12 citations as recorded by crossref.

1. Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice) U. Herzfeld et al.
2. Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models Z. Li et al.
3. A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets P. Arndt & H. Fricker
4. Monitoring Earth’s climate variables with satellite laser altimetry L. Magruder et al.
5. Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges U. Herzfeld et al.
6. Sea ice concentration estimates from ICESat-2 linear ice fraction – Part 1: Multi-sensor comparison of sea ice concentration products E. Buckley et al.
7. Ice, Cloud, and Land Elevation Satellite-2 Photon-Counting Lidar for 3-Dimensional Ocean Observation: A Comprehensive Review of Algorithms, Applications, and Synergistic Opportunities P. Chen et al.
8. Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models C. Xiong & X. Li
9. Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice G. Dawson & J. Landy
10. Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data N. Fuchs et al.
11. An automated method for estimating the depth of melt ponds using ICESat-2 LiDAR point cloud data: application to surface melt of Arctic sea ice X. Shen et al.
12. Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al.