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Continuous observation of climate indicators, such as trends in lakefreezing, is important to understand the dynamics of the local and globalclimate system. Consequently, lake ice has been included among the EssentialClimate Variables (ECVs) of the Global Climate Observing System (GCOS), andthere is a need to set up operational monitoring capabilities. Multi-temporalsatellite images and publicly available webcam streams are among the viabledata sources to monitor lake ice. In this work we investigate machinelearning-based image analysis as a tool to determine the spatio-temporal extentof ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from bothmultispectral optical satellite images (VIIRS and MODIS) and RGB webcam images.We model lake ice monitoring as a pixel-wise semantic segmentation problem,i.e., each pixel on the lake surface is classified to obtain a spatiallyexplicit map of ice cover. We show experimentally that the proposed systemproduces consistently good results when tested on data from multiple wintersand lakes. Our satellite-based method obtains mean Intersection-over-Union(mIoU) scores >93%, for both sensors. It also generalises well across lakes andwinters with mIoU scores >78% and >80% respectively. On average, our webcamapproach achieves mIoU values of 87% (approx.) and generalisation scores of 71%(approx.) and 69% (approx.) across different cameras and winters respectively.Additionally, we put forward a new benchmark dataset of webcam images(Photi-LakeIce) which includes data from two winters and three cameras.

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning