To accurately simulate the changes in hydrothermal characteristics of permafrost on the plateau under the influence of climate change, a set of soil texture data for permafrost areas is urgently needed. The compositional nature, i.e., non-negativity, constant sum, unbiasedness prediction, and minimum prediction error variance [11], makes soil PSFs distinctive from other soil properties [12]. Traditional geostatistical techniques, such as ordinary kriging, are not directly applicable due to theoretical and practical problems [13; 14]. Log-ratio transformation methods, such as additive log-ratio, centered log-ratio, symmetry log-ratio, and isometric log-ratio transformations [12; 15; 16] can be implemented with geostatistical methods, or machine learning approaches in PSFs prediction to obtain promising results [1; 14; 17]. The digital soil mapping (DSM) framework, formalized by [PERSON] et al. [18], offers a spatial model to quantitatively express the relationship between a soil property or class and environmental variables for a given spatial location [18; 19; 20]. A considerable demand for quantitative and spatial soil information impulses DSM to be extensively applied in predicting multiple soil properties at various spatial scales [21; 22; 23]. Since machine learning algorithms are not conditioned to follow any statistical assumptions, their predictions often appear more accurate than those made by conventional models [19]. [PERSON] et al. [24] reviewed DSM cases and reported the strength and weaknesses of six commonly used machine learning algorithms. The comparison showed that RF and Cubist were outstanding since they not only excel at dealing with small sample size, also had better interpretability of the resulting model. A conflict lies in the covariate selection for digital soil mapping. On one hand, environmental covariates are often collected comprehensively to minimize the subjectivity in covariates collection and improve the model performance [21]. On the other, abundant variable information can bring about data redundancy and interfere with the learning process, affecting model performance [21; 25]. Hence, covariate selection is an essential step in DSM modeling [26]. Covariate selection generally involves two problems, i.e., minimal-optimal and all-relevant. The former aims to search for the minimum set of predictor variables yielding the best prediction accuracy [27; 28], while the latter is focused on finding all-relevant variables to the target property [29]. Therefore, the minimal-optimal set is of particular interest in developing predictive models, while the all-relevant set has great value in understanding the mechanisms underlying the soil-environment relationship [21]. In permafrost regions of QTP, most vegetation communities have a shallow root system [30]. The soil organic carbon is mainly concentrated in the topsoil (i.e., 0-30 cm) and declines rapidly with depth [31; 32], leading to the high soil porosity and water retention capability of the topsoil on the QTP [33; 34]. Some commonly used datasets (e.g., GLDAS-Noah dataset and the China dataset of soil properties for land surface modeling) underestimate sand content and overestimate clay content on QTP, which results in the overestimation of soil moisture. As primary input for all kinds of land surface models, accurate estimation of the spatial distribution of soil PSFs is an urgent need for understanding the hydrothermal processes in permafrost regions under climate change. However, due to the harsh environment and complex landscape, in situ observations at the permafrost distributed areas are scarce [5; 35], making a well-focused dataset of soil PSFs in this area continue to be challenging work. Therefore, this study focused on the surficial layer and integrated the log-ratio transformations, covariate selection, and machine learning techniques to generate the spatial distribution of soil PSFs in two permafrost regions.