Carbon dioxide (CO₂) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO₂ concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO₂ concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO₂ concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO₂ concentrations on a global scale. We assumed that CO₂ concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R²) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R² was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO₂ concentration (XCO₂), we found that the accuracy throughout the World is between −2.56~3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.

Measurements of land-cover changes suggest that such shifts may alter atmospheric concentrations of greenhouse gases (GHGs). However, owing to the lack of large-scale GHG data, a quantitative description of the relationships between land-cover changes and GHG concentrations does not exist on a regional scale. The Greenhouse Gases Observing Satellite (GOSAT) launched by Japan on 23 January 2009 can be of use in investigating this issue. In this study, we first calculated the monthly average GHG concentrations in East Asia from April 2009 to October 2011 and found that CO₂ concentration displays a seasonal cycle, but that the CH₄ seasonal trend is unclear. To understand the relationship between land cover and GHG concentrations, we used GHG data from GOSAT, normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and land-cover data from EAS-GlobCover (2009) to analyse the correlation coefficients between land cover and GHG concentrations. We observed that vegetation may generally be considered as a source of, but not a sink for, CO₂ and CH₄, either on a yearly scale or during the growing season. With respect to the relationships between land-cover types and GHG concentrations, we conclude that on a yearly scale, land-cover types are not closely correlated with GHG concentrations. During the growing season, croplands and scrublands are negatively correlated with XCO₂ (the ratio of the total number of CO₂ molecules to that of dry air molecules), and forest, grasslands and bare areas are positively correlated with XCO₂. Forest and croplands can be viewed as CH₄ sources, while scrublands and grasslands can be thought of as CH₄ sinks.

In Asia, sand dust storms (SDSs) occur nearly every year, especially in northern China. However, there is little research about the relationship between SDSs and greenhouse gases (GHGs). In this article, we selected four SDSs that occurred in Asia in the spring of 2009 and 2010. We monitored the areas covered by these SDSs using Moderate Resolution Imaging Spectroradiometer (MODIS) data, then we used Greenhouse Gases Observing Satellite (GOSAT) data to check how the SDSs affected the concentrations of CO₂ and CH₄. We then compared the concentrations of CO₂ and CH₄ on SDS days with the monthly mean values of the months in which SDSs occurred. We also compared the concentrations of CO₂ and CH₄ on SDS days with the values before and after the SDSs. After analysis, we found that SDSs had increased the concentrations of CO₂ and CH₄ in the atmosphere. When the SDSs occurred, the concentrations of CO₂ and CH₄ increased and reached peak values on the last or penultimate days of the storms and then decreased to their normal values. Atmospheric flow is the main reason for the increase in concentration of CO₂, and the lack of the free radical (OH) during SDSs and the presence of CH₄ sources in southeast China are the main reasons for the increase in concentration of CH₄. We also found that in arid and semi-arid areas, SDSs had little effect on the concentration of these two GHGs.