## 4 DEM Validation Based on ICESat-2 Data
### Vertical Accuracy Versus Land Cover  
Covering various regions of Hunan province, these datasets allow further study of the relationship between the DEM accuracy and terrain type and ground cover. The global land-cover model referenced in this study is the GlobaLand30 dataset produced by National Geomatics Center of China (NGCC), which represents one of the first efforts in mapping a global land cover at 30 m spatial resolution [57]. GlobaLand30 defines ten land cover categories with an overall classification accuracy of 83.51%, which has been validated [58]. In this study area, six land cover types were identified according to the distribution of ICESat-2 reference data, which are cultivated land, forest, grassland, wetland, water body, as well as artificial surfaces. We assumed that the land cover types do not change significantly during the image acquisition  
Figure 5: DEM error statistics of different slopes; ME (top), RMSE (middle) and STD (bottom). (1: 0–2\({}^{\circ}\); 2: (2–6\({}^{\circ}\)]; 3: (6–25\({}^{\circ}\)); 4: \(>\)25\({}^{\circ}\)).  
Figure 7 shows the error statistics from all DEMs over different land cover types. The ME values (except water body of SRTM1, SRTM3 DEM and AW3D30 DEM, and the wetland of AW3D30 DEM) are negative for land cover types among all DEMs, and that of forest is higher than that of other classes. This result is related to the terrain characteristics of different land cover types. However, the variation of the MEs of the water body is related to the fluctuations of water levels. As the RMSE shows, the forest has the poorest accuracy, followed by grassland, and the cultivated land, wetland and water bodies. Artificial surfaces have the highest accuracies. Hence, the DEM accuracy increases as the land cover changes from fully vegetated to non-vegetated areas.  
For DEM at 30 m resolution, SRTM1 offers the best quality in each land cover class. As AW3D30 has been calibrated based on ICESat/GLAS data, it shows a better quality than ASTER GDEM2 and TDX30 DEM. Furthermore, we can find that AW3D30 DEM shows a better accuracy over the forest area and a lower vertical accuracy than that of SRTM1 DEM over the wetland, this phenomenon can be attributed to the fitting method used for AW3D30 calibration, which raises the terrain height in  
Figure 6: The scatter plots between DEM error and terrain aspect for (**a**) SRTM1; (**b**) SRTM3; (**c**) ASTER; (**d**) AW3D30; (**e**) TDX30 and (**f**) TDX90.  
Figure 7: The error statistics in different land cover classes: ME (top), RMSE (middle) and STD (bottom) values of different DEMs. CL: cultivated land, FR: forest, GL: grassland, WL: wetland, WB: water body, AS: artificial surfaces.  
lower areas (e.g., wetland) in the removal of the vegetation height signals (forest). In addition, both the ASTER and AW3D30 show lower quality than that of InSAR DEM over the region of artificial surfaces. The accuracies of the 90-m DEMs (both SRTM3 and TDX90) are nearly the same for all land cover types, except for the water body. SRTM1 performs a little better than 90 m SRTM3, because of its higher resolution. However, the resolution seems have no influence on the accuracy of TDX30 and TDX90. According the result, the following conclusion is drawn, in the case of the same land cover type, the resolution has little influence on the DEM accuracy. Finally, STD in the lower panel of Figure 7 shows similar trends as RMSE.