{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:12:59Z","timestamp":1771474379771,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"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":["PE21420"],"award-info":[{"award-number":["PE21420"]}],"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>Arctic sea ice plays a significant role in climate systems, and its prediction is important for coping with global warming. Artificial intelligence (AI) has gained recent attention in various disciplines with the increasing use of big data. In recent years, the use of AI-based sea ice prediction, along with conventional prediction models, has drawn attention. This study proposes a new deep learning (DL)-based Arctic sea ice prediction model with a new perceptual loss function to improve both statistical and visual accuracy. The proposed DL model learned spatiotemporal characteristics of Arctic sea ice for sequence-to-sequence predictions. The convolutional neural network-based perceptual loss function successfully captured unique sea ice patterns, and the widely used loss functions could not use various feature maps. Furthermore, the input variables that are essential to accurately predict Arctic sea ice using various combinations of input variables were identified. The proposed approaches produced statistical outcomes with better accuracy and qualitative agreements with the observed data.<\/jats:p>","DOI":"10.3390\/rs13173413","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T21:59:45Z","timestamp":1630447185000},"page":"3413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4943-3790","authenticated-orcid":false,"given":"Junhwa","family":"Chi","sequence":"first","affiliation":[{"name":"Center of Remote Sensing and GIS, Korea Polar Research Institute, Incheon 21990, Korea"}]},{"given":"Jihyun","family":"Bae","sequence":"additional","affiliation":[{"name":"Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1293-9405","authenticated-orcid":false,"given":"Young-Joo","family":"Kwon","sequence":"additional","affiliation":[{"name":"Center of Remote Sensing and GIS, Korea Polar Research Institute, Incheon 21990, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1038\/s41558-019-0619-1","article-title":"An emergent constraint on future Arctic sea-ice albedo feedback","volume":"9","author":"Thackeray","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","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_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":"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_5","unstructured":"Meier, W., Bhatt, U.S., Walsh, J., Thoman, R., Bieniek, P., Bitz, C.M., Blanchard-Wrigglesworth, E., Eicken, H., Hamilton, L.C., and Hardman, M. (2021). 2020 Sea Ice Outlook Post-Season Report, Sea Ice Prediction Network."},{"key":"ref_6","first-page":"1334","article-title":"The central role of diminishing sea ice in recent Arctic temperature amplification","volume":"464","author":"Screen","year":"2010","journal-title":"Nat. Cell Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/ngeo2071","article-title":"Arctic amplification dominated by temperature feedbacks in contemporary climate models","volume":"7","author":"Pithan","year":"2014","journal-title":"Nat. Geosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.5194\/tc-13-113-2019","article-title":"Past and future interannual variability in Arctic sea ice in coupled climate models","volume":"13","author":"Mioduszewski","year":"2019","journal-title":"Cryosphere"},{"key":"ref_9","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":"2016","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_10","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_11","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_12","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_13","doi-asserted-by":"crossref","unstructured":"Chi, J., and Kim, H.-C. (2017). Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9121305"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, K., Cho, J., Kang, Y.Q., Yoon, H.-J., and Lee, Y.-W. (2018). Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble. Remote Sens., 11.","DOI":"10.3390\/rs11010019"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Choi, M., De Silva, L.W.A., and Yamaguchi, H. (2019). Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration. Remote Sens., 11.","DOI":"10.3390\/rs11091071"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.5194\/tc-14-1083-2020","article-title":"Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks","volume":"14","author":"Kim","year":"2020","journal-title":"Cryosphere"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhang, R., Wang, Y., Yan, H., and Hong, M. (2021). Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9030330"},{"key":"ref_18","unstructured":"Cho, K., and Naoki, K. (2015). Advantages of AMSR2 for Monitoring Sea Ice from Space, Citeseer."},{"key":"ref_19","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_20","unstructured":"Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H.J. (2020). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM\/I-SSMIS Passive Microwave Data, Version 1."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1029\/2005JC003384","article-title":"Sea ice remote sensing using AMSR-E 89-GHz channels","volume":"113","author":"Spreen","year":"2008","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_22","unstructured":"Comiso, J.C. (1995). SSM\/I Sea Ice Concentrations Using the Bootstrap Algorithm."},{"key":"ref_23","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"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_25","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor\u00e1nyi, A., Sabater, J.M., Nicolas, J., Peubey, C., Radu, R., and Rozum, I. (2021, June 02). ERA5 Hourly Data on Single Levels from 1979 to Present. