Column-averaged dry air mole fraction of atmospheric CO₂ (XCO₂), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO₂ concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO₂ from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO₂. The approach integrated XCO₂ from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO₂ (GM-XCO₂) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO₂ precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R² of 0.97 from cross-validation. GM-XCO₂ showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO₂ or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO₂ product may be also used in different carbon cycle research applications with different precision requirements.

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO₂ emissions. Quantifying anthropogenic CO₂ emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO₂ concentration. In this study, we propose an approach for estimating anthropogenic CO₂ emissions by an artificial neural network using column-average dry air mole fraction of CO₂ (XCO₂) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO₂ anomalies (dXCO₂) derived from XCO₂ and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO₂ in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO₂ emissions especially in the areas with high anthropogenic CO₂ emissions. Our results indicate that XCO₂ data from satellite observations can be applied in estimating anthropogenic CO₂ emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO₂ uptake and emissions, from satellite observations.

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-XCH_4, 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.

Atmospheric CO₂ concentrations are sensitive to the effects of climate extremes on carbon sources and sinks of the land biosphere. Therefore, extreme changes of atmospheric CO₂ can be used to identify anomalous sources and sinks of carbon. In this study, we develop a spatiotemporal extreme change detection method for atmospheric CO₂ concentrations using column-averaged CO₂ dry air mole fraction (XCO₂) retrieved from the Greenhouse gases Observing SATellite (GOSAT) from 1 June 2009 to 31 May 2016. For extreme events identified, we attributed the main drivers using surface environmental parameters, including surface skin temperature, self-calibrating Palmer drought severity index, burned area, and gross primary production (GPP). We also tested the sensitivity of XCO₂ response to changing surface CO₂ fluxes using model simulations and Goddard Earth Observing System (GEOS)-Chem atmospheric transport. Several extreme high XCO₂ events are detected around mid-2010 over Eurasia and in early 2016 in the tropics. The magnitudes of extreme XCO₂ increases are around 1.5–1.8 ppm in the Northern Hemisphere and 1.2–1.4 ppm in Southern Hemisphere. The spatiotemporal pattern of detected high XCO₂ events are similar to patterns of local surface environmental parameter extremes. The extreme high XCO₂ events often occurred during periods of increased temperature, severe drought, increased wildfire or reduced GPP. Our sensitivity tests show that the magnitude of detectable anomalies varies with location, for example 25% or larger anomalies in local CO₂ emission fluxes are detectable in tropical forest, whereas anomalies must be half again as large in mid-latitudes (~37.5%). In conclusion, we present a method for extreme high XCO₂ detection, and large changes in land CO₂ fluxes. This provides another tool to monitor large-scale changes in the land carbon sink and potential feedbacks on the climate system.

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.

Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO₂ concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO₂ dry air mole fraction (XCO₂) from January 2003 to May 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO₂ changes during growing season for 13 years. We derived a relationship between XCO₂ and surface net CO₂ fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO₂ flux enhancement based on satellite observed XCO₂ changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO₂ emissions, we estimated an enhanced XCO₂ drawdown during the GP of −0.070 to −0.084 ppm yr⁻¹. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2–3% of EC’s annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO₂ to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord.