**5. Conclusions**

Most of current forest condition assessments are mainly based on structural and functional parameters investigated in the field. To evaluate forest conditions in a comprehensive and comparable manner, this study developed a methodology on forest condition assessment based on explicit modeling and mapping of forest parameters from satellite images. E fficient predictors and algorithms were implemented to map structure and function parameters in the CMNNR of 2017 based on ALOS-2, Sentinel-1, Sentinel-2, and DSM from ALOS. With parameter modeling, this study assessed forest conditions to provide a foundation of methodology and up-to-date information of the CMNNR.

The results included performances of predictor variables and models on spatial modeling of the structure and function, maps of forest parameters, and conditions. First, explicit relationships between Sentinl-2-derived biophysical variables and simple forest structure parameters such as canopy closure and stand density were discovered. Topographic and spectral indices from L band InSAR and MSI contributed more than L and C band SAR in RF modeling of complex forest parameters such as forest age and soil fertility. While backscatters of HV were more important in the RF modeling of stand volume, forest age, and soil fertility than those of HH. Meanwhile, backscatters of VV were more

sensitive to forest age and soil fertility than those of VH. Models explained spatial dynamics and characteristics of forest parameters to a good extent based on the independent validation set (*r* ≥ 0.75). Second, all maps of forest parameters showed that the lower altitude northern slope had larger values than the south. Third, the mean score of forest conditions in the CMNNR was 58.51, with the smallest in the core zone (56.96) and the largest in the transition area (63.23). The assessment illustrated that the distribution of forest conditions in the CMNNR mainly resulted from spatial variations of function parameters including stand volume and soil fertility.

**Author Contributions:** L.C., C.R., and B.Z. designed this research. L.C. conducted field sampling, performed the experiments, conducted the analysis and drafted the manuscript. Y.W. supervised preparation of the manuscript. L.C., Y.W., C.R., B.Z., and Z.W. revised and finalized the manuscript.

**Funding:** This study is supported by the National Key Research and Development Project of China (No. 2016YFC0500300), the Jilin Scientific and Technological Development Program (No. 20170301001NY), the funding from Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2017277, 2012178) and National Earth System Science Data Center of China. The principal author appreciates the scholarship provided by the China Scholarship Council (CSC) (No. 201804910492) for her study in the University of Rhode Island.

**Acknowledgments:** We appreciate critical and constructive comments and suggestion from the reviewers that helped improve the quality of this manuscript. The authors are grateful to the support from colleagues and local forestry bureau who participated in the field surveys and data collection. We thank the National Earth System Science Data Center (http://www..geodata.cn) for providing geographic information data. This study is supported by the National Key Research and Development Project of China (No. 2016YFC0500300, the Jilin Scientific and Technological Development Program (No. 20170301001NY), the funding from Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2017277, 2012178) and National Earth System Science Data Center of China. The principal author appreciates the scholarship provided by the China Scholarship Council (CSC) (No. 201804910492) for her study in the University of Rhode Island.

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