The Impact of Noise on Machine Learning-Based Lake Ice Detection on Lake Śniardwy Using Sentinel-1 SAR Data

Lake ice monitoring is critical for assessing climate change, but in-situ observations are often limited. Sentinel-1 Synthetic Aperture Radar (SAR) data is a strong method for ice detection because it is not restricted by cloud cover and it is readily available. However, SAR-based classification can be affected by atmospheric and surface-related noise. This study examines the impact of noise on machine learning-based lake ice detection over Lake Śniardwy, Poland, using Sentinel-1 Vertical-Vertical (VV) and Vertical-Horizontal (VH) backscatter data. Binary logistic regression models were trained on scenes with strong class separability between ice and water and then validated on separate low- and high-noise datasets. The models achieved high accuracy under low-noise scenes, reaching up to 96.9%, but performed poorly on high-noise scenes. The results show that wind-related surface roughness and associated atmospheric conditions can significantly reduce classification reliability. Comparison with backscatter from a nearby coniferous forest confirmed that the main disturbances were concentrated over the lake surface. The study highlights the importance of careful scene selection and noise assessment in SAR-based lake ice classification.