Machine Learning Model-Based Estimation of XCO2 with High Spatiotemporal Resolution in China

Abstract

As the most abundant greenhouse gas in the atmosphere, CO₂ has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO₂ is of great significance in climate research. However, existing CO₂ monitoring methods have great limitations, and it is difficult to obtain large-scale monitoring data with high spatial resolution, thus limiting the effective monitoring of carbon sources and sinks. To obtain complete Chinese daily-scale CO₂ information, we used OCO-2 XCO₂ data, Carbon Tracker XCO₂ data, and multivariate geographic data to build a model training data set, which was then combined with various machine learning models including Random Forest, Extreme Random Forest, XGBoost, LightGBM, and CatBoost. The results indicated that the Random Forest model presented the best performance, with a cross-validation R² of 0.878 and RMSE of 1.123 ppm. According to the final estimation results, in terms of spatial distribution, the highest multi-year average RF XCO₂ value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In terms of time, from 2016 to 2018, the annual XCO₂ in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal effects, the multi-year average XCO₂ was highest in spring (407.76 ± 1.72 ppm) and lowest in summer (403.15 ± 3.36ppm). Compared with the Carbon-Tracker data, the XCO₂ data set constructed in this study showed more detailed spatial changes, thus, can be effectively used to identify potentially important carbon sources and sinks.

1. Introduction

Atmospheric carbon dioxide (CO₂) is the most important greenhouse gas. Due to the disturbance of human activities, its concentration has increased from about 280 ppmv before the industrial revolution to 414 ppmv. At the same time, due to the emission of greenhouse gases, the average global temperature has risen by about 1.09 °C over the past 100 years, which has caused irreversible damage and impacted the global ecological environment [1,2]. The knock-on effects between ecosystems are huge and often inestimable. The international community has attached great importance to the issue of climate change. Many countries have successively signed the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. China has also proposed carbon peaking and carbon neutrality goals. How to accurately monitor carbon sources and sinks, reduce global CO₂ emissions, and consequently reduce the greenhouse effect are currently major concerns worldwide.

Traditional CO₂ observation methods rely on ground-based observations at ground stations, which have high precision and are continuous on the time scale. However, due to the low number and uneven regional distribution of monitoring stations, in addition to the fact that most of them are distributed in developed countries and densely populated areas [3], it is often difficult to obtain effective large-scale monitoring data, especially in regions, such as the oceans, polar regions, and deserts [4]. This leads to greater uncertainty in research on the temporal and spatial distribution and size of carbon sources and sinks. In 2002, the first global CO₂ concentration observation map based on the Scanning Imaging Absorption Spectrometer for Atmospheric Mapping (SCIAMACHY) was successfully constructed [5]. Technology using passive satellite remote sensing to detect CO₂ by receiving information in the near-infrared band of the sun has developed rapidly, providing some of the most potent methods for monitoring the global distribution of greenhouse gases with high temporal and spatial resolution. Through remote sensing, some defects of the “bottom-up” model simulation method can be avoided, especially the huge uncertainty in CO₂ estimation due to the differences in ground emission inventory surveys [6,7,8]. The originally designed satellites were not dedicated to atmospheric CO₂ monitoring tasks. Although they can achieve continuous observation in time and space, they only have low observation resolution; for example, the ENVISAT and METOP-A satellites have observation footprints of 30 × 60 km and 50 × 50 km, respectively. With the emergence of dedicated carbon satellites, the CO₂ observation footprint and accuracy have been greatly improved, and satellite observations have shown good consistency with the ground-based Total Carbon Column Observation Network (TCCON) [9]. However, the scanning pattern of carbon satellites results in the sparse distribution of observation records, such as those obtained by China’s TANSAT, Japan’s GOSAT, and the United States OCO-2 satellites [10,11], all of which face the problem of discontinuous observations in time and space. As such, the current high temporal-spatial resolution continuous CO₂ concentration monitoring capability is still insufficient at both regional and global scales. Rough observation spatial resolution or more significant data missing problems limit the application of relevant CO₂ observation products in some aspects, such as terrestrial ecosystem carbon cycle monitoring, “carbon pollution from the same source” pollution traceability, assimilation of model output results, and accurate estimation of carbon sources and sinks.

Fortunately, the rich information obtained by multi-source remote sensing enables a series of feasible methods for producing CO₂ data with fine spatial resolution and continuity in time and space. On the one hand, from the perspective of multi-source CO₂ observation satellites, CO₂ reconstruction methods based on data fusion have been developed. For example, Hai Nguyen [12] has used the data fusion method of dimensionality reduction Kalman smoothing and the Spatial Random Effects model to realize CO₂ observation data fusion between GOSAT, AIRS, and OCO-2. Although the data fusion method can reduce the differences in CO₂ observations by different satellites to a certain extent, it is still unable to reconstruct the continuous spatial distribution of CO₂, largely due to the insufficient information on CO₂ observed by satellites. On the other hand, geostatistical technology, as a common method for completing spatial information, has also been applied to the spatial completion and refinement of CO₂ information. A large number of studies have shown that using CO₂ footprints from satellite observations, combined with ordinary Kriging interpolation [13], space-time Kriging interpolation [14], sliding window Kriging interpolation [15], and other methods, allows for the production of a fine CO₂ spatial distribution. However, as geostatistical methods require a large number of temporally and spatially similar input samples, the spatial resolution of the output results must be increased at the expense of temporal resolution. At the same time, spatial interpolation is likely to smooth the spatial features of CO₂. These smoothed features can not be ignored in some applications, such as pollution source research.

