{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:12:20Z","timestamp":1773249140888,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,12]],"date-time":"2017-12-12T00:00:00Z","timestamp":1513036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004230","name":"Korea Polar Research Institute","doi-asserted-by":"publisher","award":["PE17120"],"award-info":[{"award-number":["PE17120"]}],"id":[{"id":"10.13039\/501100004230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Arctic sea ice is an important indicator of the progress of global warming and climate change. Prediction of Arctic sea ice concentration has been investigated by many disciplines and predictions have been made using a variety of methods. Deep learning (DL) using large training datasets, also known as deep neural network, is a fast-growing area in machine learning that promises improved results when compared to traditional neural network methods. Arctic sea ice data, gathered since 1978 by passive microwave sensors, may be an appropriate input for training DL models. In this study, a large Arctic sea ice dataset was employed to train a deep neural network and this was then used to predict Arctic sea ice concentration, without incorporating any physical data. We compared the results of our methods quantitatively and qualitatively to results obtained using a traditional autoregressive (AR) model, and to a compilation of results from the Sea Ice Prediction Network, collected using a diverse set of approaches. Our DL-based prediction methods outperformed the AR model and yielded results comparable to those obtained with other models.<\/jats:p>","DOI":"10.3390\/rs9121305","type":"journal-article","created":{"date-parts":[[2017,12,12]],"date-time":"2017-12-12T13:35:00Z","timestamp":1513085700000},"page":"1305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":127,"title":["Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4943-3790","authenticated-orcid":false,"given":"Junhwa","family":"Chi","sequence":"first","affiliation":[{"name":"Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute, Incheon 21990, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6831-9291","authenticated-orcid":false,"given":"Hyun-choel","family":"Kim","sequence":"additional","affiliation":[{"name":"Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute, Incheon 21990, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1038\/nclimate2524","article-title":"Attribution of Arctic temperature change to greenhouse-gas and aerosol influences","volume":"5","author":"Najafi","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1007\/s10712-014-9284-0","article-title":"Effects of Arctic Sea Ice Decline on Weather and Climate: A Review","volume":"35","author":"Vihma","year":"2014","journal-title":"Surv. Geophys."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Stroeve, J., Holland, M.M., Meier, W., Scambos, T., and Serreze, M. (2007). Arctic sea ice decline: Faster than forecast. Geophys. Res. Lett., 34.","DOI":"10.1029\/2007GL029703"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"871","DOI":"10.5194\/tc-6-871-2012","article-title":"Antarctic sea ice variability and trends, 1979\u20132010","volume":"8","author":"Parkinson","year":"2012","journal-title":"Cryosphere"},{"key":"ref_5","first-page":"1118","article-title":"Seasonal ice loss in the Beaufort Sea: Toward synchrony and prediction","volume":"120","author":"Steele","year":"2015","journal-title":"J. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cavalieri, D.J., Parkinson, C.L., Gloersen, P., and Zwally, H. (1996). Sea Ice Concentrations from Nimbus-7 SSMR and DMSP SSM\/I-SSMIS Passive Microwave Data, NASA DAAC at the National Snow and Ice Data Center. Available online: http:\/\/dx.doi.org\/10.5067\/8GQ8LZQVL0VL.","DOI":"10.5067\/8GQ8LZQVL0VL"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Spreen, G., Kaleschke, L., and Heygster, G. (2008). Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys. Res., 113.","DOI":"10.1029\/2005JC003384"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7233","DOI":"10.1109\/TGRS.2014.2310136","article-title":"Retrieval of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A Comparison of Eleven Sea Ice Concentration Algorithms","volume":"52","author":"Ivanova","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lindsay, R.W., Zhang, J., Schweiger, A.J., and Steele, M.A. (2008). Seasonal predictions of ice extent in the Arctic Ocean. J. Geophys. Res., 113.","DOI":"10.1029\/2007JC004259"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1002\/qj.2401","article-title":"A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales","volume":"142","author":"Guemas","year":"2014","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1002\/2014GL059388","article-title":"Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008\u20132013","volume":"41","author":"Stroeve","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1175\/MWR-D-12-00057.1","article-title":"Seasonal Prediction of Arctic Sea Ice Extent from a Coupled Dynamical Forecast System","volume":"141","author":"Wang","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1002\/grl.50129","article-title":"Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system","volume":"40","author":"Sigmond","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_18","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2016). Time Series Analysis: Forecasting and Control, John Wiley & Sons. [5th ed.]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014, January 14\u201318). Long short-term memory recurrent neural network architectures for large scale acoustic modelling. Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association, Singapore.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1016\/j.scient.2011.08.031","article-title":"Tuning the parameters of an artificial neural network using central composite design and genetic algorithm","volume":"18","author":"Bashiri","year":"2011","journal-title":"Sci. Iran."},{"key":"ref_24","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference for Learning Representations, San Diego, CA, USA."},{"key":"ref_25","unstructured":"Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., and Ng, A.Y. (July, January 28). On Optimization Methods for Deep Learning. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA."},{"key":"ref_26","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/S0034-4257(96)00220-9","article-title":"Passive microwave algorithms for sea ice concentration: A comparison of two techniques","volume":"60","author":"Comiso","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1109\/36.843033","article-title":"An Enhancement of the NASA Team Sea Ice Algorithm","volume":"38","author":"Markus","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bontempi, G., Taieb, S.B., and Borgne, Y.L. (2013). Machine learning strategies for time series forecasting. Business Intelligence, Springer.","DOI":"10.1007\/978-3-642-36318-4_3"},{"key":"ref_30","unstructured":"Hipel, K.W., and McLeod, A.I. (1994). Time Series Modelling of Water Resources and Environmental Systems, Elsevier."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5225","DOI":"10.1080\/01431160110109552","article-title":"ARMA time series modelling of remote sensing imagery: A new approach for climate change studies","volume":"23","author":"Piwowar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/S0377-0273(01)00298-0","article-title":"Applications of autoregressive models and time\u2013frequency analysis to the study of volcanic tremor and long-period events","volume":"114","author":"Lesage","year":"2002","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_33","unstructured":"(2017, November 15). Arctic Sea Ice News and Analysis. Available online: http:\/\/nsidc.org\/arcticseaicenews."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1126\/science.286.5446.1934","article-title":"Global warming and Northern Hemisphere sea ice extent","volume":"286","author":"Vinnikov","year":"1999","journal-title":"Science"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/12\/1305\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:53:43Z","timestamp":1760208823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/12\/1305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,12]]},"references-count":34,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["rs9121305"],"URL":"https:\/\/doi.org\/10.3390\/rs9121305","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,12]]}}}