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-single-levels?tab=overview."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1175\/JCLI-D-13-00014.1","article-title":"Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic","volume":"27","author":"Lindsay","year":"2014","journal-title":"J. Clim."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6138","DOI":"10.1029\/2019GL082781","article-title":"Improved Performance of ERA5 in Arctic Gateway Relative to Four Global Atmospheric Reanalyses","volume":"46","author":"Graham","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4027","DOI":"10.1175\/JCLI-D-19-0648.1","article-title":"Robustness of the Recent Global Atmospheric Reanalyses for Antarctic Near-Surface Wind Speed Climatology","volume":"33","author":"Dong","year":"2020","journal-title":"J. Clim."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.solener.2018.01.005","article-title":"A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data","volume":"162","author":"Srivastava","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-020-06140-w","article-title":"A deep learning approach for forecasting non-stationary big remote sensing time series","volume":"13","author":"Rhif","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_33","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., and Woo, W. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1029\/2019EA000812","article-title":"A Deep Learning-Based Methodology for Precipitation Nowcasting With Radar","volume":"7","author":"Chen","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_35","first-page":"1","article-title":"Prediction of Short-Time Rainfall Based on Deep Learning","volume":"2021","author":"Sun","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_36","first-page":"100007","article-title":"Short-term temperature forecasts using a convolutional neural network\u2014An application to different weather stations in Germany","volume":"2","author":"Kreuzer","year":"2020","journal-title":"Mach. Learn. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kim, K.-S., Lee, J.-B., Roh, M.-I., Han, K.-M., and Lee, G.-H. (2020). Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8100805"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/TPAMI.2016.2599174","article-title":"Long-Term Recurrent Convolutional Networks for Visual Recognition and Description","volume":"39","author":"Donahue","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.image.2018.09.003","article-title":"TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition","volume":"71","author":"Ma","year":"2019","journal-title":"Signal Process. Image Commun."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"67772","DOI":"10.1109\/ACCESS.2019.2918808","article-title":"Two-Stream Convolutional Network for Improving Activity Recognition Using Convolutional Long Short-Term Memory Networks","volume":"7","author":"Ye","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chi, J., and Kim, H.-C. (2021). Retrieval of daily sea ice thickness from AMSR2 passive microwave data using ensemble convolutional neural networks. GIScience Remote Sens., 1\u201319.","DOI":"10.1080\/15481603.2021.1943213"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Computer Vision\u2014ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111204","DOI":"10.1016\/j.rse.2019.05.023","article-title":"Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data","volume":"231","author":"Chi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_46","unstructured":"Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A Learning Algorithm for Continually Running Fully Recurrent Neural Networks","volume":"1","author":"Williams","year":"1989","journal-title":"Neural Comput."},{"key":"ref_48","unstructured":"He, T., Zhang, J., Zhou, Z., and Glass, J. (2019). Quantifying Exposure Bias for Open-Ended Language Generation. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tran, Q.-K., and Song, S.-K. (2019). Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks. Atmosphere, 10.","DOI":"10.3390\/atmos10050244"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1175\/1520-0442(2000)013<0896:VISATO>2.0.CO;2","article-title":"Variations in Surface Air Temperature Observations in the Arctic, 1979\u201397","volume":"13","author":"Rigor","year":"2000","journal-title":"J. Clim."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Serreze, M.C., Maslanik, J.A., Scambos, T.A., Fetterer, F., Stroeve, J., Knowles, K., Fowler, C., Drobot, S., Barry, R., and Haran, T.M. (2003). A record minimum arctic sea ice extent and area in 2002. Geophys. Res. Lett., 30.","DOI":"10.1029\/2002GL016406"},{"key":"ref_52","first-page":"512","article-title":"Summer storms bolster Arctic ice","volume":"500","author":"Morello","year":"2013","journal-title":"Nat. Cell Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5285","DOI":"10.1038\/s41467-019-13299-8","article-title":"The role of cyclone activity in snow accumulation on Arctic sea ice","volume":"10","author":"Webster","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/s41612-019-0080-x","article-title":"On the dynamic instability of Arctic sea ice","volume":"2","author":"Chavas","year":"2019","journal-title":"Npj Clim. Atmospheric Sci."},{"key":"ref_55","unstructured":"Kaiming, H., Xiangyu, Z., Shaoqing, R., and Jian, S. (2017, January 21\u201326). Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_56","unstructured":"Tan, M., and Le, Q.V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3413\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:53:48Z","timestamp":1760165628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,27]]},"references-count":56,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173413"],"URL":"https:\/\/doi.org\/10.3390\/rs13173413","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,27]]}}}