Using measurements of the column-averaged CO_2 dry air mole fraction (XCO_2) 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 XCO_2. A unique global land mapping dataset of XCO_2 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 XCO_2 retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO_2 and NDVI, and implement correlation analyses of seasonal variation between XCO_2 and the vegetation parameters. As a result, the spatial distribution of XCO_2 SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO_2 from CarbonTracker. The spatial pattern of XCO_2 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 XCO_2 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 XCO_2 and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO_2 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 CO_2 columns
