Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5277-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5277-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2024
Research article |
| 19 Nov 2024
| 19 Nov 2024
Pan-Arctic sea ice concentration from SAR and passive microwave
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Jørgen Buus-Hinkler
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Suman Singha
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Hoyeon Shi
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
Matilde Brandt Kreiner
National Center for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark
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Cited articles
Allen, M. J., Dorr, F., Gallego, J. A., Martínez-Ferrer, L., Kalaitzis, F., Ramos-Pollan, R., and Jungbluth, A.: Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data, in: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, https://www.climatechange.ai/papers/neurips2023/76 (last access: 8 November 2024), 2023. a, b
Allen-Zhu, Z. and Li, Y.: Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2012.09816, 2020. a
Andersen, S., Tonboe, R., Kaleschke, L., Heygster, G., and Pedersen, L. T.: Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice, J. Geophys. Res.-Oceans, 112, C08004, https://doi.org/10.1029/2006JC003543, 2007. a
Asadi, N., Scott, K. A., Komarov, A. S., Buehner, M., and Clausi, D. A.: Evaluation of a Neural Network With Uncertainty for Detection of Ice and Water in SAR Imagery, IEEE T. Geosci. Remote, 59, 247–259, https://doi.org/10.1109/TGRS.2020.2992454, 2021. a
Baordo, F., Vargas, L., and Howe, E.: Algorithm Theoretical Basis Document for Global Sea Ice Concentration Level 2 and Level 3 (OSI-410-a, OSI-401-d, OSI-408-a), https://osisaf-hl.met.no/sites/osisaf-hl/files/baseline_document/osisaf_atbd_ice-conc_l2-3_v1p3.pdf (last access: 8 November 2024), 2023. a
Boulze, H., Korosov, A., and Brajard, J.: Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks, Remote Sens., 12, 13, https://doi.org/10.3390/rs12132165, 2020. a, b
Bourbigot, M., Vincent, P., Johnsen, H., and Piantanida, R.: Sentinel-1 IPF Auxiliary Product Specification, https://sentinels.copernicus.eu/documents/247904/1877131/DI-MPC-PB-0241-3-11-1-Sentinel-1IPFAuxiliaryProductSpecification.pdf/c31f63fa-2db5-d19e-20b0-45e9079d7a04?t=1697806798118 (last access: 8 November 2024), 2023. a
Buus-Hinkler, J., Wulf, T., Stokholm, A. R., Korosov, A., Saldo, R., and Pedersen, L. T.: AI4Arctic/ASIP Sea Ice Dataset – version 2, Technical University of Denmark [data set], https://doi.org/10.11583/DTU.c.6244065.v2, 2022. a, b, c
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A.: Emerging Properties in Self-Supervised Vision Transformers, arXiv [preprint], https://doi.org/10.48550/arXiv.2104.14294, 2021. a, b
Caruana, R.: Multitask Learning, Mach. Learn., 28, 41–75, https://doi.org/10.1023/A:1007379606734, 1997. a
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.-Atmos., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355, 1984. a
Chen, X., Scott, K. A., and Clausi, D. A.: Uncertainty Analysis of Sea Ice and Open Water Classification on SAR Imagery Using a Bayesian CNN, in: Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, edited by: Rousseau, J.-J. and Kapralos, B., Springer Nature Switzerland, Cham, 343–356, ISBN 978-3-031-37731-0, 2023. a
Cheng, A., Casati, B., Tivy, A., Zagon, T., Lemieux, J.-F., and Tremblay, L. B.: Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2, The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, 2020. a
