The freeze–thaw cycle can change the size and shape of soil particles, that is, cause fragmentation and aggregation of soil mineral particles, thereby affecting the soil structure, which, in turn, affects the soil properties. In addition, the process of soil mineral fragmentation and aggregation, as an event closely related to the cold climate, plays an irreplaceable role in reconstructing the climatic conditions of a certain area and a certain period of time and has important referential significance for the restoration of historical permafrost boundaries and the study of climate change. Therefore, in order to better study the effect of freeze–thaw cycles on the fragmentation and aggregation of soil mineral particles, four soil specimens were selected for this study; the freeze–thaw cycle tests were carried out 0, 3, 6, 9, 50, and 100 times, respectively, and the test results were analyzed. The results of these experiments indicate that (1) after freeze–thaw cycles, the changes in grain-size distribution are different in various soil types, and the changes are mainly concentrated in 0.001–0.1 mm; (2) the coarse particle size particles (sand, silt) can be fragmented into relatively fine-grained particles (silt, clay). The fine-grained particles (clay) cannot undergo fragmentation, and the decrease in clay is due to its aggregation; (3) with the increase in freeze–thaw cycles, the fraction change trend of four specimens is parallel to the x-axis, which means the fragmentation and aggregation of soil mineral particles are in a state of equilibrium or balance; (4) the increase in the K_var value indicates the change in grain-size distribution, which means aggregation and fragmentation of soil mineral particles. After the 100th cycle, the K_var value of four specimens is <0.1, which belongs to a low-intensity area; (5) the freeze–thaw cycle will change the type of soil, and after 100 freeze–thaw cycles, the soil type of specimen (H) changes from silty clay loam to silty clay; (6) during the freeze–thaw process, different types of soil have different situations of aggregation and fragmentation.

Groundwater icings, typical features of permafrost hydrology, are indicative of hydrothermal interactions between surface and ground waters, and permafrost. Their main morphological parameters, i.e., icing area and volume, are generally estimated with low accuracy. Only scarce field observational data on icing volume and seasonal development exist to date. Our study evaluates and compares performance of several widely used techniques of icing morphometric estimation, based on field data, collected on a giant Icing #2 in the Samokit River basin, southern Yakutia. Groundwater icing area was estimated by: (a) staking, (b) unmanned aerial vehicle (UAV) surveys, and (c) satellite imagery analysis. Icing #2 area in late February was between 1.38·10⁶ m² and 1.68·10⁶ m², icing volume, between 1.73·10⁶ m³ and 4.20·10⁶ m³, depending on the technique used. Staking is the least accurate, but also the only direct technique, which is hence used as a baseline tool in our study. Staking-based assessment of icing morphometry is the most conservative, while UAV-based estimates of icing area are higher by 14% to 17%, and of icing volume, by 74% to 142%, compared to staking. The latter appears, in our case, to be the least accurate method, although a direct one. It requires a sufficient number of staking points and transects, which should be set up to represent all icing zones, i.e., channel branches and alluvial islands. Photogrammetry based on UAV surveys has numerous advantages, i.e., higher precision of a per pixel icing volume calculation, based on an ice-free valley bottom digital surface model (DSM), and potential reusability of a resulting DSM. However, positioning precision suffers from the overlay of multiple flyovers required because of battery replacements, and, in our case, an insufficient number of ground control points. Satellite imagery along with B.L. Sokolov’s empirical approach were used to estimate the annual maximum icing area and volume, and the empirical estimates tend to converge to satellite-based values. Finally, all thing being equal, UAV-based photogrammetry shows higher precision in estimating the icing morphometrical parameters.