Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena.

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s⁻¹ and −0.141 to 0.222 s⁻¹, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions.

The spatial distribution of river ice types during the freeze-up period provides important information on channel freezing and ice-cover development and serves as a basis for ice-condition monitoring and ice-jam flood risk assessment. However, river ice types differ considerably in spatial scale, spectral response, and surface structure, making their fine-scale identification under complex ice conditions challenging. In this study, nine representative frozen reaches along the Inner Mongolia section of the Yellow River were investigated using UAV multispectral orthomosaics with a spatial resolution of approximately 0.15 m. Four representative river-surface classes—open water, transparent ice, smooth ice, and rough ice—were mapped. Their responses in the visible, red-edge, and near-infrared bands were analyzed together with spectral indices and gray-level cooccurrence matrix (GLCM) texture features to characterize interclass spectral and textural differences. A deep-learning mapping model integrating multispectral and textural information was subsequently developed and evaluated on spatially independent reaches. Permutation importance was also used to quantify the contribution of each remote-sensing feature. The results showed that the four surface classes exhibited distinct spectral and textural characteristics. Transparent ice and open water showed limited spectral differences in several bands, whereas texture features captured differences in their local spatial structures and improved the model’s ability to distinguish spectrally similar classes. The model combining spectral indices and GLCM texture features accurately delineated the spatial distributions of the river ice types. In the spatially independent test reaches, it achieved an overall accuracy of 95.80%, a Macro-F1 score of 94.53%, and a mean intersection over union (mIoU) of 90.14%. The model achieved an mIoU of 84.55% on an additional independent test reach with complex ice conditions, which was excluded from training and parameter optimization. Feature-contribution analysis identified spectral indices and texture features as important sources of discriminatory information. NDUWI and NDFSI, together with texture measures calculated from the red and near-infrared bands, ranked among the most informative inputs. This result suggests that the four river-surface classes were distinguished not only by their spectral differences but also by variations in local surface patterns. The UAV imagery therefore supported detailed mapping of heterogeneous frozen reaches and provided spatially explicit information relevant to ice-condition monitoring, analysis of river-ice development, and assessment of potential ice-jam flooding.

The formation and evolution of ice in the Yellow River represent complex dynamic processes. To elucidate the structural characteristics of ice crystals and their governing mechanisms in the Inner Mongolia reach, this investigation utilized high-resolution Sentinel-2 satellite imagery to systematically monitor spatiotemporal variations in open-water formations across diverse channel morphologies throughout the ice regime period. Systematic ice sampling was conducted across diverse channel morphologies of the Yellow River to quantify critical parameters, including crystalline structure characteristics, equivalent diameter distributions, density variations, and sediment content profiles. The results indicate the transformation of open water resulting from various river configurations during the freezing season exhibits distinct characteristics, which are significantly influenced by temperature variations. Ice crystal characterization exhibits that the crystalline structure predominantly manifests as two primary forms: columnar and granular ice formations, with their distribution varying systematically across different channel configurations. Ice crystal morphology exhibits heterogeneity in both form and dimensional characteristics. Columnar ice consistently exhibits larger equivalent diameters compared to granular ice formations. A progressive enhancement in the equivalent diameter of crystals is observed along the vertical axis corresponding to the thickness of the ice during the growth process. The ranges of variation in ice crystal size, ice density, and mud content within ice exhibit differences contingent upon the specific crystal structures present. Observational studies and comparative analyses of ice samples from the Inner Mongolia reach of the Yellow River reveal that channel morphology, ambient thermal conditions, and hydrodynamic parameters are the primary determinants governing the variability in ice microstructure and its associated physical characteristics. This investigation provides fundamental scientific insights and quantitative data that advance our understanding of river ice microstructural characteristics.