In recent years, based on multi-source big data such as human activity information, atmospheric condition information, and geospatial information, regression technology has been widely used for the reconstruction of CO₂ data with high temporal and spatial resolution. With the assistance of multi-source data, even a simple multiple linear regression model (ML) can obtain a good fitting effect, with a multi-region verification coefficient of determination (R²) typically ranging between 0.57 and 0.75 [16]. However, due to the complexity of the transport process of CO₂ between terrestrial ecosystems, marine ecosystems, and the atmospheric environment, linear models face the problem of insufficient fitting ability. In order to overcome this bottleneck, many nonlinear models have been used for the reconstruction of CO₂ remote sensing data, which have been richly developed in recent years. Siabi [17] has used the multi-layer perceptron (MLP) model to construct the nonlinear correspondence between the XCO₂ of the OCO-2 satellite and multi-source data, successfully filling the gaps in satellite observations. Furthermore, the XGBoost model constructed by I. A. Girach [18] and the CO₂ reconstruction model based on LightGBM constructed by He [19] has achieved good objective fitting accuracy. Based on the Extreme Random Forest and the Random Forest models, Li [20] and Wang [21] have generated continuous spatiotemporal atmospheric CO₂ concentration data at global moderate and regional scales. Compared with the direct CO₂ satellite observation data, the reconstructed CO₂ data can achieve daily global coverage, thus having has richer application value. In a recent study, Zhang [22] combined a neural network model and the GWR model to develop a new geographically weighted neural network (GWNN) model, which can effectively capture the spatial heterogeneity of CO₂, and the model accuracy has been further improved. It can be seen that machine learning algorithms have strong applicability for CO₂ reconstruction.

Some recent studies have successfully captured the nonlinear correspondence between the XCO₂ of GOSAT and OCO-2 and multi-source data using machine learning algorithms, such as multi-layer perceptron (MLP) [17], LightGBM (LGBM) [18], and Extreme Random Forest (ERT) [19], successfully filling the gaps in the satellite observations.

To produce CO₂ data with high precision and high spatiotemporal resolution using the coarse resolution CO₂ data output by Carbon Tracker, supplemented by multi-source data (e.g., temperature, air pressure, vegetation indices, and elevation), we compared mainstream machine learning models, including random forest, extreme random forest, XGBoost, LGBM, and Catboost, in terms of reconstructing the CO₂ data observed by OCO-2, and evaluated the different characteristics of various machine learning models. At the same time, the daily value of XCO₂ in China was estimated, and the temporal and spatial distribution of CO₂ in China from 2016 to 2018 and its reasons for formation were analyzed. Our reconstructed data set is expected to facilitate applications in many regional studies of carbon sources and sinks.

2. Materials and Methods

2.1. Satellite Data

The CO₂ column concentration data used in this study were derived from the OCO-2 satellite product (OCO2_L2_Lite_FP), the first dedicated carbon observation satellite launched by the National Aeronautics and Space Administration (NASA) in July 2014 to measure the CO₂ column concentration (XCO₂), monitoring near-surface carbon sources and carbon sinks. The satellite at a local overpass time of approximately 13:30, the spatial resolution is 2.25 km × 1.29 km and its revisit period is 16 days [23]. Compared with other CO₂ observation satellites, the OCO-2 satellite data has a better spatial resolution, and its monitoring accuracy is higher [10]. The XCO₂ data used in this study were from 1 January 2016 to 31 December 2018, and, through quality screening, XCO₂ data with a quality fraction of 0 were selected and resampled to a 0.1° grid. Consequently, 108,665 records were generated and used for model training.

2.2. Supplementary Data

We used the Carbon-Tracker model CO₂ column concentration data (CT XCO₂) and multiple geographic variables to model the true XCO₂ (Table 1). Geographic variables included elevation, population density, landuse, normalized difference vegetation index (NDVI), and meteorological data. In addition, latitude and longitude were also used as model predictors.

2.2.1. Carbon-Tracker

Carbon Tracker (CT) is a CO+ measurement and modeling system developed by the National Oceanic and Atmospheric Administration (NOAA) to track CO+ sources and sinks around the world. We used daily CT2019B XCO₂_1330LST data from 1 January 2016 to 31 December 2018, which provides the global XCO₂ distribution at 13:30 local time with a spatial resolution of 3° × 2° [24].

