# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 3 Results and Discussion  
### Estimated Anthropogenic Emissions by GRNN  
We use the annual dXCO\({}_{2}\) dataset and ODIAC CO\({}_{2}\) emissions data from the year 2010 to 2014 as the training dataset, which have the total of 5415 samples available, to build a GRNN model for estimating anthropogenic emission. By applying \"the holdout method\" described in Section 2.3, we obtain the optimized spread parameter \(\sigma\) as 0.1. Then we apply GRNN model to the annual dXCO\({}_{2}\) data in 2015 to predict target dependent variable, anthropogenic emission with the same unit as the ODIAC CO\({}_{2}\) emissions.  
The CO\({}_{2}\) emissions estimated using the annual dXCO\({}_{2}\) and the actual ODIAC CO\({}_{2}\) emission in 2015 are shown in Figure 4. Comparing Figure 4a with Figure 4b, we can see that the spatially changing pattern of estimated emission by satellite-based observation is exactly similar as that of the actual magnitude of ODIAC. Moreover, the estimated emission presents a more smoothing spatial details than the actual emission, which is mainly because the Kriging procedure smooths the CO\({}_{2}\) signals  
Figure 3: Schematic diagram of the generalized regression neural network architecture based on Cigizolu and Alp [30].  
from point sources of strong anthropogenic emission, and 10 km spatial resolution of each GOSAT footprint observations also smooths the signals. The magnitude of estimated emission is generally less than that of ODIAC. Figure 5 presents the differences between them and the corresponding histogram.  
It can be seen from Figure 5a that the difference between the estimated CO\({}_{2}\) emission and ODIAC emission mainly change from \(-5\) Mt to 5 Mt, which accounts for 91% of the total grids. The magnitude of difference from \(-1\) Mt to 1 Mt accounts for 71% of the total grids. The low magnitude of ODIAC emissions in the range of 1-10\({}^{4}\) t/year shown in Figure 4b are generally underestimated by satellite-based observations (shown in yellow in Figure 5a). These are mostly located in semi-arid grasslands, forests in the northern areas as shown in land use map of Figure 5c.  
This underestimation implies that the emission estimated by dXCO\({}_{2}\) has high uncertainty in the areas of low anthropogenic emission that is likely due to the CO\({}_{2}\) uptake of biosphere which is still remaining in dXCO\({}_{2}\). The estimated emission, moreover, is much lower than ODIAC emission over the areas around big cities, such as Beijing, Shanghai, Guangzhou. This underestimation indicates that the smoothing effects of the estimated emission, which is likely because the spatial resolution of GOSAT observations (10 km) is not sufficient to detect the emission of point sources. On the other  
Figure 4: The anthropogenic CO\({}_{2}\) emissions in 2015 in China: (**a**) CO\({}_{2}\) emission estimated using GRNN based on the annual dXCO\({}_{2}\) in 2015 from GOSAT observations. The Tibet area shown as blank is filtered due to their high uncertainty XCO\({}_{2}\) retrievals; (**b**) the CO2 emission from ODIAC in 2015.  
Figure 5: (**a**) The difference between the estimated CO\({}_{2}\) emission using mapping-XCO\({}_{2}\) in 2015 and ODIAC emission in 2015; (**b**) Histogram comparison of the estimated and the ODIAC CO\({}_{2}\) emission (in unit of Ton/year) in 2015; (**c**) Land use of China in 2010.  
hand, the estimated emissions are generally larger than ODIAC emission in the south-eastern region of China where there are many anthropogenic emitting sources which can be seen in Figure 8. The general overestimation in this region is likely because the large emitting sources around raise the concentration of CO\({}_{2}\) over those non-emitting areas nearby them through the atmospheric transport.  
Lastly, comparing the satellite-based estimation of CO\({}_{2}\) emissions with ODIAC emission for all grids as shown in Figure 6, we find they show a significant correlation (R\({}^{2}\)) of 0.65 with p value less than 0.01.