{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:49:17Z","timestamp":1769834957749,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["42106232"],"award-info":[{"award-number":["42106232"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41876222"],"award-info":[{"award-number":["41876222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["ZY0722031"],"award-info":[{"award-number":["ZY0722031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["JD0619010"],"award-info":[{"award-number":["JD0619010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Global Change and Air\u2013Sea Interaction II","award":["42106232"],"award-info":[{"award-number":["42106232"]}]},{"name":"Global Change and Air\u2013Sea Interaction II","award":["41876222"],"award-info":[{"award-number":["41876222"]}]},{"name":"Global Change and Air\u2013Sea Interaction II","award":["ZY0722031"],"award-info":[{"award-number":["ZY0722031"]}]},{"name":"Global Change and Air\u2013Sea Interaction II","award":["JD0619010"],"award-info":[{"award-number":["JD0619010"]}]},{"name":"Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC)","award":["42106232"],"award-info":[{"award-number":["42106232"]}]},{"name":"Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC)","award":["41876222"],"award-info":[{"award-number":["41876222"]}]},{"name":"Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC)","award":["ZY0722031"],"award-info":[{"award-number":["ZY0722031"]}]},{"name":"Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC)","award":["JD0619010"],"award-info":[{"award-number":["JD0619010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions.<\/jats:p>","DOI":"10.3390\/rs15235437","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:12:13Z","timestamp":1700568733000},"page":"5437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Dengyan","family":"Zhong","sequence":"first","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6503-0505","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxia","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","unstructured":"Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., and Muelbert, M. (2019). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, IPCC. Chapter 3."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.1175\/JCLI-D-21-0548.1","article-title":"Subseasonal-to-Seasonal Arctic Sea Ice Forecast Skill Improvement from Sea Ice Concentration Assimilation","volume":"35","author":"Zhang","year":"2022","journal-title":"J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.5194\/gmd-14-1445-2021","article-title":"The Regional Ice Ocean Prediction System v2: A pan-Canadian ocean analysis system using an online tidal harmonic analysis","volume":"14","author":"Smith","year":"2021","journal-title":"Geosci. Model Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1175\/JCLI-D-16-0437.1","article-title":"Impacts of sea ice thickness initialization on seasonal Arctic sea ice predictions","volume":"30","author":"Dirkson","year":"2017","journal-title":"J. Clim."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lavergne, T., and Down, E. (2023). A Climate Data Record of Year-Round Global Sea Ice Drift from the EUMETSAT OSI SAF. Earth Syst. Sci. Data Discuss., 1\u201338.","DOI":"10.5194\/essd-2023-40"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7827","DOI":"10.1002\/2015JC011273","article-title":"Accuracy of short-term sea ice drift forecasts using a coupled ice-ocean model","volume":"120","author":"Schweiger","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"e2019MS001938","DOI":"10.1029\/2019MS001938","article-title":"Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations","volume":"12","author":"Yang","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.polar.2019.08.001","article-title":"Grid size dependency of short-term sea ice forecast and its evaluation during extreme Arctic cyclone in August 2016","volume":"21","author":"Yamaguchi","year":"2019","journal-title":"Polar Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9731","DOI":"10.1029\/2018GL079394","article-title":"Bright prospects for Arctic sea ice prediction on subseasonal time scales","volume":"45","author":"Zampieri","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"L19501","DOI":"10.1029\/2011GL048970","article-title":"Trends in Arctic sea ice drift and role of wind forcing: 1992\u20132009","volume":"38","author":"Spreen","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4677","DOI":"10.5194\/gmd-16-4677-2023","article-title":"IceTFT v 1.0.0: Interpretable Long-Term Prediction of Arctic Sea Ice Extent with Deep Learning","volume":"16","author":"Mu","year":"2023","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"736429","DOI":"10.3389\/fmars.2021.736429","article-title":"Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function","volume":"8","author":"Liu","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","unstructured":"Petrou, Z.I., and Tian, Y. (2017, January 23\u201328). Prediction of sea ice motion with recurrent neural networks. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128230"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alqushaibi, A., Abdulkadir, S.J., Rais, H.M., and Al-Tashi, Q. (2020, January 8\u20139). A review of weight optimization techniques in recurrent neural networks. Proceedings of the 2020 International Conference on Computational Intelligence (ICCI), Bandar Seri Iskandar, Malaysia.","DOI":"10.1109\/ICCI51257.2020.9247757"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhai, J., and Bitz, C.M. (2021). A machine learning model of Arctic sea ice motions. arXiv.","DOI":"10.1002\/essoar.10504769.1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Farooque, G., Xiao, L., Yang, J., and Sargano, A.B. (2021). Hyperspectral image classification via a novel spectral\u2013spatial 3D ConvLSTM-CNN. Remote Sens., 13.","DOI":"10.3390\/rs13214348"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6865","DOI":"10.1109\/TGRS.2019.2909057","article-title":"Prediction of sea ice motion with convolutional long short-term memory networks","volume":"57","author":"Petrou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.c. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_23","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., and Feng, J. (2018, January 2\u20138). A\u02c6 2-nets: Double attention networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_24","first-page":"230004","article-title":"Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill","volume":"2","author":"Hoffman","year":"2023","journal-title":"Artif. Intell. Earth Syst."},{"key":"ref_25","unstructured":"Lin, Z., Li, M., Zheng, Z., Cheng, Y., and Yuan, C. (2020, January 7\u201312). Self-attention convlstm for spatiotemporal prediction. