## 5 Conclusions  
In this study, we validated three global LAI products and their uncertainty products using \(\sim\)8000 in situ measurements of LAI from subtropical and tropical forests in the southern China region. The finest resolution product, Collection 300 m GEOV3, performed best, with the lowest RMSE and the lowest bias. It also best captured the temporal dynamics observed in the in-situ dataset, including the magnitude, amplitude, and phases of the LAI time series. Collection 6 500 m MODIS LAI showed the poorest performance and importantly it did not capture the temporal dynamics observed in situ for the forests in this specific region. Critically, for all three LAI products, the accompanying uncertainties were all far smaller than the bias compared to the in situ measurements, indicating that each product uncertainty estimate is not robust. Given the importance of LAI uncertainty in the context of constraining models of carbon cycling and other ecosystem processes, users should use the product's uncertainty with caution and consider inflating the existing LAI product uncertainty when using it within data assimilation frameworks or other studies in which the uncertainty plays an important role.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/12/19/3122/s1](http://www.mdpi.com/2072-4292/12/19/3122/s1)]([http://www.mdpi.com/2072-4292/12/19/3122/s1](http://www.mdpi.com/2072-4292/12/19/3122/s1)), Table S1: Detailed forest community information in the southern China region, Table S2: The detailed information for field LAI measurements at each site, Table S3: Distribution of the ratio of EO uncertainty estimates to bias, based on a comparison against in situ LAI measurements, Figure S1: Forest appearance for each forest in this study in the southern China region, Figure S2: TOPO30 DEM (1 km) in the China region. Figure S3: Location of the sampling subplots and the GEOV2 1 km LAI pixels showed on the Landsat EUI scenes for different sites, Figure S4: Location of the sampling subplots and the GEOV2 1 km LAI pixels showed on the SARTM 90 m DEM images for different sites. Figure S5: Frequency distribution of the ratio of LAI products uncertainty with LAI products bias against the in situ measurements for different sites. Figure S6: Regression analyses between the Landsat EUI/NDVI values and the field observed LAI values at sampling subplots, Figure S7: Regression analyses between the proportion of forests cover (LAI pixel level) and the remotely sensed LAI bias against the field measured LAI for different LAI products, Figure S8: Summary of the dates for the MODIS LAI pixels which were influenced by the cloud in this study.  
**Author Contributions:** Conceptualization, Y.Z. and M.W.; methodology, Y.Z., M.W., T.L.S., and D.T.M.; formal analysis, T.L.S. and S.F.-P. and D.T.M.; writing--original draft preparation, Y.Z. and M.W.; writing--review and editing, M.W., X.C., T.L.S., D.T.M., S.F.-P., and Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under grant number 41771049 and NCEO, CSSP Newton Fund, and China Scholarship Council.  
**Acknowledgments:** The authors thank all the workers in the Chinese Ecosystem Research Network for field data collection and database establishment.  
**Conflicts of Interest:** The authors declare no conflict of interest.