Anthropogenic greenhouse gas emissions represented by carbon dioxide and methane are an important driving force of global warming in the last century. The key to controlling global warming is to control greenhouse gas emissions. Carbon dioxide is an important greenhouse gas. Research and development of scientific carbon dioxide emission monitoring technology and scientific identification of regional carbon dioxide emission and absorption are of great significance to serving our country's carbon emission policies at different stages.

Traditional inventory methods calculate the total carbon emissions by counting the energy consumed by each emission source. Since statistics and emission factors cannot be updated quickly, it is difficult for these methods to capture the dynamic changes in emission sources. Flux data based on concentration measurement is updated frequently, and the measurement data is objective, which can provide a more accurate basis for greenhouse gas emissions and traceability. In recent decades, various methods have been proposed to measure fluxes based on concentration measurement. The measurement methods for the terrestrial biosphere flux include the chamber method, micrometeorology method, equilibrium boundary layer concepts, and inverse system for space-borne platforms. The measurement methods for the point sources flux contain the inverse diffusion technology represented by the Gaussian plume model, source pixel method, cross-sectional flux method, integrated mass enhancement method, Gaussian vector integral method, and horizontal net flux measurement method.

The ground-based in-situ measurement technology represented by the flux tower features high measurement accuracy and strong time continuity and plays a vital role in the flux detection of forests, farmland, and other earth ecosystems. However, since the measurement area of the flux tower usually does not exceed 1 km², it is difficult to quantitatively understand the sources and sinks of greenhouse gases on a global scale due to the sparseness of the sites and limited representation distance.

Satellite remote sensing data can obtain the global spatial distribution and changes of greenhouse gases with fast inversion speed, which can make up for the shortage of ground base stations. At present, satellite remote sensing can detect greenhouse gas emissions from the earth's ecosystems and point sources. Many countries and teams have inverted the greenhouse gas fluxes of global ecosystems based on satellite remote sensing. In 2021, the Chinese Institute estimated the global CO₂ flux distribution based on the TanSat satellite, and the results are in good agreement with other satellites such as Japan's GOSAT and the US's OCO-2. There are also a number of studies that capture CO₂ fluxes from terrestrial power plants through satellite measurements. However, satellite remote sensing still has many limitations in accuracy, resolution, and data coverage. The atmospheric chemical transport model can simulate the three-dimensional gas concentration field of the atmosphere, but due to the incomplete transport model and meteorological field, and uncertain initial field and emission sources, the obtained three-dimensional distribution of gas concentration deviates from the actual situation, and it needs to be combined with the actual situation. The observed data is further corrected to improve accuracy.

Lidar technology is characterized by long detection distance, high spatial-temporal resolution, and all-day detection. Active remote sensing is an important direction for the development of satellite remote sensing. However, current satellite active remote sensing is mainly based on the path integration technology IPDA and can only obtain the concentration of the entire atmospheric column, thus making it difficult to accurately invert the vertical distribution of point source emissions and affecting the inversion effect of point source emission flux. The ground-based differential absorption lidar DIAL can obtain distance-resolved gas concentration distribution and simultaneously detect atmospheric wind field data with high precision. Although its coverage is not as good as that of satellites, it is wider than that of a single station. It is an effective means for local area gas flux monitoring.

Although the measurement methods of greenhouse gas fluxes are becoming increasingly more abundant, the spatial-temporal resolution, data coverage, and measurement accuracy of the existing methods for the concentration distribution and emission flux of greenhouse gases are still very limited. In the future, greenhouse gas flux measurement technology can be further developed in several directions. Measurement data from satellites, and ground-based and airborne platforms is assimilated to obtain higher-precision three-dimensional distribution of greenhouse gas fluxes, the mechanism of greenhouse gas sources and sinks is analyzed, and natural and anthropogenic carbon emissions are identified. In addition, we develop a greenhouse gas assimilation forecast system and build accurate greenhouse gas source-sink models and inversion models at different scales. As a result, global high spatial-temporal resolution remote sensing is realized through satellite networking to form a global quality-uniform and continuous greenhouse gas observation dataset, and observe the greenhouse gas concentration and spatial-temporal changes of sources and sinks in an all-round way. Collaborative monitoring technologies for greenhouse gases and pollutants are also developed.