2.2.2. Elevation

The Shuttle Radar Topography Mission (SRTM) is an 11-day international project initiated by the National Geospatial Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA) to acquire and generate near-global high-resolution land elevation products [25]. The data set used in this study was SRTM3, with a spatial resolution of 90 m.

2.2.3. Population Density

WorldPop is a global population data assessment project initiated by the University of Southampton in October 2013. This data covers population density, comprehensive population, age and gender structure, birth rate, population flow, flight connections, and so on [26]. The population density data used in this study were obtained from the WorldPop population density data set, with a spatial resolution of 1 km.

2.2.4. Land-Use and NDVI

Land-use data (MCD12Q1) and NDVI data (MOD13C1 and MYD13C1) were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite [27,28]. The spatial resolutions of the land-use and NDVI data were 500 m and 0.05°, respectively. Among them, the land-use data followed the IGBP classification standard.

2.2.5. Meteorological Data

The meteorological data were obtained from the ECMWF Fifth Generation Reanalysis (ERA5) dataset with a spatial resolution of 0.25° × 0.25°, including temperature, dew point temperature, wind speed, and atmospheric pressure [29]. The above meteorological data all comprise the data between 13:00 and 14:00, corresponding to the satellite transit time.

For data with a spatial resolution less than 0.1°, such as elevation, population density, landuse, and NDVI, we resampled it to 0.1° using the nearest neighbor method. On the other hand, the inverse distance weight interpolation method was used to interpolate coarser data to the 0.1° grid, such as ERA5 weather analysis data and CT2019B XCO₂ data.

2.3. Model Description

Compared with previous studies [16,17,18,19], we utilized a variety of machine-learning methods to model and estimate XCO₂. The machine learning methods used in this research can be divided into Bagging and Boosting algorithms, according to the integration method.

2.3.1. Models Based on Bagging Ensemble Methods

• ●. Random Forest (RF)

A Random Forest (RF) model [30] is a machine-learning algorithm that can be used for both classification and regression. In the random forest model, the decision tree is the basic unit of the model. By using the bootstrap sampling method to randomly extract samples of the same size from the total data sample multiple times, a large number of decision trees are established without any pruning. Finally, an ensemble of these decision trees is trained to compute classification or regression results. The random forest model is not sensitive to multicollinearity in the data and has the advantages of high precision, fast calculation speed, robust calculation results, and strong generalization ability.

• ●. Extreme Random Forest (ERT)

Compared with Random Forest, Extreme Random Forest [31] uses the entire data set to train a single decision tree, which ensures the utilization of training samples and can reduce the final prediction bias (Bias) to a certain extent. To ensure the structural difference between each decision tree, the extreme random tree introduces greater randomness in node division: the division threshold of each feature from the sub-data set is randomly selected, and the best division according to the specified threshold feature is chosen as the optimal partition attribute.

2.3.2. Models Based on Boosting Ensemble Methods

• ●. eXtreme Gradient Boosting (XGBoost)

eXtreme Gradient Boosting [32] is an optimized distributed gradient boosting algorithm with a faster running speed than current mainstream machine learning models. This model introduces a regularization term to control the complexity of the model in the loss function, and the modified loss function is interpreted using the two-dimensional Taylor formula. This not only overcomes the shortcoming of over-fitting in traditional gradient boosting models but also improves the accuracy and generalization ability of the model.

• ●. Light Gradient Boosting Machine (LightGBM)

Light Gradient Boosting Machine [33] is a variant of the tree-based gradient boosting algorithm, which uses a histogram algorithm to ensure that the model achieves the expected effect with less memory. In addition, LightGBM does not use the decision tree growth strategy of layer-by-layer growth but, instead, introduces a leaf-by-leaf growth strategy. In comparison, this strategy uses less memory and allows the model to converge faster.

• ●. Categorical + Boosting (CatBoost)

The Categorical + Boosting [34] model is a gradient boosting algorithm framework based on a symmetric decision tree-based learner, which consists of Categorical and Boosting models. In addition, CatBoost also solves the problems of gradient deviation and prediction offset, thereby reducing the occurrence of over-fitting and improving the accuracy and generalization ability of the algorithm.

We used the above five machine learning models, based on CT XCO₂ data and multivariate geographic data, to train different models and optimize their hyperparameters to obtain better prediction performance, followed by their comparison. Then, the optimal model was used to predict XCO₂ and generate daily full-coverage XCO₂ data.

2.4. Model Evaluation

In this study, CT XCO₂ and multiple geographical variables were used as the influencing factors of OCO₂ XCO₂, and a CO₂ column concentration regression model was constructed. We evaluated the predictive performance of different models using 10-fold sample cross-validation. For the sample-based cross-validation process, we randomly divided all the data into 10 groups of equal size. In each of the 10 rounds, 9 sets were used as training data to construct the model and the remaining set was used for predictive model evaluation.

We evaluated the model performance using the square of the correlation coefficient (R²) to determine the extent to which the model explained the variation in the observations. In addition, the Root Mean Square Error (RMSE) was used to indicate the standard deviation of residuals (prediction error), while mean bias (Bias) was used to quantify the difference between simulated and observed values.