Dawkins, H. and Nejadgholi, I.: Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Volume 2: Short Papers, edited by: Muresan, S., Nakov, P., and Villavicencio, A., Association for Computational Linguistics, Dublin, Ireland, 538–544, https://doi.org/10.18653/v1/2022.acl-short.59, 2022. a
EUMETSAT: Global Sea Ice Concentration (AMSR-2), EUMETSAT [data set], https://osi-saf.eumetsat.int/products/osi-408-a (last access: 8 November 2024), 2023. a
European Space Agency: Sentinel-1 SAR User Guide, https://sentiwiki.copernicus.eu/web/s1-products (last access: 8 November 2024), 2023. a
Feng, T., Liu, X., and Li, R.: Super-Resolution-Aided Sea Ice Concentration Estimation From AMSR2 Images by Encoder–Decoder Networks With Atrous Convolution, IEEE J. Sel. To. Appl. Earth Obs., 16, 962–973, https://doi.org/10.1109/JSTARS.2022.3232533, 2023. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a
Frenkel, L. and Goldberger, J.: Network Calibration by Class-based Temperature Scaling, in: 2021 29th European Signal Processing Conference (EUSIPCO), 1486–1490, https://doi.org/10.23919/EUSIPCO54536.2021.9616219, 2021. a
Fuller, A., Millard, K., and Green, J. R.: SatViT: Pretraining Transformers for Earth Observation, IEEE Geosci. Remote Sens., 19, 1–5, https://doi.org/10.1109/LGRS.2022.3201489, 2022. a
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. B.: Masked Autoencoders Are Scalable Vision Learners, arXiv [preprint], https://doi.org/10.48550/arXiv.2111.06377, 2021. a, b, c
Hendrycks, D. and Gimpel, K.: Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units, arXiv [preprint], https://doi.org/10.48550/arXiv.1606.08415, 2016. a
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K.: Deep Networks with Stochastic Depth, Computer Vision – ECCV 2016, Springer International Publishing, 646–661, https://doi.org/10.1007/978-3-319-46493-0_39, 2016. a
International Ice Charting Working Group: IICWG Task Team 4 & 12 on Uncertainty, https://nsidc.org/noaa/iicwg/task-teams (last access: 8 November 2024), 2021. a
International Maritime Organization, Maritime Safety Committee: Guidance on Methodologies for Assessing Operational Capabilities and Limitations in Ice – Appendix - Methodology for Assessing Operational Capabilities and Limitations in Ice: Polar Operational Limit Assessment Risk Indexing System (POLARIS), https://www.imorules.com/GUID-2C1D86CB-5D58-490F-B4D4-46C057E1D102.html (last access: 8 November 2024), 2016. a
Ioffe, S. and Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, ICML'15, JMLR.org, 448–456, https://doi.org/10.5555/3045118.3045167, 2015. a
Iris, D. G., Colin, A., and Longépé, N.: Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network, IEEE J. Sel. Top. Appl. Earth Obs., 14, 5831–5841, https://doi.org/10.1109/JSTARS.2021.3074068, 2021. a
Jakubik, J., Roy, S., Phillips, C. E., Fraccaro, P., Godwin, D., Zadrozny, B., Szwarcman, D., Gomes, C., Nyirjesy, G., Edwards, B., Kimura, D., Simumba, N., Chu, L., Mukkavilli, S. K., Lambhate, D., Das, K., Bangalore, R., Oliveira, D., Muszynski, M., Ankur, K., Ramasubramanian, M., Gurung, I., Khallaghi, S., Hanxi, Li, Cecil, M., Ahmadi, M., Kordi, F., Alemohammad, H., Maskey, M., Ganti, R., Weldemariam, K., and Ramachandran, R.: Foundation Models for Generalist Geospatial Artificial Intelligence, arXiv [preprint], https://doi.org/10.48550/arXiv.2310.18660, 2023. a
