Air-sea CO2 flux
Net air-sea CO2 flux

Net air-sea CO2 flux is usually used to characterize whether the ocean absorbs or releases CO2. Air-sea CO2 flux is calculated using the product of CO2 partial pressure difference between seawater and atmosphere and the CO2 exchange rate at the sea-air interface. The same calculation formula used in field observations (including section, buoy, and navigational monitoring) is used in net CO2 flux estimates based on satellite remote sensing; however, the calculated parameter data are mainly satellite-derived or model-simulated products. For example, atmospheric CO2 concentrations can be obtained from global CO2 background station data or atmospheric circulation model-simulated CO2 concentration data, or from satellite data. NASA, ESA, and JAXA have launched CO2 monitoring satellites (NASA: OCO, OCO-2; ESA: SCIAMACHY, CarbonSat; JAXA: GOSAT), and China also successfully launched its carbon monitoring satellite in December 2016. The CO2 exchange rate at the sea-air interface is usually expressed as a function of wind speed and wave height and can be calculated using satellite-derived wind speed and effective wave height products. In addition, the partial pressure of seawater CO2 (pCO2) is closely related to the biogeochemical environment of the water body and spatiotemporal variation is complex, making it more difficult to estimate sea-air CO2 flux using satellite remote sensing.

Remote sensing of seawater partial pressure of CO2 (pCO2)

Seawater pCO2 refers to the partial pressure of CO2 in a gas-liquid equilibrium state. It is a form of carbon in a seawater carbonate system controlled by the chemical equilibrium of CO3-2, HCO3-1, and H.
As seawater pCO2 cannot be directly obtained by satellite-measured radiance, it is necessary to use proxy parameters for characterization. Most seawater pCO2 remote sensing inversion algorithms are based on linear or multiple regression relationships between pCO2 and satellite-derived parameters such as temperature and chlorophyll. Researchers have also used more sophisticated mathematical methods to build statistical models of pCO2. These statistical algorithms have achieved good results in specific study areas, but the applicability of these algorithms depends on the season, regional representation, and the number of sample observations. In addition, it is difficult to obtain a significant statistical model in complex marginal sea areas.
Another way to estimate seawater pCO2 by satellite remote sensing is based on a control mechanism analysis method. Bai et al. (2015) proposed a semi-analytical algorithm model of seawater pCO2 based on control mechanism analysis (“Mechanistic-based Semi-Analytic-Algorithm” (MeSAA-pCO2). The main concept of the MeSAA-pCO2 model is that total pCO2 variability can be described as the algebraic sum of pCO2 variability caused by each major control process, such as thermodynamics, mixing of different carbonates, bioactivity, and CO2 flux at the air-sea interface. In this inversion process, establishing the analytical or semi-analytical quantitative model of each control factor (mechanism) based on satellite-derived products is the key. To date, the MeSAA-pCO2 model has been successfully used to estimate seawater pCO2 in the East China Sea (Bai et al., 2015), which is dominated by Yangtze River diluted water, and in the Bering Sea (Song et al., 2016). Compared with other algorithms, the MeSAA-pCO2 model not only considers the contribution of terrestrial matter, but also applies the same model for the whole sea area, overcomes the plaque problem, and can be extended to different marginal sea systems.

Representative articles
Bai, Y., Cai, W., He, X., Zhai, W., Pan, D., Dai, M., & Yu, P. (2015). A mechanistic semi‐analytical method for remotely sensing sea surface pCO2 in river‐dominated coastal oceans: A case study from the East China Sea. Journal of Geophysical Research, 120(3), 2331-2349.
Chen, C. T. A., Huang, T. H., Chen, Y. C., Bai, Y., He, X., & Kang, Y. (2013). Air-sea exchanges of CO2 in the world's coastal seas. Biogeosciences,10,10(2013-10-15), 10(10), 6509-6544.
Chen, C. T. A., Huang, T. H., Fu, Y. H., Bai, Y., & He, X. (2012). Strong sources of CO₂ in upper estuaries become sinks of CO₂ in large river plumes. Current Opinion in Environmental Sustainability, 4(2), 179-185.
Song, X., Bai, Y., Cai, W. J., Chen, C. T., Pan, D., He, X., & Zhu, Q. (2015). Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA). Remote Sensing, 8(7), 558.

Le, C. ,  Gao, Y. ,  Cai, W. J. ,  Lehrter, J. C. ,  Bai, Y. , &  Jiang, Z. P. . (2019). Estimating summer sea surface pco2 on a river-dominated continental shelf using a satellite-based semi-mechanistic model. Remote Sensing of Environment, 225, 115-126.
Yu, T., Pan, D., Bai, Y., He, Y., Li, D., & Liang, C. (2016). A quantitative evaluation of the factors influencing the air-sea carbon dioxide transfer velocity. Acta Oceanologica Sinica, 35(11), 68-78.
LÜ Hang-yu, BAI Yan, LI Qian, et al. Satellite remote sensing retrieval of aquatic pCO2 in summer in the Pearl River Estuary. Journal of Marine Sciences,2018,36(2):1-11, doi:10.3969/j.issn.1001-909X.2018.02.001.(In Chinese)