In addition, we also utilized ground station CO₂ monitoring data to evaluate the predictive performance of the Random Forest model, including those from Waliguan (WLG) station (36.28° N, 100.90° E) and Lulin (LLN) station (23.47° N, 120.87° E). We obtained discontinuous daily CO₂ data from WLG and LLN stations and filtered out invalid data that had obvious problems in the collection or analysis process and did not meet the specific survey purpose, according to qcflag. The predicted data were evaluated by comparing ground-based observations with RF-CO₂ data at a spatial resolution of 0.1° × 0.1°.

3. Results and Discussion

3.1. Predictive Performance Evaluation and Important Factors

For XCO₂ modeling, machine learning models with different integration methods were selected. Among the models based on the bagging integration method, the random forest model performed best (Table 2), with an R² of 0.878, a mean square error (RMSE) of 1.123 ppm, and a mean absolute error (MAE) of 0.867 ppm. Among the models based on the boosting ensemble method, the CatBoost model performed the best (see Table 2), with an R2 of 0.845, a Root Mean Square Error (RMSE) of 1.261 ppm, and a mean absolute error (MAE) of 0.935 ppm. Therefore, we chose a random forest as the optimal model for the prediction of XCO₂.

The random forest model performed well in predicting XCO₂ on a diurnal scale, with an R2 of 0.878 and an RMSE of 1.123 ppm in cross-validation (Figure 1). Compared with CT XCO₂, its R2 and root mean square error (RMSE) performance were better, and the average deviation (bias) was slightly improved; meanwhile, compared with the XCO₂ average, the difference was not large.

There was a certain difference between RF-CO₂ and the observations at Waliguan Station (WLG) and Lulin Station (LLN); see Figure 2. This is because surface stations such as Waliguan mainly measure near-surface CO₂ concentrations, while the RF-CO₂ data represent the total column average concentration of CO₂ (i.e., XCO₂) [35]. Moreover, there are obvious changes in atmospheric CO₂ over the day, and the low correlation may also be attributed to the mismatch between the observation time of ground stations and that of the satellites. However, RF-CO₂ showed similar seasonal and interannual trends to those observed at the ground stations (see Figure 2). Seasonally, both were higher in spring and winter and lower in summer and autumn. Both of the interannual changes showed an increasing trend year by year, but the increase in RF-CO₂ was not as obvious as that for the station monitoring data; again, mainly because RF-CO₂ is a vertically integrated concentration, and its change was lower than that of the near-surface concentration.

The feature importance results indicated that CT XCO₂ was the most important predictor (Table 3), with a relative importance value of 83.08%, indicating that the predicted XCO₂ increased almost linearly with the increase in CT XCO₂; this was due to CT XCO₂ and OCO-2 XCO₂ having a relatively high correlation, with R² 0.795 (Figure 1a). Meteorological predictors, with a total importance value of 9.23%, can affect the spatiotemporal distribution of XCO₂ by affecting carbon emissions and diffusion [30,31]. The dew point temperature and air temperature were found to have a greater impact on XCO₂ at 2.72% and 3.12%, respectively which was consistent with the previous research results; that is, XCO₂ is related to temperature and dry/wet conditions [36]. Wind speed had a small effect on XCO₂, with an importance of 1.4%; however, when the wind speed is high, it can disperse CO₂ closer to the background level [37]. The total importance of latitude, longitude, and elevation was 5.77%, indicating that terrain has a certain influence on CO₂. The total importance of the remaining variables in XCO₂ modeling was 1.92%, explaining the influence of population density, vegetation, and land-use type.

3.2. Comparison of RF XCO₂ and CT XCO₂

From 2016 to 2018, the national average of RF XCO₂ was 0.237 ppm lower than CT XCO₂ (Figure 3d), but the national annual mean difference showed an increasing trend, from −0.108 ppm in 2016 to 0.239 ppm in 2018 (Figure 3a–c). In terms of spatial distribution, ∆XCO₂ (∆XCO₂ = CT XCO₂ − RF XCO₂) was relatively high in East China, Central China, South China, and Northeast China. The CT XCO₂ value was higher than the RF XCO₂ value. ∆XCO₂ was significantly lower in southern Xinjiang, indicating that CT XCO₂ was significantly underestimated in this region. However, ∆XCO₂ was relatively small in North China, Southwest China, and most parts of Northwest China, indicating that the CT XCO₂ value was relatively accurate and presented little difference from the RF XCO₂ value. The main reason for the above phenomenon is that CT XCO₂ relies heavily on ground data; however, China currently has few ground monitoring stations with uneven distribution. China is preparing to install more ground monitoring stations, which will help to conduct better monitoring in the future, allowing for further Validation and improvement of Carbon Tracker models.

