## 4 Discussion  
### Relationship between Variables and Permafrost Prior Data  
Both the training samples composed of permafrost prior data and the selected characteristic variables play an important role in the results of the model. The suitable training samples and characteristic variables can significantly improve the calculation efficiency and classification accuracy of the model. This section discusses the relationship between the characteristic variables used and the permafrost prior data, the correlation between each characteristic variable, and the correlation between characteristic variables and PP. Permafrost prior data are very valuable measured data. The specific regularities between permafrost and characteristic variables are an important subject of permafrost research. They could be discovered by analyzing the differences between permafrost areas and non-permafrost areas (Figure 7). In the analysis of LST1, LST2, and LST3, the surface temperature distribution of the samples with permafrost are more concentrated, and the average level of the surface temperature is lower than that of samples without permafrost. This also proves that the surface temperature can reflect the occurrence state of permafrost. As for DEV, the DEV distribution of the samples without permafrost is more concentrated and lower than that of the samples with permafrost. It should be noted that the definition of permafrost has been supplemented with the concept of cryptic state, which also covers the part of it which, despite remaining at a temperature of \(\leq\)0 \({}^{\circ}\)C for a certain time, remains not frozen [70]. In the judgment of boreholes, the cryptic state may be mistaken for non-permafrost. So, the discussion of DEV needs further study [71]. Compared with the distribution of SR values of the two, the dispersion is similar, but it can be seen that the samples with permafrost are in the low-value range; on the contrary, the solar radiation values of the samples without permafrost are in the relatively high-value range. This is consistent with the trend of permafrost degradation in Northeast China [24]. In this study, the difference of SR between permafrost and non-permafrost samples is the largest, and the overlap part is the least. Therefore, in similar studies, SR may become a significant distinguishing variable of permafrost areas. As for NDVI and NDWI, it can be seen that, for samples without permafrost, there is a small range of concentration at the low-value range of NDVI and the high-value range of NDWI, which is due to the addition of some water bodies in the samples without permafrost [72]. To sum up, it can be considered that the selected seven characteristic variables are representative and offer a good distinction between permafrost areas and non-permafrost areas in the study area. In addition, soil types are not considered as a characteristic variable. It is because the geological conditions are very complex in the study area [73], and the resolution of the available soil types is poor, which does not meet the needs of high-resolution mapping. If conditions permit, in a follow-up study or the study of other areas, soil type or other new characteristic variables should be added. From Table A1, it can be seen that PP was negatively correlated with LST1, LST2, LST3, and SR and positively correlated with DEV. This means that, the lower the surface temperature in spring, autumn, and winter, the lower the solar radiation value, the higher the soil moisture, and the higher the PP. It should be noted that there is a negative correlation between PP and NDVI. In combination with the current environmental conditions in the study area, it is speculated that the process of permafrost degradation will be accompanied by changes in tree species from coniferous forest to broad-leaved forest, and the results of these changes will lead to an increase in NDVI [74].