**4. Conclusions**

This work evaluated four LAI products, GLASS, GLOBALBNU, GLOBMAP, and MODIS, over China using direct and indirect methods. Reference data from OLIVE platform were used for direct validation, and results show that GLASS performed best, with the highest *R*<sup>2</sup> (0.94) and lowest RMSE (0.61), while MODIS performed worst, and GLOBALBNU and GLOBMAP performed moderately. This indicates that the three improved LAI products all show improvement in LAI accuracy over China.

The comparison among the four LAI products revealed that the spatial pattern of all the products agrees well with each other. The spatial correlation indicates four pairs of the products have a strong correlation (*R*<sup>2</sup> > 0.72), while two pairs shows moderate correlation. Compared with MODIS, the spatial correlation ranks as: GLOBALBNU > GLASS > GLOBMAP; this can be easily explained by their LAI retrieval algorithm. LAI difference analysis shows that for all types of biome and for most of the climate zones, GLASS, GLOBALBNU, and GLOBMAP LAI are higher than MODIS. Significant analysis illustrates evergreen needleleaf forest (ENF) and woody savannas (SAV) mainly correspond to large LAI SD, while evergreen needleleaf forest (ENF) and grassland (GRA) are more responsible for RSD. In view of biome types, the value of SD, ranging from 0.17 to 0.75, is partially dependent on the land cover type, i.e., biomes with large LAI have large SD. However, the RSD for all biomes is on the order of 0.3, indicating a typical 30% uncertainty for LAI products. From the perspective of climate types, temperate dry hot summer, temperate warm summer/dry winter and temperate hot summer/no dry season climate types are mainly responsible for large SD, while temperate warm summer/dry winter and cold dry winter/warm summer climate types mainly correspond to large RSD. For different climate types, the value of SD ranges from 0.05 to 0.8. However, the RSD of most climate types is on the order of 0.3, in line with the findings from biome types. Therefore, the comparison results indicate there is a typical 30% uncertainty for the four LAI products.

Our results could benefit researchers for LAI product selection and uncertainty quantification and could also provide clues for data producers to further improve their datasets. Moreover, the uncertainties quantified by this comparison could benefit researchers who include LAI as an input parameter. For instance, our results could contribute to the error matrix development in the data assimilation system developed by Huang et al. [8]. In this study, due to the page limit, we mainly focus on the spatial patterns of four LAI climatologies and annual means. In the future, we will compare the temporal variations and trends of these four LAI products, which could contribute to research related to phenology and global change. Meanwhile, there is a need to supplement more field measurements of LAI and more accurate reference maps over mainland China.

**Acknowledgments:** This work was jointly supported by the National Basic Research Program of China (No. 2015CB953703), the National Key Research and Development Program of China (2017YFA0603703), and the National Natural Science Foundation of China (91537210 & 91747101). The four LAI products were provided by the Center for Global Change Data Processing and Analysis at Beijing Normal University (BNU), the Land–Atmosphere Interaction research group at BNU, Ronggao Liu's group at the Institute of Geographic Sciences and Natural Resources Research of the Chinese Academy of Sciences (CAS), and Level 1 and Atmosphere Archive and Distribution System of National Aeronautics and Space Administration (NASA). The land cover map was provided by the Cold and Arid Regions Environmental and Engineering Research Institute, CAS. The computation of this work was supported by Tsinghua National Laboratory for Information Science and Technology. The authors also acknowledge Pauline Lovell and Arthur Cracknell for their kind help and comments on this paper.

**Author Contributions:** All authors made great contributions to this study. Hui Lu conceived and designed this study. Xinlu Li and Hui Lu performed the experiments. Xinlu Li, Hui Lu, Kun Yang, and Le Yu wrote the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
