# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 5 Conclusions  
In this study, we developed the ECSIF-based product with six datasets of GPP, ET, and WUE at 0.05\({}^{\circ}\) spatial resolution and 8-day temporal resolution during 2001-2020. These datasets were based on three SIF products and two machine learning algorithms. First, we examined the relationships of SIF and GPP, as well as SIF and ET, at global FLUXNET sites. In general, positive correlations were found between SIF and these flux variables across most PFTs. We then created six ML models for each of ten PFTs, based on the cross-combination of two ML algorithms and three SIF datasets. Validations of these models showed that the derived GPP and ET values had high correlations and low biases when compared to site-level observations. Using SHAP methods, we confirmed that SIF played a dominant role in predicting site-level GPP and ET. Finally, we generated global ECSIF_GPP and ECSIF_ET datasets by applying the ML models to gridded measurements and found significant increases in global GPP, ET, and WUE during 2001-2020. The causes and consequences of these profound changes in global carbon and water fluxes deserve further investigations in future studies. Moreover, we consider using more spatially distributed site datasets with longer temporal coverage, SIF data observed from satellite directly, and different machine learning algorithms such as deep learning to derive more accurate, further spatiotemporally spanning, and higher-resolution global GPP and ET. **Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs17122064/s1](https://www.mdpi.com/article/10.3390/rs17122064/s1)]([https://www.mdpi.com/article/10.3390/rs17122064/s1](https://www.mdpi.com/article/10.3390/rs17122064/s1)). Figure S1: Global distribution of FLUXNET sites; Figure S2: The relationships between Solar-Induced chlorophyll Fluorescence (SIF) and (a-c) gross primary productivity (GPP) or (d-f) evapotranspiration (ET) at the global FLUXNET sites; Figure S3: The same with Figure 4 but for the XGB model; Figure S4: Long-term (a) mean and (d) trend of water use efficiency (WUE) derived with ECSIF and their comparisons with the (b,e) X-BASE products during 2001-2020; Figure S5: Long-term trend of GPP, ET and WUE from MODIS (a-c) and PML (d-f) during 2001-2020; Figure S6 Comparisons of GPP and ET for ECSIF and FLUXCOM X-BASE products with global FLUXNET sites; Figure S7: Long-term mean of GPP and ET derived with ECSIF and their comparisons with the X-BASE products during 2001-2020; Figure S8: Long-term trend of (a) GPP and (d) ET derived with ECSIF and their comparisons with the (b,e) X-BASE products during 2001-2020; Figure S9: Long-term trend of SIF, meteorology and carbon dioxide used as inputs during 2001-2020; Table S1: Summary of global FLUXNET sites; Table S2: WUE across different PFTs; Table S3: Differences using site-level GPP derived by various methods; Table S4: Differences in GPP, ET and WUE due to various CO\({}_{2}\) products; Table S5: Differences in using dominate PFT and the percentage of PFT in 2001. **Author Contributions:** Conceptualization, H.Z. and X.Y. ; methodology, H.Z. and X.Y. ; validation, J.Z. and X.L. ([PERSON]) and X.L. ([PERSON]); formal analysis, J.Z., H.Z., Z.X., J.X., J.W. and F.Z. ; data curation, J.Z., H.Z. and J.X. ; writing--original draft preparation, J.Z. and H.Z. ; writing--review and editing, H.Z., X.Y., J.X. and F.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the National Key Research and Development Program of China (Grant No. 2023 YFP0805402) and the National Natural Science Foundation of China (Grant No. 42275128). [PERSON] was supported by University of New Hampshire (via bridge support) and Iola Hubbard Climate Change Endowment.