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108267","DOI":"10.1016\/j.comnet.2021.108267","article-title":"Self-attentive deep learning method for online traffic classification and its interpretability","volume":"196","author":"Xie","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sperber, M., Niehues, J., Neubig, G., St\u00fcker, S., and Waibel, A. (2018). Self-attentional acoustic models. arXiv.","DOI":"10.21437\/Interspeech.2018-1910"},{"key":"ref_29","unstructured":"Won, M., Chun, S., and Serra, X. (2019). Toward interpretable music tagging with self-attention. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13195-022-01043-2","article-title":"An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks","volume":"14","author":"Ruengchaijatuporn","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_31","unstructured":"Tschudi, M., Fowler, C., Maslanik, J., Stewart, J., and Meier, W. (2023, November 07). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 3 [IABP buoys]. Available online: https:\/\/nsidc.org\/data\/nsidc-0116\/versions\/3."},{"key":"ref_32","unstructured":"Tschudi, M., Meier, W., Stewart, J., Fowler, C., and Maslanik, J. (2023, November 07). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4, Boulder, Colorado USA, NASA National Snow and Ice Data Center. Available online: https:\/\/nsidc.org\/data\/nsidc-0116\/versions\/4."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"352","DOI":"10.3189\/172756406781811286","article-title":"High-resolution sea-ice motions from AMSR-E imagery","volume":"44","author":"Meier","year":"2006","journal-title":"Ann. Glaciol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"C10011","DOI":"10.1029\/2007JC004582","article-title":"Estimating sea ice area flux across the Canadian Arctic Archipelago using enhanced AMSR-E","volume":"113","author":"Agnew","year":"2008","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20193","DOI":"10.3402\/polar.v32i0.20193","article-title":"Influence of winter sea-ice motion on summer ice cover in the Arctic","volume":"32","author":"Kimura","year":"2013","journal-title":"Polar Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1109\/TGRS.2012.2184124","article-title":"Enhanced Arctic sea ice drift estimation merging radiometer and scatterometer data","volume":"50","author":"Ezraty","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/TGRS.2005.859352","article-title":"Arctic-wide operational sea ice drift from enhanced-resolution QuikScat\/SeaWinds scatterometry and its validation","volume":"44","author":"Haarpaintner","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","unstructured":"Meier, W.N., Markus, T., and Comiso, J. (2018). AMSR-E\/AMSR2 Unified L3 Daily 12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids, Version 1, Active Archive Center."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113748","DOI":"10.1016\/j.rse.2023.113748","article-title":"Improved algorithm for determining the freeze onset of Arctic sea ice using AMSR-E\/2 data","volume":"297","author":"Qu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3189\/172756408784700743","article-title":"Comparison of sea-ice extent and ice-edge location estimates from passive microwave and enhanced-resolution scatterometer data","volume":"48","author":"Meier","year":"2008","journal-title":"Ann. Glaciol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Agarwal, A., Gupta, S., and Singh, D.K. (2016, January 14\u201317). Review of optical flow technique for moving object detection. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India.","DOI":"10.1109\/IC3I.2016.7917999"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Robinson, A., Magnusson, M., and Felsberg, M. (2023, January 8\u201311). Leveraging Optical Flow Features for Higher Generalization Power in Video Object Segmentation. Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIP49359.2023.10222542"},{"key":"ref_43","unstructured":"Fleet, D., and Weiss, Y. (2006). Handbook of Mathematical Models in Computer Vision, Springer."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Revaud, J., Weinzaepfel, P., Harchaoui, Z., and Schmid, C. (2015, January 7\u201312). Epicflow: Edge-preserving interpolation of correspondences for optical flow. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298720"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., and Schmid, C. (2013, January 1\u20138). DeepFlow: Large displacement optical flow with deep matching. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.175"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., and Zitnick, C.L. (2013, January 1\u20138). Structured forests for fast edge detection. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.231"},{"key":"ref_47","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_48","first-page":"3518811","article-title":"Self-attention ConvLSTM and its application in RUL prediction of rolling bearings","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_49","first-page":"202","article-title":"An improved algorithm of EfficientNet with Self Attention mechanism","volume":"Volume 12174","author":"Wang","year":"2022","journal-title":"Proceedings of the International Conference on Internet of Things and Machine Learning (IoTML 2021)"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lin, Y.L., Chen, Y.C., Liu, A., Lin, S.C., and Hsu, W.Y. (2022, January 4\u20137). Effective Construction of a Reflection Angle Prediction Model for Reflectarrays Using the Hadamard Product Self-Attention Mechanism. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore.","DOI":"10.1109\/SSCI51031.2022.10022195"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, H., Pang, M., Jiang, Y., and Lin, H. (2022, January 10\u201315). Deep Learning with Attention Mechanism for Electromagnetic Inverse Scattering. Proceedings of the 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S\/URSI), Denver, CO, USA.","DOI":"10.1109\/AP-S\/USNC-URSI47032.2022.9886637"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Min, L., Wu, A., Fan, X., Li, F., and Li, J. (2023). Dim and Small Target Detection with a Combined New Norm and Self-Attention Mechanism of Low-Rank Sparse Inversion. Sensors, 23.","DOI":"10.3390\/s23167240"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhong, D.Y. (2023, November 07). Data for Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data. 2023, November. Available online: https:\/\/figshare.com\/articles\/dataset\/Data_for_Self-Attention_ConvLSTM_for_Short-term_Arctic_Sea_Ice_Motion_Prediction_Using_AMSR-E_36_5_GHz_Data_\/24354901.","DOI":"10.3390\/rs15235437"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5437\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:46Z","timestamp":1760131546000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":53,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235437"],"URL":"https:\/\/doi.org\/10.3390\/rs15235437","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,21]]}}}