The RF XCO₂ fit the OCO-2 XCO₂ well, and thus the spatiotemporal distribution of ∆XCO₂ may serve to represent the difference between OCO-2 XCO₂ and CT XCO₂ visually. In contrast, the differences between CT XCO₂ and OCO-2 XCO₂ n East China, Central China, South China, Northeast China, and southern Xinjiang were significantly larger, while those in North China, Southwest China, and Northwest China were relatively small. The comparison results indicated that there are still high uncertainties in CT XCO₂, which may be mainly due to the errors in the emission inventory and the small number of ground observation stations. This result may also be due to the high uncertainty and coarse spatial resolution (3° × 2°) of CT XCO₂, making it insufficient to display the detailed spatial distribution of XCO₂, especially in small areas. Therefore, the XCO₂ data set, with full coverage and high spatial resolution, is of great value for monitoring the distribution of carbon sources and sinks in China.

3.3. Spatial Distribution of RF XCO₂

From 2016 to 2018, the multi-year average of RF XCO₂ in China was 405.86 ± 1.73 ppm (Figure 4a), with the highest level in East China (406.94 ± 0.65 ppm) and the lowest level in Northwest China (405.56 ± 1.43 ppm). CO₂ emissions are often related to intensive human activities. East China and Central China not only possess large populations but also have developed economies and intensive human activities. This is also the main reason for the high XCO₂ observed in East and Central China. XCO₂ was also relatively high in parts of North China, mainly due to the intensive human activities in the Beijing—Tianjin—Hebei region, the use of centralized heating for a long period of time in winter, high CO₂ emissions, and cold and dry winters, resulting in the low photosynthetic efficiency of vegetation. Inner Mongolia has low population density and lush vegetation, so XCO₂ is relatively low in this region [37]. In South China, the economy is relatively developed and there are many human activities; however, due to the warm and humid climate, the vegetation coverage rate is relatively high, and its photosynthetic carbon fixation rate is relatively high, causing the level of XCO₂ to be moderate [35]. For Northeast and Northwest China, the population density is low, and carbon emissions from fossil fuel combustion and biomass combustion are relatively low, causing the XCO₂ to be low. The southwest region has a moderate population, but the vegetation is lush, the climate is humid, and the photosynthetic efficiency of the vegetation is high, such that the XCO₂ is low. Compared with CT XCO₂, RF XCO₂ presented a more detailed and accurate spatiotemporal distribution. Compared with OCO-2 satellite data, due to clouds or other reasons, there are a lot of missing data, making it difficult to directly apply to carbon source and carbon sink monitoring, while RF XCO₂ can achieve full coverage of XCO₂ data, allowing for more effective monitoring of carbon sources and sinks.

From 2016 to 2018, the national RF XCO₂ increased from 403.37 to 407.90 ppm (see Figure 4b), with an average rate of 2.265 ppm/year. The XCO₂ growth rates in North China, Southwest China, and East China were all higher than the national average rate (2.315 ppm/year, 2.303 ppm/year, and 2.267 ppm/year, respectively), while the XCO₂ growth rates in Northwest, Northeast, Central, and South China were lower than the national average rate (2.263 ppm/year, 2.222 ppm/year, 2.195 ppm/year and 2.178 ppm/year, respectively). Although XCO₂ was still increasing, its growth rate gradually slowed down, from 2.44 ppm/year in 2016–2017 to 2.09 ppm/year in 2017–2018, which may be due to the promotion of low-carbon life and the use of clean energy.

From 2016 to 2018, the national averages of RF XCO₂ in spring (Figure 5a), summer (Figure 5b), autumn (Figure 5c), and winter (Figure 5d) were 407.76 ± 1.72, 403.15 ± 3.36, 404.86 ± 1.71 and 406.90 ± 2.50 ppm, respectively. From the perspective of seasonal distribution, in most regions of China, XCO₂ in spring was higher than that in summer, consistent with the results of previous studies [35,38]. In spring, the average seasonal value of XCO₂ in Northeast China, East China, North China, Central China, South China, and Northwest China was higher than 407 ppm; meanwhile, in summer, the average seasonal value of XCO₂ in Northeast China, North China, Northwest China, and parts of Central China was lower than 405 ppm. The reason may be that the summer was warm and humid, vegetation photosynthesis was strong, and a large amount of CO₂ was absorbed by plants, resulting in a decrease of 4.61 ppm in the national average in summer compared with spring. In winter, due to the cold and dry climate, plant respiration is stronger than photosynthesis, resulting in a large amount of CO₂ being accumulated in the atmosphere, leading to generally higher XCO₂ than that in autumn and summer. In addition, most areas in northern China use fossil fuels or biomass for heating in winter, producing a large amount of CO₂. This is why the seasonal variations in North China, Northeast China, and Northwest China are greater than those in the South. In summary, the main reasons for the seasonal variation of XCO₂ may be plant photosynthesis and human activities (mainly including fossil fuel consumption and agricultural production) [35,39].

