**6. Conclusions**

In this study, we applied machine-learning-based regression models to map the spatial patterns of forest diversity in a temperate mixed forest in northeast China. We did this by coupling the newly available diversity product from GEDI LiDAR and multi-temporal Sentinel-2 imagery. Our results showed that a variety of diversity indices can be predicted accurately through combining forest vertical structure information, plant biochemistry, and phenological variability. More accurately, utilizing the FHD index from GEDI, vegetation indices (NDVI, NDWI and EVI), and shortwave infrared band from Sentinel-2 imagery enhanced our ability to estimate forest diversity better than other variables, especially during the growing season. Moreover, comparing four regression algorithms, the study confirmed that the RF model, combined with GEDI LiDAR and Sentinel-2 data, showed strong performance on forest diversity estimation (R<sup>2</sup> = 0.79) and outperformed SVM, KNN, and LR models (R2 = 0.76, 0.68 and 0.57, respectively). Our results also stressed the great potential of GEDI LiDAR and Sentinel-2 images as explanatory variables for the prediction of forest biodiversity indices. From a forest management perspective, our study developed a reproducible workflow, based on free and openly available GEDI LiDAR and Sentinel-2, that can potentially be used in a routine manner to map forest diversity distribution with a high-resolution, advancing biodiversity conservation and forest ecological restoration.

**Author Contributions:** Conceptualization, Y.X. and C.R.; methodology, Y.X.; validation, P.L. and H.J.; formal analysis, P.L.; writing—original draft preparation, Y.X.; writing—review and editing, C.R.; visualization, H.L.; project administration, C.R.; funding acquisition, All authors have read and agreed to the published version of the manuscript.

**Funding:** The research was funded by the National Natural Science Foundation of China (No. 42171367), and Science & Technology Fundamental Resources Investigation Program (No. 2022FY101902).

**Data Availability Statement:** The remote sensing data were downloaded from Land Processes Distributed Active Archive Center (https://lpdaac.usgs.gov/, accessed on 21 October 2022), and the code used in this study are openly available at https://github.com/xiyanbiao (accessed on 21 October 2022).

**Acknowledgments:** The authors appreciate the colleagues for cooperation on field campaign and measurements.

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