Abstract: Channels often freeze in cold regions in winter. A control system of horizontal freezing always causes a significant decrease in the water delivery capacity of channels. Improper regulation may even lead to disasters, such as ice jams and ice dams. A large number of studies on the channel ice conditions have been conducted to improve the water delivery capacity and safety during the ice period. However, it is still lacking in the scheduling decisions from the verified numerical simulation, particularly for the spatiotemporal density of observation data. Remote sensing has great potential for the channel ice monitoring, due to the wide range of observation and high timeliness. This study aims to explore the suitable method for the remote sensing monitoring of channel ice conditions in cold regions. The open channel was taken from the Beijing-Shijiazhuang section in the middle route of the south-to-north water transfer project. Three types of feature space datasets were constructed using 11 bands of Sentinel-2 images, including the complete feature, the optimized feature, and the combined feature. These datasets were selected as the inputs for the classification of the support vector machine (SVM), maximum likelihood estimation (MLE), and random forest (RF). Nine classifiers were trained to identify the freezing range of the channel. An experiment was conducted to extract the freezing range of the channel from the images of the Beijuma check-gate. The performance of classification was compared under different classifications and inputs. The results indicated that the near-infrared (NIR), visible light (R, G, and B), and shortwave infrared (SWIR) were the key bands to recognize the range of the channel freezing. The highest accuracy was achieved in the SVM using the limited sample size, with a producer's accuracy (PA) of 85.10%-87.91% and a commission error (CE) of 10.84%-16.08% under different feature inputs; The accuracy of RF classification was close to that of SVM using complete and optimized feature, with PA=84.67%-86.61%, and CE=13.76%-14.41%. But the classification was seriously deviated from the reality under combined features; The MLE classification shared the low accuracy under all three types of features, indicating unsuitable for the recognition of channel icing range. Overall, the SVM had a low sensitivity to the feature space, whereas, the high-precision observation was achieved in the range of channel freezing under different feature inputs; The RF shared a high sensitivity to feature space, where the unstable accuracy was observed when the input features changed. Taking the SVM with complete features as an example, the spatiotemporal generalization of the classifier was finally verified for the model. The lowest mapping accuracy of 82.09% and the highest misclassification error of 13.82% were achieved at different times and channel segments. The classification model with the better accuracy can be expected to effectively identify the icing range of the channel. The finding can provide a new approach to monitoring the ice conditions in water transfer projects in cold regions. The application scenarios of satellite remote sensing can also be extended to the strong reference in the field of ice monitoring.