Karvonen, J.: Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks, IEEE T. Geosci. Remote, 42, 1566–1574, https://doi.org/10.1109/TGRS.2004.828179, 2004. a
Karvonen, J.: Baltic Sea Ice Concentration Estimation Using SENTINEL-1 SAR and AMSR2 Microwave Radiometer Data, IEEE T. Geosci. Remote, 55, 2871–2883, https://doi.org/10.1109/TGRS.2017.2655567, 2017. a
Karvonen, J.: Baltic Sea Ice Concentration Estimation From C-Band Dual-Polarized SAR Imagery by Image Segmentation and Convolutional Neural Networks, IEEE T. Geosci. Remote, 60, 1–11, https://doi.org/10.1109/TGRS.2021.3097885, 2022. a
Karvonen, J., Vainio, J., Marnela, M., Eriksson, P., and Niskanen, T.: A Comparison Between High-Resolution EO-Based and Ice Analyst-Assigned Sea Ice Concentrations, IEEE J. Sel. Top. Appl. Earth Obs., 8, 1799–1807, https://doi.org/10.1109/JSTARS.2015.2426414, 2015. a, b
Khaleghian, S., Ullah, H., Kræmer, T., Hughes, N., Eltoft, T., and Marinoni, A.: Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks, Remote Sens., 13, 1734, https://doi.org/10.3390/rs13091734, 2021. a, b
Korosov, A., Johansson, M., Shuchman, R., Dierking, W., and Kwok, R.: SeaSAR Theme 4: Sea Ice, Sea Ice Drift, Sea Ice Type, Multi-sensor synergy, In situ campaigns, https://eo4society.esa.int/training_uploads/seasar2023/Seasar2023_Theme_4_Sea_Ice_Retrievals_part1.pdf (last access: 8 November 2024), 2023. a
Kortum, K., Singha, S., and Spreen, G.: Robust Multiseasonal Ice Classification From High-Resolution X-Band SAR, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2022.3144731, 2022. a, b
Kortum, K., Singha, S., Spreen, G., Hutter, N., Jutila, A., and Haas, C.: SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition, The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, 2024. a
Kreiner, M. B., Wulf, T., Jakobsen, J., Nielsen, A. A., and Pedersen, L. T.: Inter- and intra-analyst ice edge assessment, figshare [data set], https://doi.org/10.6084/m9.figshare.22312648.v1, 2023. a
Kull, M., Perello-Nieto, M., Kängsepp, M., Filho, T. S., Song, H., and Flach, P.: Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration, Proceedings of the 33rd International Conference on Neural Information Processing Systems, 1103, 11 pp., 2019. a, b
Lakshminarayanan, B., Pritzel, A., and Blundell, C.: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, Curran Associates Inc., 6405–6416, ISBN 9781510860964, 2017. a, b, c, d, e, f, g
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a
Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., and Xie, S.: A ConvNet for the 2020s, arXiv [preprint], https://doi.org/10.48550/arXiv.2201.03545, 2022. a
Loshchilov, I. and Hutter, F.: Fixing Weight Decay Regularization in Adam, arXiv [preprint], https://doi.org/10.48550/arXiv.1711.05101, 2017. a
Malmgren-Hansen, D., Pedersen, L. T., Nielsen, A. A., Skriver, H., Saldo, R., Kreiner, M. B., and Buus-Hinkler, J.: ASIP Sea Ice Dataset – version 1, DTU Data [data set], https://doi.org/10.11583/DTU.11920416.v1, 2020. a, b
Malmgren-Hansen, D., Pedersen, L. T., Nielsen, A. A., Kreiner, M. B., Saldo, R., Skriver, H., Lavelle, J., Buus-Hinkler, J., and Krane, K. H.: A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion, IEEE T. Geosci. Remote, 59, 1890–1902, https://doi.org/10.1109/TGRS.2020.3004539, 2021. a, b, c, d, e
Meier, W. N. and Stewart, J. S.: Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters, Remote Sens., 12, 16, https://doi.org/10.3390/rs12162552, 2020. a
