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Observations of atmospheric carbon dioxide (CO) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO can be analyzed and modeled by geostatistical methods, and CO values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO, and does not consider temporal variation in the satellite-observed CO data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO data sets in China. Prediction of monthly CO using the spatiotemporal variogram model and space-time kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient ( r), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO than those from the spatial-only approach. [ABSTRACT FROM AUTHOR]

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale. [ABSTRACT FROM AUTHOR]