## 6 Conclusions  
A first assessment of the freely available DEMs over the south-central Chinese province of Hunan was presented through a comparison with the highly accurate ICESat-2 altimetry points. Meanwhile, the effects of the terrain morphological characteristics (e.g., elevation, slope and aspect) and land cover type were also analyzed in the presented study.  
The assessment result shows that, among the 30 m models, SRTM1 DEM performed best, followed by AW3D30 DEM and ASTER GDEM2 performed worst. SRTM1 DEM has been extensively used, and provided a good representation of the topography. AW3D30 DEM, however, shows a very uneven quality and can be used in the areas without SRTM1 DEM. It is noted that the selection of reference data can impact the analysis results as described in Section 5.1. Therefore, high-precision and independent reference data is important to assess the DEM accuracy. Among the 90 m models, the recently released TDX90 DEM shows higher quality than the SRTM3 DEM, which confirms the advantages of fusing multi-baseline or ascending and descending viewing geometry for producing InSAR DEM. However, the influence of vegetation change in height over a period of more than a decade still is not excluded.  
Results furthermore show that DEM errors significantly increased with increasing slope value above 6\({}^{\circ}\), which can be used to corrected the systematic errors caused by terrain slope angles [53]. However, no relationship was found between the vertical error and terrain aspect, whereas the DEM vertical accuracy significantly decreases as the land cover ranges from non-vegetated to vegetated.  
Overall, ASTER GDEM2 performed worst, while SRTM1 DEM showed a very robust performance. Thus, SRTM1 is the most promising dataset with high accuracy and spatial resolution, and thus the future applications should utilize SRTM1 DEM. AW3D30 DEM, however, is another good choice for 1-arc-second resolution. The findings of this study are important to understand the error associated with these freely available DEMs. However, due to few ICESat-2 sampling data, it is difficult to unravel the influence of each factors (e.g., elevation, slope, land cover type) on DEM accuracy. In addition, multitemporal land cover type map is also crucially important to explore the influence of land cover changes on the results obtained. Therefore, further studies and analysis need to answer these questions.  
Conceptualization, Z.L. and H.F.; Methodology, Z.L.; Software, H.F.; Validation, Z.L., C.Z., J.Z., and T.Z.; Formal Analysis, Z.L., J.Z. and T.Z.; Investigation, C.Z.; Resources, J.Z.; Data Curation, J.Z. and H.F.; Writing--Original Draft Preparation, Z.L.; Writing--Review & Editing, Z.L., H.F., and J.Z.; Supervision, J.Z.; Project Administration, C.Z. and J.Z.; Funding Acquisition, J.Z. and H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant 41820104005, Grant 41531068, Grant 41904004, Grant 41604012, and in part by the Young Elite Scientists Sponsorship Program by Human province of China (No. 2018 RS3093) and Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring (2020-13).  
The authors thank the data distribution agencies for providing data: (1) SRTM and ASTER GDEM data were obtained from the USGS Earthexplorer site; (2) TanDEM-X DEM was obtained from German Aerospace Center (DLR) data website. (3) AW3D30 DEM was provided by the Japan Aerospace Exploration Agency (JAXA); (4) ICESat data was provided by the National Snow and Ice Data Center; (5) 30-m Global Land Cover Dataset was provided by the National Geomatics Center of China. Figure 1 were generated by Generic Mapping Tool software [66].  
The authors declare no conflict of interest.