{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T17:33:16Z","timestamp":1769275996101,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U21A20457"],"award-info":[{"award-number":["U21A20457"]}]},{"name":"National Natural Science Foundation of China","award":["62231021"],"award-info":[{"award-number":["62231021"]}]},{"name":"National Natural Science Foundation of China","award":["U2006207"],"award-info":[{"award-number":["U2006207"]}]},{"name":"National Natural Science Foundation of China","award":["62371380"],"award-info":[{"award-number":["62371380"]}]},{"name":"National Natural Science Foundation of China","award":["62271457"],"award-info":[{"award-number":["62271457"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Given the significant spatial non-uniformity of marine evaporation ducts, accurately predicting the regional distribution of evaporation duct height (EDH) is crucial for ensuring the stable operation of radio systems. While machine-learning-based EDH prediction models have been extensively developed, they fail to provide the EDH distribution over large-scale regions in practical applications. To address this limitation, we have developed a novel spatiotemporal prediction model for EDH that integrates multiple environmental information sources, termed the EDH Spatiotemporal Network (EDH-STNet). This model is based on the Swin-Unet architecture, employing an Encoder\u2013Decoder framework that utilizes consecutive Swin-Transformers. This design effectively captures complex spatial correlations and temporal characteristics. The EDH-STNet model also incorporates nonlinear relationships between various hydrometeorological parameters (HMPs) and EDH. In contrast to existing models, it introduces multiple HMPs to enhance these relationships. By adopting a data-driven approach that integrates these HMPs as prior information, the accuracy and reliability of spatiotemporal predictions are significantly improved. Comprehensive testing and evaluation demonstrate that the EDH-STNet model, which merges an advanced deep learning algorithm with multiple HMPs, yields accurate predictions of EDH for both immediate and future timeframes. This development offers a novel solution to ensure the stable operation of radio systems.<\/jats:p>","DOI":"10.3390\/rs16224227","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T06:23:16Z","timestamp":1731478996000},"page":"4227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["EDH-STNet: An Evaporation Duct Height Spatiotemporal Prediction Model Based on Swin-Unet Integrating Multiple Environmental Information Sources"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0178-6330","authenticated-orcid":false,"given":"Hanjie","family":"Ji","sequence":"first","affiliation":[{"name":"School of Physics, Xidian University, Xi\u2019an 710071, China"},{"name":"National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Physics, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwen","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Physics, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangming","family":"Guo","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/PROC.1985.13138","article-title":"Tropospheric radio propagation assessment","volume":"73","author":"Hitney","year":"1985","journal-title":"Proc. IEEE"},{"key":"ref_2","unstructured":"Zhang, J. (2012). Methods of Retrieving Tropospheric Ducts Above Ocean Surface Using Radar Sea Clutter and GPS Signals. [Ph.D. Thesis, Xidian University]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10067","DOI":"10.1109\/JSTARS.2024.3395630","article-title":"IPILT\u2013OHPL: An Over-the-Horizon Propagation Loss Prediction Model Established by Incorporating Prior Information Into the LSTM\u2013Transformer Structure","volume":"17","author":"Ji","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5606","DOI":"10.1109\/TGRS.2019.2900582","article-title":"A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer","volume":"57","author":"Gilles","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7166","DOI":"10.1109\/TGRS.2016.2597138","article-title":"Inverting for Maritime Environments Using Proper Orthogonal Bases from Sparsely Sampled Electromagnetic Propagation Data","volume":"54","author":"Fountoulakis","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"569","DOI":"10.5194\/npg-14-569-2007","article-title":"An artificial neural network predictor for tropospheric surface duct phenomena","volume":"14","author":"Isaakidis","year":"2007","journal-title":"Nonlinear Process Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/LAWP.2018.2873110","article-title":"Calculation Method for Evaporation Duct Profiles Based on Artificial Neural Network","volume":"17","author":"Yan","year":"2018","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1109\/LGRS.2018.2842235","article-title":"An Evaporation Duct Height Prediction Method Based on Deep Learning","volume":"15","author":"Zhu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1029\/2019RS006882","article-title":"PDD_GBR: