# Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020
## 4 Discussion  
### Model Performance and Variable Selection  
In this study, 21 variables were considered ECs, and a national 0-20 and 0-100 depth SOCD random forest model was developed. It was found that the 0-100 cm depth model (R\({}^{2}\) = 0.61, LCCC = 0.76) had a higher accuracy than the 0-20 cm depth model (R\({}^{2}\) = 0.55, LCCC =0.71). In particular, the R\({}^{2}\) of the 0-100 cm depth model was greatly improved compared to previous studies [40; 41; 42]. The Lin's CCC values of both models were higher than 0.7, which is at a relatively high level compared with that reported in previous studies [43; 44]. This indicates that the SOC model used in this study has high stability and reliability.  
On the one hand, the random forest model was able to fully utilize its intrinsic stochasticity and integrated learning to more comprehensively consider a variety of potential variables and reduce the model's over-reliance on specific features [45; 46]. By integrating the results of multiple decision trees, random forests were able to provide more stable and accurate predictions, which reduces the errors in the inversion process, giving them a significant advantage in dealing with the complex problem of predicting SOC changes across time [41; 47; 48]. On the other hand, most studies on soil samples have time errors when matching with environmental covariates (ECs) [40; 41; 42]. This study extracted ECs in the corresponding year based on the SOCD sampling year, which increases the reliability of the relationship between SOCD and ECs. Finally, current studies on SOC time series in China mainly focus on the period before 2015 [40; 41]. To reduce the errors due to  
Figure 9: Land use change from 2001 to 2020 (the left side is the area transferred out of different land types, and the right side is the area transferred in from different land types; area unit is km\({}^{2}\)).  
spatio-temporal heterogeneity, we supplemented the data for 3097 and 770 soil samples at a depth of 0-20 cm and 0-100 cm, respectively, after 2015 by collecting and field survey soil samples. In 2022, our project team conducted field surveys in typical areas across the country, from which we obtained 1940 and 263 soil samples at a depth of 0-20 cm and 0-100 cm, respectively, which greatly increased the sample density. These advantages thus enhance the accuracy and reliability of our assessment results.  
Our results show that from 2001 to 2020, the variation in SOCS in China ranged from 38.11 to 41.12 Pg C for a depth of 0-20 cm and from 110.49 to 114.96 Pg C for a depth of 0-100 cm. Regarding the study of soil carbon pools, [PERSON] et al. (1996) utilized the information from 745 soil profile surveys across the country, and the earliest estimation of China's soil carbon stock was 185.7 Pg C ([PERSON] and [PERSON] (1999)) estimated the SOC pool in China to be about 100.18 Pg C based on the second national soil census data using GIS technology ([PERSON] and [PERSON] (1999)). Most other estimates of soil carbon stocks in China based on the second national soil census data were in the range of 69 \(\sim\) 92 Pg C ([PERSON] and [PERSON] (1999)). Compared to most studies that only assessed SOC in forest, grassland, farmland, and wetland ecosystems ([PERSON] and [PERSON] (1999); [PERSON] and [PERSON] (1999)), SOC in desert, urban, and other terrestrial ecosystems, which are also non-negligible in terms of soil carbon stock, were included in our study, except for glaciers and lakes, which are also important reasons for the larger estimation ([PERSON] and [PERSON] (1999)). In addition, the accumulation of SOC over recent decades is not negligible.  
Overall, our study has improved the simulation methodology and data processing, and the simulation results are reliable. However, model simulations inevitably introduce errors, and the dependence on data quality and completeness, as well as the limited ability to characterize complex soil processes, deserve more in-depth exploration in the future.