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The Arctic is one of the important drivers of global climate and environmental change. Its atmosphere, oceans, and sea ice movements have direct or indirect impacts on global atmosphere and ocean circulation as well as climate variability. Sea ice plays a crucial role in maintaining climate balance in the Arctic region, preventing more solar radiation from entering the sea through its high reflectivity and limiting air sea heat exchange, reducing sea surface temperature and sea ice melting rate. Sea ice thickness is an important parameter that describes the properties of sea ice. Obtaining the freeboard of sea ice through satellite altimetry measurements is of critical importance for sea ice thickness retrieval and understanding changes in Arctic sea ice. So far, research on satellite observations of pan-Arctic sea ice thickness has been limited to winter months. The key to measuring the freeboard using altimetry lies in distinguishing sea surface types and correctly identifying adjacent inter ice waterways. However, there are a large number of melting pools on the surface of summer floating ice, which make traditional waveform classification schemes unable to accurately distinguish sea surface types and become the main obstacle to retrieval freeboard of summer sea ice. In this study, a one-dimensional convolutional neural network classification model is built using CryoSat-2 summer sea ice classification training and testing dataset to improve summer sea ice freeboard retrieval. The model uses three parameters as input sources, i.e., elevation, pulse peakness, and backscatter coefficient. It achieves an overall accuracy of 84.3%. The sea ice freeboard iscalculated fromthe elevation difference between the ice-covered waterways and itssurrounding floating ice, resulting in distributionsof 15-dayindividual sea ice freeboard and sea ice freeboard on an80-km resolution grid. The results show that although there are manymissing data due to noise and other issues, effective sea ice freeboard can be obtained in all months in summer.It demonstratesthe feasibility of this method.
Xinran Yang (Sat,) studied this question.