4. Conclusions

Based on OCO₂ XCO₂, CT XCO₂, and multivariate geographic data, the full-coverage spatiotemporal distribution of daytime XCO₂ in China from 2016 to 2018 was obtained using a Random Forest machine learning model. Compared with CT XCO₂, having a coarse spatial resolution (3° × 2°), RF XCO₂ with a high spatial resolution (0.1° × 0.1°) showed more detailed spatial variation, indicating that it may be used to identify potentially important carbon sources and sinks in further research. The RF-XCO₂ data set constructed in this study better revealed the distribution of XCO₂ in China. In terms of spatial distribution, the highest multi-year average RF XCO₂ value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In view of the different levels of CO₂ emissions in different geographical regions, it is necessary to reduce CO₂ emissions in East China, Central China and parts of North China or to establish an effective carbon trading market to achieve a dynamic carbon emission balance in different regions. In terms of time, from 2016 to 2018, the annual XCO₂ in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal trends, the multi-year average XCO₂ in spring was the highest (407.76 ± 1.72 ppm), while that in summer was the lowest (403.15 ± 3.36 ppm). In view of these inter-annual and seasonal changes, it is necessary to fully promote clean energy, replace fossil fuels and biomass fuels, and reduce seasonal changes within the year while maintaining a low growth rate. With the continuous launch of carbon monitoring satellites (e.g., GOSAT, OCO-2, and OCO-3), future multi-satellite combinations can better achieve data assimilation, which is expected to not only improve the quality of data but also extend the timeframe for XCO₂ prediction.

Author Contributions

Conceptualization, S.H.; methodology, S.H., Y.Y. and Z.W.; investigation, Y.Y., Z.W., L.L. and Z.Z.; writing—original draft preparation, S.H., Z.W. and H.D.; writing—review and editing, S.H. and C.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data used in this study, including satellite and ground data, are from sources providing the data freely available through the internet.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. Relationship between OCO-2 XCO2 and CT XCO2 (a) resampled to 0.1° × 0.1° by inverse distance-weighted interpolation, and RF XCO2 (b) predicted by the Random Forest model in sample-based cross-validation. The red dotted line represents the fitted line, while the dashed black line indicates a 1:1 relationship.

Figure 2. Comparison of RF-CO2 observation data with WLG (a) and LLN (b) station observations.

Figure 3. Spatial distribution of the annual mean difference between CT XCO2 and RF XCO2 from 2016 to 2018 (a–c) and the multi-year mean difference between CT XCO2 and RF XCO2 (d).

Figure 4. Spatial distribution of multi-year averages RF XCO2 (a) and 2016—2018 annual averages RF XCO2 (b).

Figure 5. Spatial distribution of multi-year Spring (a), Summer (b), Autumn (c), and Winter (d) averages of RF XCO2 from 2016 to 2018.

Table 1

Auxiliary data and related information.

Data Source	Type	Spatial Resolution	Time Resolution
Carbon Tracker	XCO2	3° × 2°	3 h
MODIS	NDVI	0.05° × 0.05°	8 d
	Land-Use (LU)	500 m × 500 m	1 y
ERA-5	2 m temperature (t2m)	0.25° × 0.25°	1 h
	2 m dewpoint temperature (d2m)
	Surface pressure (sp)
	10 m v-component of wind (v10)
	10 m u-component of wind (u10)
World Pop	Population density (pop)	1 km × 1 km	1 y
SRTM	DEM	90 m × 90 m	-

Table 2

Comparison of prediction performance of different machine learning models.

Model	Cross-Validation R2	RMSE (ppm)	MAE (ppm)
RF	0.878	1.123	0.867
ERT	0.845	1.261	0.931
XGB	0.841	1.279	0.952
LGB	0.832	1.312	0.981
CatBoost	0.845	1.261	0.935

Table 3

XCO₂ prediction model variable importance distribution.

Variable	Importance	Variable	Importance
Longitude	2.03%	u10	0.68%
Latitude	2.23%	v10	0.72%
CT XCO2	83.08%	DEM	1.51%
d2m	2.72%	pop	0.81%
t2m	3.12%	LU	0.2%
sp	1.99%	NDVI	0.91%

References

1. IPCC. Climate Change 2021: The Physical Science Basis; Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge Press: Cambridge, UK, 2021.

2. Edenhofer, O.; Seyboth, K. Intergovernmental panel on climate change (IPCC). Encycl. Energy Nat. Resour. Environ. Econ.; 2013; 26, pp. 48-56.

3. Hungershoefer, K.; Breon, F.M.; Peylin, P.; Chevallier, F.; Rayner, P.; Klonecki, A.; Houweling, A.; Marshall, J. Evaluation of various observing systems for the global monitoring of CO₂ surface fluxes. Atmos. Chem. Phys.; 2010; 10, pp. 10503-10520. [DOI: https://dx.doi.org/10.5194/acp-10-10503-2010]

4. Butz, A.; Hasekamp, O.P.; Frankenberg, C.; Aben, I. Retrievals of atmospheric CO₂ from simulated space-borne measurements of backscattered near-infrared sunlight: Accounting for aerosol effects. Appl. Opt.; 2009; 48, pp. 3322-3336. [DOI: https://dx.doi.org/10.1364/AO.48.003322]

5. Bovensmann, H.; Burrows, J.P.; Buchwitz, M.; Frerick, J.; Noël, S.; Rozanov, V.V.; Chance, K.V.; Goede, A.P.H. SCIAMACHY—Mission objectives and measurement modes. J. Atmos. Sci.; 1999; 56, pp. 127-150. [DOI: https://dx.doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2]

6. Zhao, M.; Yue, T.; Zhang, X.; Sun, J.; Jiang, L.; Wang, C. Fusion of multi-source near-surface CO₂ concentration data based on high accuracy surface modeling. Atmos. Pollut. Res.; 2017; 8, pp. 1170-1178.

