Abstract
Retrieval of glacier surface heights from ICESat-2 photon-counting data is significant to monitor glacier surface morphologies (e.g., crevasses) and their changes. However, existing methods are susceptible to complex glacier surfaces and diverse signal-to-noise ratios (SNRs). Therefore, we propose a robust density estimation method for glacier-height retrieval from ICESat-2 photon-counting data. A multi-scale random sample consensus (RANSAC) strategy is firstly employed to obtain the best elliptical neighborhood for each photon, to improve the consistency of neighboring photons. Then, a hybrid weighted density estimation method is developed to robustly describe the differences in the spatial distribution patterns between signal and noise photons. High-quality extraction of signal photons is finally implemented using adaptive thresholds in the local along-track segments. To test its performance, four datasets with strong and weak beams from the Jakobshavn Isbræ Glacier, western Greenland, were selected. Results showed that the proposed method achieved excellent performances in glacier-height retrieval in various glacier surface morphologies and diverse SNRs, due to discriminative densities between noise and signal photons were obtained. The average percentages of along-track segments with signal densities larger than noise densities for weak and strong beams were 92.0% and 99.9%. The average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F<sub>1</sub>-scores</i> reached 0.927 and 0.968 for the weak and strong beams, even the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F<sub>1</sub>-score</i> reached 0.892 for the heavily crevassed complex surface with a low SNR. Moreover, comparison experiments with existing methods demonstrated that the proposed method showed superior performances in density estimation and glacier-height retrieval, and improved more than 17.33% and 16.10% in the most complex surface, respectively.
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