Using measurements of the column-averaged CO₂ dry air mole fraction (XCO₂) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO₂. A unique global land mapping dataset of XCO₂ with a resolution of 1° by 1° in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO₂ retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO₂ and NDVI, and implement correlation analyses of seasonal variation between XCO₂ and the vegetation parameters. As a result, the spatial distribution of XCO₂ SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO₂ from CarbonTracker. The spatial pattern of XCO₂ SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 ± 0.49 ppm on average) and Central Africa (with SCA of 8.33 ± 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere–atmosphere interactions at local scales. Results from correlation analyses between XCO₂ and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO₂ and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO₂ compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere–atmosphere interactions on the seasonal cycle pattern of atmospheric CO₂ columns. Full article

The aim of this study is to evaluate the Greenhouse gases Observation SATellite (GOSAT) column-averaged CH₄ dry air mole fraction (XCH₄) data by using geostatistical analysis and conducting comparisons with model simulations and surface emissions. Firstly, we propose the use of a data-driven mapping approach based on spatio-temporal geostatistics to generate a regular and gridded mapping dataset of XCH₄ over Monsoon Asia using five years of XCH₄ retrievals by GOSAT from June 2009 to May 2014. The prediction accuracy of the mapping approach is assessed by using cross-validation, which results in a significantly high correlation of 0.91 and a small mean absolute prediction error of 8.77 ppb between the observed dataset and the prediction dataset. Secondly, with the mapping data, we investigate the spatial and temporal variations of XCH₄ over Monsoon Asia and compare the results with previous studies on ground and other satellite observations. Thirdly, we compare the mapping XCH₄ with model simulations from CarbonTracker-CH₄ and find their spatial patterns very consistent, but GOSAT observations are more able to capture the local variability of XCH₄. Finally, by correlating the mapping data with surface emission inventory, we find the geographical distribution of high CH₄ values correspond well with strong emissions as indicated in the inventory map. Over the five-year period, the two datasets show a significant high correlation coefficient (0.80), indicating the dominant role of surface emissions in determining the distribution of XCH₄ concentration in this region and suggesting a promising statistical way of constraining surface CH₄ sources and sinks, which is simple and easy to implement using satellite observations over a long term period. Full article

Satellite observations can be used to detect the changes of CO₂ concentration at global and regional scales. With the column-averaged CO₂ dry-air mole fraction (Xco₂) data derived from satellite observations, the issue is how to extract and assess these changes, which are related to anthropogenic emissions and biosphere absorptions. We propose a k-means cluster analysis to extract the temporally changing features of Xco₂ in the Central-Eastern Asia using the data from 2009 to 2013 obtained by Greenhouse Gases Observing Satellite (GOSAT), and assess the effects of anthropogenic emissions and biosphere absorptions on CO₂ changes combining with the data of emission and vegetation net primary production (NPP). As a result, 14 clusters, which are 14 types of Xco₂ seasonal changing patterns, are obtained in the study area by using the optimal clustering parameters. These clusters are generally in agreement with the spatial pattern of underlying anthropogenic emissions and vegetation absorptions. According to correlation analysis with emission and NPP, these 14 clusters are divided into three groups: strong emission, strong absorption, and a tendency of balancing between emission and absorption. The proposed clustering approach in this study provides us with a potential way to better understand how the seasonal changes of CO₂ concentration depend on underlying anthropogenic emissions and vegetation absorptions. Full article