Radar altimetry for classifying surface conditions of subarctic lakes during freezing and thawing periods

Ice cover on subarctic lakes is an important indicator of climate change at local- or regional scales. This study proposes a new approach for classifying altimetry data from Jason-2 and SARAL/Altika satellite missions to characterize surface states of subarctic lakes. It focuses on Great Slave Lake (Canada) during freeze-up and thaw periods. For the first time, parameters from altimetry waveforms were used in an unsupervised clustering to establish distinct clusters from waveforms observed from Jason-2 and SARAL/Altika during the freeze-up and the thaw period. Clusters are assigned to the different surface states (open water, pure ice and leads) based on a priori altimetry and radiometric information. The statistics of these clusters were then used to construct two trained models of supervised classification based upon KNN (K-nearest neighbour) and SVM (support vector machine). The SVM-based model yielded the best results (accuracy of 92% with Jason-2, and 98% with SARAL/Altika). It was used to classify all waveforms considered in the study from the nominal orbits of Jason-2 (2008–2016) and SARAL/Altika (2013–2016). Results were superimposed onto Moderate Resolution Imaging Spectroradiometer (MODIS) products for qualitative visual and semi-quantitative assessments.