{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:46:16Z","timestamp":1778877976060,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52101405"],"award-info":[{"award-number":["52101405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the Spatio-Temporal Decomposition Network (STDNet), to predict the daily SIC in the Arctic. The model is based on the Seasonal and Trend decomposition using Loess (STL) decomposition idea to decompose the model into trend and seasonal components. In addition, we have designed the Global Sparse Attention Module (GSAM) to help the model extract global information. STDNet not only extracts seasonal signals and trend information with periodical correspondence from the data but also obtains the spatio-temporal dependence features in the data. The experimental methodology involves predicting the next 10 days based on the first 10 days of data. The prediction results provided the following metrics for the 10-day forecast of STDNet: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination of 1.988%, 3.541%, 5.843%, and 0.979, respectively. The average Binary Accuracy (BACC) at the beginning of September for the period 2018\u20132022 reached 93.85%. The proposed STDNet model outperforms and is lighter than existing deep-learning-based SIC prediction models.<\/jats:p>","DOI":"10.3390\/rs16234534","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T09:18:32Z","timestamp":1733217512000},"page":"4534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration"],"prefix":"10.3390","volume":"16","author":[{"given":"Xu","family":"Zhu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojun","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangming","family":"Jiang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4276-3677","authenticated-orcid":false,"given":"Yi","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2176-9030","authenticated-orcid":false,"given":"Huihui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10584-021-03172-3","article-title":"Arctic shipping guidance from the CMIP6 ensemble on operational and infrastructural timescales","volume":"167","author":"Li","year":"2021","journal-title":"Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TIA.2012.2190816","article-title":"Forecasting power output of photovoltaic systems based on weather classification and support vector machines","volume":"48","author":"Shi","year":"2012","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.solener.2015.05.037","article-title":"3D cloud detection and tracking system for solar forecast using multiple sky imagers","volume":"118","author":"Peng","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1038\/nature09051","article-title":"The central role of diminishing sea ice in recent Arctic temperature amplification","volume":"464","author":"Screen","year":"2010","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.5194\/tc-12-2569-2018","article-title":"Arctic mission benefit analysis: Impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance","volume":"12","author":"Kaminski","year":"2018","journal-title":"Cryosphere"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6680","DOI":"10.1002\/2014JC009963","article-title":"Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter","volume":"119","author":"Yang","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e2020JC016686","DOI":"10.1029\/2020JC016686","article-title":"Sea ice properties in high-resolution sea ice models","volume":"126","author":"Zhang","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3167","DOI":"10.1029\/2019MS001726","article-title":"The GFDL global ocean and sea ice model OM4. 0: Model description and simulation features","volume":"11","author":"Adcroft","year":"2019","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e1601191","DOI":"10.1126\/sciadv.1601191","article-title":"The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean","volume":"3","author":"Horvat","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101809","DOI":"10.1016\/j.ocemod.2021.101809","article-title":"Improving the accuracy of barotropic and internal tides embedded in a high-resolution global ocean circulation model of MITgcm","volume":"162","author":"Fu","year":"2021","journal-title":"Ocean Model."},{"key":"ref_11","first-page":"15","article-title":"CICE: The Los Alamos Sea ice model documentation and software user\u2019s manual version 5.1 LA-CC-06-012","volume":"675","author":"Hunke","year":"2015","journal-title":"T-3 Fluid Dyn. Group Los Alamos Natl. Lab."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8327","DOI":"10.1002\/2015JC011283","article-title":"Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the US Navy\u2019s Arctic Cap Nowcast\/Forecast System","volume":"120","author":"Hebert","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1002\/qj.2555","article-title":"Sea ice forecast verification in the Canadian global ice ocean prediction system","volume":"142","author":"Smith","year":"2016","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"E1304","DOI":"10.1175\/BAMS-D-20-0073.1","article-title":"The future of sea ice modeling: Where do we go from here?","volume":"101","author":"Blockley","year":"2020","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.3189\/002214311796406095","article-title":"Sea-ice models for climate study: Retrospective and new directions","volume":"56","author":"Hunke","year":"2010","journal-title":"J. Glaciol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girard, L., Weiss, J., Molines, J.-M., Barnier, B., and Bouillon, S. (2009). Evaluation of high-resolution sea ice models on the basis of statistical and scaling properties of Arctic sea ice drift and deformation. J. Geophys. Res. Ocean., 114.","DOI":"10.1029\/2008JC005182"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"360","DOI":"10.3189\/172756411795931769","article-title":"Spatial and temporal characterization of sea-ice deformation","volume":"52","author":"Hutchings","year":"2011","journal-title":"Ann. Glaciol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6097","DOI":"10.1175\/JCLI-D-20-0846.1","article-title":"A regional seasonal forecast model of Arctic minimum sea ice extent: Reflected solar radiation versus late winter coastal divergence","volume":"34","author":"Kim","year":"2021","journal-title":"J. Clim."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8042","DOI":"10.1002\/2015GL065860","article-title":"Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook","volume":"42","author":"Cullather","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1175\/JCLI-D-15-0313.1","article-title":"Predicting summer Arctic sea ice concentration intraseasonal variability using a vector autoregressive model","volume":"29","author":"Wang","year":"2016","journal-title":"J. Clim."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8151","DOI":"10.1175\/JCLI-D-15-0858.1","article-title":"Arctic sea ice seasonal prediction by a linear Markov model","volume":"29","author":"Yuan","year":"2016","journal-title":"J. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4953","DOI":"10.1007\/s00382-018-4426-6","article-title":"Subseasonal forecast of Arctic sea ice concentration via statistical approaches","volume":"52","author":"Wang","year":"2019","journal-title":"Clim. Dyn."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Andrade, J., Katoch, S., Turaga, P., Spanias, A., Tepedelenlioglu, C., and Jaskie, K. (2019, January 5\u201317). Formation-aware cloud segmentation of ground-based images with applications to PV systems. Proceedings of the 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), Patras, Greece.","DOI":"10.1109\/IISA.2019.8900762"},{"key":"ref_24","unstructured":"Ranzato, M., Szlam, A., Bruna, J., Mathieu, M., Collobert, R., and Chopra, S. (2014). Video (language) modeling: A baseline for generative models of natural videos. arXiv."},{"key":"ref_25","unstructured":"Srivastava, N., Mansimov, E., and Salakhudinov, R. (2015, January 6\u201311). Unsupervised learning of video representations using lstms. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_26","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","first-page":"5622","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume":"30","author":"Shi","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","first-page":"879","article-title":"Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms","volume":"30","author":"Wang","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","unstructured":"Wang, Y., Gao, Z., Long, M., Wang, J., and Philip, S.Y. (2018, January 10\u201315). Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_30","unstructured":"Wang, Y., Jiang, L., Yang, M.-H., Li, L.-J., Long, M., and Fei-Fei, L. (May, January 30). Eidetic 3D LSTM: A model for video prediction and beyond. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., and Yu, P.S. (2019, January 15\u201320). Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref_32","unstructured":"Guen, V.L., and Thome, N. (2020, January 13\u201319). Disentangling physical dynamics from unknown factors for unsupervised video prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_33","unstructured":"Lotter, W., Kreiman, G., and Cox, D. (2016). Deep predictive coding networks for video prediction and unsupervised learning. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1109\/TPAMI.2022.3165153","article-title":"Predrnn: A recurrent neural network for spatiotemporal predictive learning","volume":"45","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","first-page":"26950","article-title":"Mau: A motion-aware unit for video prediction and beyond","volume":"34","author":"Chang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gao, Z., Tan, C., Wu, L., and Li, S.Z. (2022, January 18\u201324). Simvp: Simpler yet better video prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00317"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5124","DOI":"10.1038\/s41467-021-25257-4","article-title":"Seasonal Arctic sea ice forecasting with probabilistic deep learning","volume":"12","author":"Andersson","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Ren, Y., and Li, X. (2021, January 1\u201316). Predicting daily arctic sea ice concentration in the melt season based on a deep fully convolution network model. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554118"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4304819","DOI":"10.1109\/TGRS.2022.3177600","article-title":"A data-driven deep learning model for weekly sea ice concentration prediction of the pan-arctic during the melting season","volume":"60","author":"Ren","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wei, J., Hang, R., and Luo, J.-J. (2022). Prediction of pan-arctic sea ice using attention-based LSTM neural networks. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.860403"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Li, W., Shao, Q., Han, G., and Wang, X. (2022). A mid-and long-term arctic sea ice concentration prediction model based on deep learning technology. Remote Sens., 14.","DOI":"10.3390\/rs14122889"},{"key":"ref_45","first-page":"1673","article-title":"Extended-range arctic sea ice forecast with convolutional long short-term memory networks","volume":"149","author":"Liu","year":"2021","journal-title":"Mon. Weather Rev."},{"key":"ref_46","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_47","first-page":"1","article-title":"IceTFT v 1.0. 0: Interpretable Long-Term Prediction of Arctic Sea Ice Extent with Deep Learning","volume":"2023","author":"Mu","year":"2023","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"9317","DOI":"10.1029\/2019JC015422","article-title":"Monthly variability in Bering Strait oceanic volume and heat transports, links to atmospheric circulation and ocean temperature, and implications for sea ice conditions","volume":"124","author":"Serreze","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chi, J., Bae, J., and Kwon, Y.-J. (2021). Two-stream convolutional long-and short-term memory model using perceptual loss for sequence-to-sequence Arctic sea ice prediction. Remote Sens., 13.","DOI":"10.3390\/rs13173413"},{"key":"ref_52","first-page":"4300522","article-title":"A Spatio-Temporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice","volume":"62","author":"Zheng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","first-page":"308","article-title":"Time-Series, 2nd edn","volume":"25","author":"Anderson","year":"1976","journal-title":"Statistician"},{"key":"ref_54","first-page":"3","article-title":"STL: A seasonal-trend decomposition procedure based on loess","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat."},{"key":"ref_55","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_57","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_58","unstructured":"Tang, C., Zhao, Y., Wang, G., Luo, C., Xie, W., and Zeng, W. (March, January 22). Sparse MLP for image recognition: Is self-attention really necessary?. Proceedings of the AAAI Conference on Artificial Intelligence, Online."},{"key":"ref_59","unstructured":"DiGirolamo, N.E., Parkinson, D.J., Gloersen, C.P., and Zwally, H.J. (2022). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM\/I-SSMIS Passive Microwave Data, Version 2, NASA National Snow and Ice Data Center Distributed Active Archive Center."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.5194\/tc-14-1519-2020","article-title":"An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)","volume":"14","author":"Tschudi","year":"2020","journal-title":"Cryosphere"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"439","DOI":"10.5194\/tc-8-439-2014","article-title":"Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations","volume":"8","author":"Huntemann","year":"2014","journal-title":"Cryosphere"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:46:05Z","timestamp":1760114765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":61,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234534"],"URL":"https:\/\/doi.org\/10.3390\/rs16234534","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]}}}