Moon, T. A., Thoman, R., Druckenmiller, M. L., Ahmasuk, B., Backensto, S. A., Ballinger, T. J., Benestad, R., Berner, L. T., Bernhard, G. H., Bhatt, U. S., Bigalke, S., BjerkeJarle, W., Brettschneider, B., Christiansen, H. H., Cohen, J. L., Decharme, B., Derksen, C., Divine, D., Drost, J., Druckenmiller, M. L., EliasChereque, A., Epstein, H. E., Fausto, R. S., Fettweis, X., Fioletov, V. E., Forbes, B. C., Frost, G. V., , Gerland, S., Goetz, S. J., Grooß, J.-U., Hanna, E., Hanssen-Bauer, I., Hendricks, S., Holmes, R. M., Ialongo, I., Isaksen, K., Johnsen, B., Jones, T., Kaler, R. S., Kaleschke, L., Kim, S.-J., Labe, Z. M., Lader, R., Lakkala, K., Lara, M. J., Lindsey, J., Loomis, B. D., Luojus, K., Macander, M. J., Mamen, J., Mankoff, K. D., Manney, G. L., McAfee, S. A., McClelland, J. W., Meier, W. N., Moon, T. A., Moore, G. W. K., Mote, T. L., Mudryk, L., Müller, R., Nyland, K. E., Overland, J. E., Parrish, J. K., Perovich, D. K., Petersen, G. N., Petty, A., Phoenix, G. K., Poinar, K., Rantanen, M., Ricker, R., Romanovsky, V. E., Serbin, S. P., Serreze, M. C., Sheffield, G., Shiklomanov, A. I., Shiklomanov, N. I., Smith, S. L., Spencer, R. G. M., Streletskiy, D. A., Suslova, A., Svendby, T., Tank, S. E., Tedesco, M., Thoman, R. L., Tian-Kunze, X., Timmermans, M.-L., Tømmervik, H., Tretiakov, M., Walker, D. A., Walsh, J. E., Wang, M., Webster, M., Wehrlé, A., Yang, D., Zolkos, S., Allen, J., Camper, A. V., Haley, B. O., Hammer, G., Love-Brotak, S., Ohlmann, L., Noguchi, L., Riddle, D. B., and Veasey, S. W.: The Arctic, B. Am. Meteorol. Soc., 104, S271–S321, https://doi.org/10.1175/BAMS-D-23-0079.1, 2023. a
Murphy, A. H. and Winkler, R. L.: Reliability of Subjective Probability Forecasts of Precipitation and Temperature, J. Roy. Stat. Soc. Ser. C, 26, 41–47, https://doi.org/10.2307/2346866, 1977. a
Naeini, M. P., Cooper, G. F., and Hauskrecht, M.: Obtaining Well Calibrated Probabilities Using Bayesian Binning, in: AAAI, 2901–2907, https://doi.org/10.1609/aaai.v29i1.9602, 2015. a
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J.: Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift, in: Proceedings of the 33rd International Conference on Neural Information Processing Systems, Curran Associates Inc., 1254, 12 pp., 2019. a, b, c
Pires de Lima, R. and Karimzadeh, M.: Model Ensemble with Dropout for Uncertainty Estimation in Binary Sea Ice or Water Segmentation using Sentinel-1 SAR, IEEE T. Geosci. Remote, 61, 1–15, https://doi.org/10.1109/TGRS.2023.3331276, 2023. a
Ponsoni, L., Ribergaard, M. H., Nielsen-Englyst, P., Wulf, T., Buus-Hinkler, J., Kreiner, M. B., and Rasmussen, T. A. S.: Greenlandic sea ice products with a focus on an updated operational forecast system, Front. Mar. Sci., 10, 979782, https://doi.org/10.3389/fmars.2023.979782, 2023. a
Ptresample developers: Pyresample, Ptresample developers [code], https://pyresample.readthedocs.io/en/latest (last access: 8 November 2024), 2023. a
Ressel, R., Singha, S., Lehner, S., Rösel, A., and Spreen, G.: Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar, IEEE J. Sel. Top. Appl. Earth Obs., 9, 3131–3143, https://doi.org/10.1109/JSTARS.2016.2539501, 2016. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Springer International Publishing, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a
Saldo, R., Brandt Kreiner, M., Buus-Hinkler, J., Pedersen, L. T., Malmgren-Hansen, D., and Nielsen, A. A.: AI4Arctic/ASIP Sea Ice Dataset – version 2, DTU Data [data set], https://doi.org/10.11583/DTU.13011134.v2, 2020. a, b, c
Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., and Chen, L.: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation, arXiv [preprint], https://doi.org/10.48550/arXiv.1801.04381, 2018. a, b
Singha, S., Johansson, A. M., Hughes, N., Munk Hvidegaard, S., and Skourup, H.: Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-band SAR with Validation by Airborne Measurements, IEEE T. Geosci. Remote, 56, 3715–3734, https://doi.org/10.1109/TGRS.2018.2809504, 2018. a
Stokholm, A., Wulf, T., Kucik, A., Saldo, R., Buus-Hinkler, J., and Hvidegaard, S. M.: AI4SeaIce: Toward Solving Ambiguous SAR Textures in Convolutional Neural Networks for Automatic Sea Ice Concentration Charting, IEEE T. Geosci. Remote, 60, 1–13, https://doi.org/10.1109/TGRS.2022.3149323, 2022. a, b, c, d, e
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z.: Rethinking the Inception Architecture for Computer Vision, arXiv [preprint], https://doi.org/10.48550/arXiv.1512.00567, 2015. a, b
Thulasidasan, S., Chennupati, G., Bilmes, J., Bhattacharya, T., and Michalak, S.: On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks, ArXiv [preprint], https://doi.org/10.48550/arXiv.1905.11001, 2020. a, b
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016. a
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H.: Going deeper with Image Transformers, 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE Computer Society, Los Alamitos, CA, USA, 32–42, https://doi.org/10.1109/ICCV48922.2021.00010, 2021. a
U.S. National Ice Center: U.S. National Ice Center Arctic and Antarctic Sea Ice Charts in SIGRID-3 Format, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.7265/4b7s-rn93, 2022. a
Wang, L., Scott, K. A., Xu, L., and Clausi, D. A.: Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study, IEEE T. Geosci. Remote, 54, 4524–4533, https://doi.org/10.1109/TGRS.2016.2543660, 2016. a
Wang, Y.-R. and Li, X.-M.: Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning, Earth Syst. Sci. Data, 13, 2723–2742, https://doi.org/10.5194/essd-13-2723-2021, 2021. a
Wen, Y., Tran, D., and Ba, J.: BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2002.06715, 2020. a
Wulf, T., Buus-Hinkler, J., and Kreiner, M. B.: Synergetic Fusion of Satellite SAR and Passive Microwave Radiometer Observations for Automatic Sea Ice Charting using Convolutional Neural Networks, in: ESA Living Planet Symposium, ESA, Bonn, Germany, https://lps22-programme.esa.int/posters/POSTERS_Day5.pdf (last access: 8 November 2024), 2022. a, b
Wulf, T., Buus-Hinkler, J., Singha, S., and Kreiner, M. B.: Operational high resolution Arctic sea ice concentration retrieval using SAR and passive microwave observations, in: nternational Symposium on Sea Ice Across Temporal and Spatial Scales, IGS, Bremerhaven, Germany, https://www.igsoc.org/wp-content/uploads/2023/06/programme_80.html (last access: 8 November 2024), 2023a. a
Wulf, T., Buus-Hinkler, J., Singha, S., and Kreiner, M. B.: Fusion of satellite SAR and passive microwave radiometer observations for automatic sea ice mapping using convolutional neural networks, in: Book of abstracts for the IICWG-DA-11 workshop, IICWG, Oslo, Norway, https://iicwg-da-11.met.no/programme (last access: 8 November 2024), 2023b. a
Zakhvatkina, N. Y., Alexandrov, V. Y., Johannessen, O. M., Sandven, S., and Frolov, I. Y.: Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images, IEEE T. Geosci. Remote, 51, 2587–2600, https://doi.org/10.1109/TGRS.2012.2212445, 2013. a
Short summary
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve...
