A Novel Snow Depth Retrieving Approach Using Time-Series Clustering in GPS-IR Data

Affected by the surrounding environment of the station and global positioning system (GPS) satellite signal receiving conditions, there are significant fluctuations in the snow depth retrieval results of the GPS interferometric reflection (GPS-IR) based on GPS data acquired in different trajectories or frequency bands, resulting in the loss of snow depth retrieval accuracy and reliability. Therefore, this contribution proposes a novel snow depth retrieving approach using time-series clustering (TSC) in GPS-IR data. For improving the adaptability of the algorithm to different application scenarios, dynamic time warping (DTW) is introduced to perform K-medoids clustering on the snow depth retrieval sequence of all available satellite trajectories, and the daily time-series snow depth retrieval results are obtained based on the clustering center evaluation index. For validation purposes, multifrequency GPS data were selected for snow depth retrieval. The results indicate that, compared with traditional approaches, the proposed TSC method demonstrated higher robustness and accuracy, with correlation coefficients reaching above 0.975 in all frequency bands. The root mean square difference (RMSD) for snow depth retrieval at P351 and AB39 stations, using GPS L1 frequency data, was 8.772 and 6.095 cm, respectively. The TSC algorithm proposed in this letter classifies the snow depth retrieval sequences of different trajectories, achieving an adaptive acquisition of the optimal snow depth retrieval sequence, thus greatly reducing the workload of manual intervention and preliminary data quality assessment in GPS-IR snow depth retrieval. The proposed GPS-IR snow depth retrieval algorithm may offer a new technical approach for future related research.