## 4 Discussion
### Comparison with Existing Maps  
Former studies have offered soil texture information at multiple spatial scales [8; 22; 73; 74; 75; 76]. However, due to the lack of field observations, the availability of these datasets in permafrost areas was difficult to evaluate. The PSFs were extracted from three available datasets (Section 2.2.3), and their descriptive statistics indicated that the silt and sand content at BQ and WQ were comparable (Table 4). While the descriptive statistics of observations indicated the silt content at BQ and WQ was quite different (Table 2). Hence, the three legacy datasets failed in depicting the spatial variance of soil PSFs, especially the higher content of sand in the WQ region. The scatter plots (Figure 4) show that the SoilGrids250m, CSCD, and CDSL overestimated the clay content and cannot capture the silt and sand variance in BQ. Besides, all three compared datasets overestimated silt fraction and underestimated sand fraction in WQ (Figure 5). They could not capture the variance of clay either. Figure 4: Comparison of soil particle size fractions at the Budongquan-Qingshuihe (BQ) region derived from four datasets. Figure 5: Comparison of soil particle size fractions at the Wenquan (WQ) region derived from four datasets. According to the spatial distribution of soil PSFs in this study, the clay and silt content increased from northwest to the southeast in both BQ and WQ (Figures 6 and 7). We notice that the CSCD and CDSL also showed a similar trend as ILR-RF prediction at RQ, while SoilGrids250m did not (Figure 6). The spatial patterns were not that consistent with ILR-RF at WQ (Figure 7). The assessment of the three legacy datasets showed low prediction accuracy on soil PSFs (Table 5). There are some reasons to explain the low predictive power of the three legacy datasets. Firstly, observations from permafrost regions are quite deficient. The CDSL and CSCD datasets were developed on the Chinese soil profile database, which barely included observations in the permafrost distributed region. The SoilGrids250m used this database as well. Even though some vacant areas were filled with pseudo-points by expert knowledge [8], the inadequate soil samples result in insufficient learning process in model fitting. Secondly, the methodology implemented in these soil datasets were different. The CDSL and CSCD datasets used the polygon linkage approach while SoilGrids250m and our study employed the machine learning approaches. From the perspective of the earth system model, [PERSON] et al. [76] have reviewed global soil properties maps and compared the maps generated by polygon-linkage methods with those generated by DSM. They found that the soil datasets produced by these two methods are quite different. These datasets may not be comparable since the linkage method results in an abrupt change between the boundaries of soil polygons, while the DSM simulated the soil properties with a continuous spatial change. In that way, the DSM-derived datasets can provide a more realistic estimation of soils than those derived by linkage methods. Even so, [PERSON] et al. [77, 78] argued that spatial information systems for polygons and grids complement various applications of