7. Ballav, S.; Naja, M.; Patra, P.K.; Machida, T.; Mukai, H. Assessment of spatio-temporal distribution of CO₂ over greater Asia using the WRF–CO₂ model. J. Earth Syst. Sci.; 2020; 129, 80. [DOI: https://dx.doi.org/10.1007/s12040-020-1352-x]

8. Andres, R.J.; Boden, T.A.; Bréon, F.M.; Ciais, P.; Davis, S.; Erickson, D.; Gregg, J.S.; Jacobson, A.; Marland, G.; Miller, J. et al. A synthesis of carbon dioxide emissions from fossil-fuel combustion. Biogeosciences; 2012; 9, pp. 1845-1871. [DOI: https://dx.doi.org/10.5194/bg-9-1845-2012]

9. Wunch, D.; Wennberg, P.O.; Osterman, G.; Fisher, B.; Naylor, B.; Roehl, C.M.; O’Dell, C.; Mandrake, L.; Viatte, C.; Griffith, D.W. et al. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO₂measurements with TCCON. Atmos. Meas. Tech. Discuss.; 2017; 10, pp. 2209-2238. [DOI: https://dx.doi.org/10.5194/amt-10-2209-2017]

10. Liang, A.; Gong, W.; Han, G.; Xiang, C. Comparison of Satellite-Observed XCO₂ from GOSAT, OCO-2, and Ground-Based TCCON. Remote Sens.; 2017; 9, 1033. [DOI: https://dx.doi.org/10.3390/rs9101033]

11. Wu, L.; Meijer, Y.; Sierk, B.; Hasekamp, O.; Butz, A.; Landgraf, J. XCO₂ observations using satellite measurements with moderate spectral resolution: Investigation using GOSAT and OCO-2 measurements. Atmos. Meas. Tech.; 2020; 13, pp. 713-729. [DOI: https://dx.doi.org/10.5194/amt-13-713-2020]

12. Nguyen, H.; Katzfuss, M.; Cressie, N.; Braverman, A. Spatio-temporal data fusion for very large remote sensing datasets. Technometrics; 2014; 56, pp. 174-185. [DOI: https://dx.doi.org/10.1080/00401706.2013.831774]

13. Tomosada, M.; Kanefuji, K.; Matsumoto, Y.; Tsubaki, H. A Prediction Method of the Global Distribution Map of CO2 Column Abundance Retrieved from GOSAT Observation Derived from Ordinary Kriging. Proceedings of the ICROS-SICE International Joint Conference 2009, Fukuoka International Congress Center; Fukuoka, Japan, 18–21 August 2009.

14. Zeng, Z.; Lei, L.; Guo, L.; Zhang, L.; Zhang, B. Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China. Chin. Sci. Bull.; 2013; 58, pp. 1948-1954. [DOI: https://dx.doi.org/10.1007/s11434-012-5652-7]

15. Hammerling, D.M.; Michalak, A.M.; Kawa, S.R. Mapping of CO2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO-2. J. Geophys. Res. Atmos.; 2012; 117, D06306. [DOI: https://dx.doi.org/10.1029/2011JD017015]

16. Guo, M.; Wang, X.; Li, J.; Yi, K.; Zhong, G.; Tani, H. Assessment of global carbon dioxide concentration using MODIS and GOSAT data. Sensors; 2012; 12, pp. 16368-16389. [DOI: https://dx.doi.org/10.3390/s121216368]

17. Siabi, Z.; Falahatkar, S.; Alavi, S.J. Spatial distribution of XCO₂ using OCO-2 data in growing seasons. J. Environ. Manag.; 2019; 244, pp. 110-118. [DOI: https://dx.doi.org/10.1016/j.jenvman.2019.05.049]

18. Girach, I.A.; Ponmalar, M.; Murugan, S.; Rahman, P.A.; Babu, S.S.; Ramachandran, R. Ramachandran. Applicability of Machine Learning Model to Simulate Atmospheric CO₂ Variability. IEEE Trans. Geosci. Remote Sens.; 2022; 60, 4107306. [DOI: https://dx.doi.org/10.1109/TGRS.2022.3157774]

19. He, C.; Ji, M.; Li, T. Deriving Full-Coverage and Fine-Scale XCO₂ Across China Based on OCO-2 Satellite Retrievals and CarbonTracker Output. Geophys. Res. Lett.; 2022; 49, e2022GL098435. [DOI: https://dx.doi.org/10.1029/2022GL098435]

