11 citations as recorded by crossref.

1. ConvLSTM-Based Wave Forecasts in the South and East China Seas S. Zhou et al. 10.3389/fmars.2021.680079
2. A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al. 10.1029/2020EA001176
3. Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression H. Han et al. 10.3390/rs13122283
4. Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function Q. Liu et al. 10.3389/fmars.2021.736429
5. Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction J. Chi et al. 10.3390/rs13173413
6. Calibration of sea ice drift forecasts using random forest algorithms C. Palerme & M. Müller 10.5194/tc-15-3989-2021
7. Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
8. On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery A. Davari et al. 10.1109/TGRS.2021.3115883
9. Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models H. Choe et al. 10.3390/rs13132490
10. Neural Network Prediction for Ice Shapes on Airfoils Using iceFoam Simulations S. Strijhak et al. 10.3390/aerospace9020096
11. Seasonal Arctic sea ice forecasting with probabilistic deep learning T. Andersson et al. 10.1038/s41467-021-25257-4