# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 5 Conclusions  
Based on in situ carbon flux measurements, meteorological records, and remote sensing data, this paper evaluated the performance of multiple machine learning models, including CNN, ANN, and RF, in generating the global carbon flux dataset (GCFD) with three carbon fluxes: GPP, RECO, and NEE. According to the station-to-station and year-to-year validations, CNN performed better than RF and ANN models, and performed better in spatial interpolation than in temporal extrapolation. Thus, CNN was chosen to produce GCFD. GCFD achieved a satisfactory performance with R\({}^{2}\) values of 0.82, 0.72, and 0.62 for GPP, RECO, and NEE, respectively, and with RMSE values of 1.54, 1.27, and 1.31 g C m\({}^{-2}\) d\({}^{-1}\), respectively. Compared with GPP and RECO, NEE has relative low accuracy, especially for its temporal change, which should be examined in the application of GCFD. GCFD showed reasonable spatial and temporal variations, and in accordance with the vegetation conditions, including LAI and FAPAR. GCFD outperformed two other machine learning based products, including Zeng et al. and FLUXCOM, in providing more accurate spatial distribution with higher resolution and more accurate annual changes. GCFD has made a great contribution to carbon flux modelling by providing a high spatial resolution dataset using a deep learning model. With a spatial resolution up to 1 km, GCFD can be a useful complement to existing model-based and satellite-based datasets for various meteorological and ecological analyses and modelling, especially for the practical applications that require high resolution carbon flux maps. In the future, GCFD can be updated with more site observations and more predictors affecting carbon fluxes, such as CO\({}_{2}\) concentration, for longer time coverage. The application of deep learning tools in predicting carbon fluxes could also be an important field of study. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f14050913/s1](https://www.mdpi.com/article/10.3390/f14050913/s1)]([https://www.mdpi.com/article/10.3390/f14050913/s1](https://www.mdpi.com/article/10.3390/f14050913/s1)), Figure S1: The correlation coefficient matrix between the variables; Figure S2: Scatter plot of GPP, RECO and NEE observed data and predicted data by GCFD (Figure a, b and c), Zeng et al (Figure d, e and f) and FLUXCOM (Figure g, h and i); Figure S3: Scatter plot of GPP, RECO and NEE observed data and predicted data by CNN model at monthly scale in station-to-station validation (Figure a, b and c) and year-to-year validation (Figure d, e and f); Figure S4: RECO maps of the global and oceanic climate area from different products on 10 th July 2010; Figure S5: NEE maps of the global and oceanic climate area from different products on 10 th July 2010; Figure S6: GPP maps of the global and a continental climate area from different products, FAPAR maps and LAI maps on 10 th July 2010; Figure S7: RECO maps of the global and a continental climate area from different products on 10 th July 2010; Figure S8: NEE maps of the global and a continental climate area from different products on 10 th July 2010; Figure S9: GPP maps of the global and a dry climate area from different products, FAPAR maps and LAI maps on 10 th July 2010; Figure S10: NEE maps of the global and a dry climate area from different products on 10 th July 2010; Figure S11: NEE maps of the global and a dry climate area from different products on 10 th July 2010; Figure S12: GPP maps of the global and a subtropical climate area from different products, FAPAR maps and LAI maps on 10 th July 2010; Figure S13: RECO maps of the global and a subtropical climate area from different products on 10 th July 2010; Figure S14: NEE maps of the global and a subtropical climate area from different products on 10 th July 2010. **Author Contributions:** [PERSON]. ([PERSON]) conceived the research and secured funding for the research. [PERSON]. ([PERSON]) and [PERSON] designed the experiment.