# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 4 Discussion
### Comparison with Different Global Carbon Flux Products  
Here, the GCFD is compared with the predictions provided by [PERSON] et al. [24] (hereafter referred to as [PERSON] et al.) and FLUXCOM [9; 19; 25]. Because FLUXCOM data have been provided at monthly scale, all three data sets were processed into a monthly scale for comparison. Using data from all sites and their corresponding grid data for comparison, the provided results in Figure S2 were obtained. It shows that GCFD led to the best performance, but significant underestimations and overestimations were observed for the other two datasets. FLUXCOM and [PERSON] et al. showed quite similar levels of performance according to R\({}^{2}\) and RMSE values, while GCFD got higher R\({}^{2}\) and lower RMSE values for three fluxes. For example, compared with FLUXCOM, GCFD improved the R\({}^{2}\) by 58%, 66%, and 168% for GPP, RECO, and NEE, respectively. The above validation may not be so fair because FLUXCOM and [PERSON] et al. [24] only used FLUXNET2015, while GCFD used two more site datasets, i.e., FLUXNET-CH4 and Drought-2018 Flux. So, further independent comparison should be done by comparing the two datasets with the results of station-to-station and year-to-year cross validations of CNN in Experiment 2. According to the station-to-station cross validation, CNN in Experiment 2 still showed higher R\({}^{2}\) and lower RMSE values than the other two datasets. For example, compared with FLUXCOM, CNN improved the R\({}^{2}\) by 47%, 40%, and 100% for GPP, RECO, and NEE, respectively. According to the year-to-year cross validation, CNN led to higher R\({}^{2}\) for GPP (improved by 30%), RECO (improved by 1%), and NEE (improved by 42%), and similar RMSE with the other two datasets (Figure S3). The above results imply that GCFD could lead to higher accuracy than FLUXCOM and [PERSON] et al. [24], though it may have drawbacks in predicting the  
Figure 9: Bar chart of feature importance of random forest model: GPP (**a**), RECO (**b**), and NEE (**c**). unseen period by the CNN model. The extrapolation for the unseen period by the model may produce poorer results compared with the interpolation for the unseen locations. Thus, it should be noted that the data in GCFD from 2019 to 2020 may have lower quality than those from 1999 to 2018 because in situ observations from 1999 to 2018 were used to derive the GCFD. The global distribution of GPP, RECO, and NEE as well as their distribution in an area with oceanic climate from the three products on 10 July 2010 are presented in Figure 10, Figures S4 and S5. As can be seen, the GCFD with a spatial resolution of 1 km has more detailed information than other products, and it captures the spatial contiguity of carbon fluxes well. Meanwhile, the spatial patterns of GPP and RECO are consistent with the distribution of the most two important predictors with high resolution (LAI and FAPAR). The spatial distribution of the three carbon fluxes in continental climate zones, dry climate zones, and subtropical climate zones are also presented to further illustrate the advantages of the GCFD (Figures S6-S14). In summary, GCFD can better reflect the detailed spatial distribution of global carbon fluxes in accordance with vegetation conditions than [PERSON] et al. and FLUXCOM. As shown in Figure 7, GCFD could better represent the annual changes of global carbon fluxes than Zeng et al. and FLUXCOM. For all three carbon fluxes, FLUXCOM had little annual change in all climate zones, which is a major defect of this product. Though Zeng et al. could reflect annual changes to some extent, it failed to reflect the increasing trend of GPP and RECO in dry climate zones and the decreasing trend in tropical climate zones, but the GCFD did. For the global average of all sites, GCFD had a slight overestimation of GPP and RECO compared with the observations, while [PERSON] et al. had a slight underestimation. However, FLUXCOM showed significant underestimation.