## 5 Conclusions  
The seasonal cycle of atmospheric CO\({}_{2}\) is mostly caused by CO\({}_{2}\) uptake and release from biosphere activity, which is profoundly connected to biosphere fluxes that determine the global net CO\({}_{2}\) sink. Satellite observations of XCO\({}_{2}\) by GOSAT, correlated with biosphere parameters from MODIS, not only expand the current in situ measurements tremendously, but also provide us with multi-parameter data of biosphere-atmosphere interaction to reveal the impacts of biosphere activity on the seasonal cycle pattern of XCO\({}_{2}\). The data-driven analysis of atmospheric CO\({}_{2}\) coupled with biosphere parameters has the significant potential to improve our understanding of the global net CO\({}_{2}\) sink with massive increases of satellite data. In this study, we introduced a data-driven approach to assess biosphere-atmosphere interaction on the seasonal change of XCO\({}_{2}\) through collecting the global data, including XCO\({}_{2}\) from GOSAT, vegetation parameters of NDVI, EVI, LAI, and GPP, and land surface temperature from MODIS from the years 2009-2014. The global land mapping dataset of XCO\({}_{2}\) (GM-XCO\({}_{2}\)), derived from GOSAT XCO\({}_{2}\) retrievals, can reveal the spatial distribution of seasonal cycle characteristics of XCO\({}_{2}\) directly and at a global scale that were not involved before. Furthermore, GM-XCO\({}_{2}\) makes it possible to implement the correlation analyses, at fine scale, of seasonal variation between satellite observed XCO\({}_{2}\) and terrestrial biosphere parameters. We found that the seasonal cycle pattern of GM-XCO\({}_{2}\) is well corresponding to that from NDVI with high correlation coefficient of 0.69 for global land and 0.74 for the NH land. That indicate the significant influence from vegetation activities on the seasonal change of XCO\({}_{2}\). Through the correlation analyses between GM-XCO\({}_{2}\) and NDVI, EVI, LAI, and GPP, it is found that NDVI and EVI have better correlations and spatial pattern with the seasonal variation of XCO\({}_{2}\) than LAI and GPP for stronger coefficients over all latitude zones. That is consistent with previous studies [(78; 79)] that NDVI and EVI, directly retrieved from MODIS observed radiance, may be better parameters representing biosphere activities for constraining of the terrestrial CO\({}_{2}\) flux than LAI and GPP, which are obtained from semi-empirical biophysical models. Using XCO\({}_{2}\) and NDVI, moreover, we show the global distribution of time delay effect in terms of lags of months, which, to our knowledge, is the first such map derived from the completely data-driven approach using satellite observations. After removing the phase lags, PCC enhanced nearly \(-0.2\) for most areas, especially for North America, where those were improved from weaker than \(-0.6\) to stronger than \(-0.8\). As a result, the phase lags between seasonal change of XCO\({}_{2}\) and the vegetation indices should be considered when assessing the impact of biosphere-atmosphere interaction on the seasonal cycle pattern of XCO\({}_{2}\). Considering the uncertainty of the GM-XCO\({}_{2}\), evaluation with cross-validation, comparisons using TCCON measurements and CT-XCO\({}_{2}\) was conducted. The method presents a significant high R\({}^{2}\) of 0.91 between the predictions and observations in cross-validation. Results of evaluation with TCCON data demonstrate the sufficient accuracy of mapping XCO\({}_{2}\) with averaged absolute bias within 2 ppm over all selected 12 TCCON sites, and the similar SCA distribution between GM-XCO\({}_{2}\) and CT-XCO\({}_{2}\) indicating the availability of GM-XCO\({}_{2}\) in capturing the seasonal change of atmospheric CO\({}_{2}\) concentration, even though some discrepancy exists in the SCA estimation between GM-XCO\({}_{2}\) and CT-XCO\({}_{2}\) over low-latitude areas.