研究目的
Investigating the inconsistency of the spatio-temporal variations of XCO2 retrievals from SCIAMACHY, GOSAT, and OCO-2 at a global scale to support their integrating application in long-term analysis of atmospheric CO2 variation.
研究成果
The generated long-term mapping XCO2 dataset reveals smooth temporal variations and similar correlation patterns with NDVI across sensors, indicating consistency in spatio-temporal characteristics and potential for applications in carbon cycle studies, though local inconsistencies require further analysis.
研究不足
Inconsistencies in XCO2 retrievals due to different sensor sensitivities, observation modes, and algorithms may affect accuracy; the study highlights the need for attention to local-scale inconsistencies and further investigation into anomalies like the 2009-2010 El Ni?o phenomenon.
1：Experimental Design and Method Selection:
The study uses a data-driven spatio-temporal geostatistics method to generate a global land mapping XCO2 dataset. It integrates XCO2 retrievals from multiple satellites using a common a priori profile and accounts for averaging kernel effects, then uniformizes the data to common spatial and temporal resolutions for correlation analysis with NDVI using Pearson's squared correlation coefficient.
2：Sample Selection and Data Sources:
XCO2 data from SCIAMACHY (BESD algorithm v02.01.01, Jan 2004-May 2009), GOSAT (ACOS XCO2 v3.5lite, Jun 2006-Jun 2014 and NIES XCO2 v02.31, Jul 2014-Aug 2014), and OCO-2 (OCO2_r7_Lite, Sep 2014-Dec 2016). NDVI data from MOD09 at 0.05 degree resolution for the same period.
3：01, Jan 2004-May 2009), GOSAT (ACOS XCO2 v5lite, Jun 2006-Jun 2014 and NIES XCO2 v31, Jul 2014-Aug 2014), and OCO-2 (OCO2_r7_Lite, Sep 2014-Dec 2016). NDVI data from MOD09 at 05 degree resolution for the same period.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Satellites and sensors: SCIAMACHY, GOSAT, OCO-2; data processing software and algorithms for geostatistical mapping and correlation analysis.
4：Experimental Procedures and Operational Workflow:
Data integration and uniformization to 1°×1° grid and 8-day intervals, followed by spatio-temporal mapping and correlation computation with NDVI.
5：Data Analysis Methods:
Pearson's squared correlation coefficient to quantify relationships between XCO2 and NDVI, and visual comparison of spatial patterns and temporal trends.
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