clustering with state-space modeling to improve weak plume detection and noise suppression. 

This work lays a solid technical foundation for the data processing and emission retrieval applications of next-generation imaging carbon satellites in China. It marks a key step forward in high-resolution satellite-based monitoring of greenhouse gas emissions and contributes an innovative solution to global carbon tracking and scientific mitigation efforts.

Comparison of detection performance before and after signal interference suppression under different surface background conditions. (Image by WU Shichao)