20. Li, J.; Jia, K.; Wei, X.; Xia, M.; Chen, Z.; Yao, Y.; Zhang, X.; Jiang, H.; Yuan, B.; Tao, G. et al. High-spatiotemporal resolution mapping of spatiotemporally continuous atmospheric CO2 concentrations over the global continent. Int. J. Appl. Earth Obs. Geoinf.; 2022; 108, 102743. [DOI: https://dx.doi.org/10.1016/j.jag.2022.102743]

21. Wang, W.; He, J.; Feng, H.; Jin, Z. High-Coverage Reconstruction of XCO₂ Using Multisource Satellite Remote Sensing Data in Beijing–Tianjin–Hebei Region. Int. J. Environ. Res. Public Health; 2022; 19, 10853. [DOI: https://dx.doi.org/10.3390/ijerph191710853]

22. Zhang, L.; Li, T.; Wu, J. Deriving gapless CO₂ concentrations using a geographically weighted neural network: China, 2014–2020. Int. J. Appl. Earth Obs. Geoinf.; 2022; 114, 103063. [DOI: https://dx.doi.org/10.1016/j.jag.2022.103063]

23. Nassar, R.; Hill, T.G.; McLinden, C.A.; Wunch, D.; Jones, D.B.A.; Crisp, D. Quantifying CO2 emissions from individual power plants from space. Geophys. Res. Lett.; 2017; 44, pp. 10045-10053. [DOI: https://dx.doi.org/10.1002/2017GL074702]

24. Jacobson, A.R.; Schuldt, K.N.; Miller, J.B.; Oda, T.; Tans, P.; Andrews, A.; Mund, J.; Ott, L.; Collatz, G.J.; Aalto, T. et al. CarbonTracker CT2019B; NOAA Global Monitoring Laboratory: Boulder, CO, USA, 2020.

25. Yang, L.; Meng, X.; Zhang, X. SRTM DEM and its application advances. Int. J. Remote Sens.; 2011; 32, pp. 3875-3896. [DOI: https://dx.doi.org/10.1080/01431161003786016]

26. Tatem, A.J. WorldPop, open data for spatial demography. Sci. Data; 2017; 4, 170004. [DOI: https://dx.doi.org/10.1038/sdata.2017.4]

27. Friedl, M.A.; Sulla-Menashe, D. MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006; NASA EOSDIS Land Processes DAAC: Sioux Falls, SD, USA, 2018.

28. Didan, K. MOD13C1 MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006; NASA EOSDIS Land Processes DAAC: Sioux Falls, SD, USA, 2015.

29. Muñoz Sabater, J. ERA5-Land Hourly Data from 1981 to Present; Copernicus Climate Change Service (C3S) Climate Data Store (CDS): Brussels, Belgium, 2019.

30. Breiman, L. Random forests. Mach. Learn.; 2001; 45, pp. 5-32. [DOI: https://dx.doi.org/10.1023/A:1010933404324]

31. Geurts, P.; Ernst, D.; Wehenkel, L. Extremely randomized trees. Mach. Learn.; 2006; 63, pp. 3-42. [DOI: https://dx.doi.org/10.1007/s10994-006-6226-1]

32. Chen, T.; Guestrin, C. XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery; New York, NY, USA, 13–17 August 2016; pp. 785-794.

33. Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T.Y. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst.; 2017; 30, pp. 569-577.

34. Dorogush, A.V.; Ershov, V.; Gulin, A. CatBoost: Gradient boosting with categorical features support. arXiv; 2018; arXiv: 1810.11363

35. Lv, Z.; Shi, Y.; Zang, S.; Sun, L. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors. Atmosphere; 2020; 11, 231. [DOI: https://dx.doi.org/10.3390/atmos11030231]

36. Falahatkar, S.; Mousavi, S.M.; Farajzadeh, M. Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN. Environ. Monit. Assess.; 2017; 189, 627. [DOI: https://dx.doi.org/10.1007/s10661-017-6285-8]

37. Britter, R.E. Atmospheric Dispersion of Dense Gases. Annu. Rev. Fluid Mech.; 1989; 21, pp. 317-344. [DOI: https://dx.doi.org/10.1146/annurev.fl.21.010189.001533]

38. Bie, N.; Lei, L.; He, Z.; Zeng, Z.; Liu, L.; Zhang, B.; Cai, B. Specific patterns of XCO₂ observed by GOSAT during 2009–2016 and assessed with model simulations over China. Sci. China Earth Sci.; 2020; 63, pp. 384-394. [DOI: https://dx.doi.org/10.1007/s11430-018-9377-7]

39. Xu, Y.; Ke, C.; Zhan, W.; Li, H.; Yao, L. Variations in satellite-derived carbon dioxide over different regions of China from 2003 to 2011. Atmos. Environ.; 2017; 150, pp. 379-388. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2016.11.032]