Research on Evaporation Duct Height Prediction Based on Gradient Boosting Regression Algorithm","volume":"54","author":"Zhao","year":"2019","journal-title":"Radio Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"81","DOI":"10.13164\/re.2020.0081","article-title":"XGB Model: Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm","volume":"29","author":"Zhao","year":"2020","journal-title":"Radioengineering"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1049\/iet-map.2019.1136","article-title":"Research on evaporation duct height prediction based on back propagation neural network","volume":"14","author":"Zhao","year":"2020","journal-title":"IET Microw. Antennas Propag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mai, Y., Sheng, Z., Shi, H., Li, C., Liu, L., Liao, Q., Zhang, W., and Zhou, S. (2020). A New Diagnostic Model and Improved Prediction Algorithm for the Heights of Evaporation Ducts. Front. Earth Sci., 8.","DOI":"10.3389\/feart.2020.00102"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"136036","DOI":"10.1109\/ACCESS.2020.3011995","article-title":"A New Short-Term Prediction Method for Estimation of the Evaporation Duct Height","volume":"8","author":"Mai","year":"2020","journal-title":"IEEE Access."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, J., Wu, J., Zhu, Q., Wang, H., Zhou, Y., Jiang, M., Zhang, S., and Wang, B. (2021). Evaporation Duct Height Nowcasting in China\u2019s Yellow Sea Based on Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13081577"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hong, F., and Zhang, Q. (2021). Time Series Analysis of Evaporation Duct Height over South China Sea: A Stochastic Modeling Approach. Atmosphere, 12.","DOI":"10.3390\/atmos12121663"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7795","DOI":"10.1109\/TAP.2021.3076478","article-title":"An Evaporation Duct Height Prediction Model Based on a Long Short-Term Memory Neural Network","volume":"69","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4444","DOI":"10.1109\/TAP.2023.3254201","article-title":"The Comparison of Long Short-Term Memory Neural Network and Deep Forest for the Evaporation Duct Height Prediction","volume":"71","author":"Liao","year":"2023","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chai, X., Li, J., Zhao, J., Wang, W., and Zhao, X. (2022). LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14143448"},{"key":"ref_19","first-page":"40","article-title":"Influence of sea surface temperature on numerical simulation of lower atmospheric duct over the South China Sea","volume":"37","author":"Cheng","year":"2022","journal-title":"Chin. J. Radio Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105720","DOI":"10.1016\/j.jastp.2021.105720","article-title":"Duct climatology over the South China Sea based on European Center for Medium Range Weather Forecast reanalysis data","volume":"222","author":"Cheng","year":"2021","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_21","first-page":"215","article-title":"Comparison between predicted and experimental result of regional evaporation duct over sea","volume":"32","author":"Zhang","year":"2017","journal-title":"Chin. J. Radio Sci."},{"key":"ref_22","unstructured":"ITU-R Recommendation P.453-14 (2012). The Radio Refractive Index: Its Formula and Refractivity Data, ITU."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1109\/TAP.2021.3098582","article-title":"Digital maps of atmospheric refractivity and atmospheric ducts based on a meteorological observation datasets","volume":"70","author":"Hao","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4300514","DOI":"10.1109\/TGRS.2021.3064606","article-title":"HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline","volume":"60","author":"Heidler","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4103508","DOI":"10.1109\/TGRS.2021.3100847","article-title":"Convective Precipitation Nowcasting Using U-Net Model","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2010). An image is worth 16\u00d716 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bojesomo, A., Al-Marzouqi, H., and Liatsis, P. (2021, January 15\u201318). Spatiotemporal Vision Transformer for Short Time Weather Forecasting. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9671442"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/JSTARS.2023.3323729","article-title":"A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting","volume":"17","author":"Bojesomo","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_30","unstructured":"Kavitha, S., Nagamani, H.S., Sumanth, S., Pareek, P.K., and Mageswari, P.U. (2024, January 15\u201316). Weather Forecasting Using Advanced Artificial Intelligence Techniques Based on Swin Transformer Network. Proceedings of the 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India."},{"key":"ref_31","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2021). Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10865","DOI":"10.1109\/TAP.2022.3191160","article-title":"The Diurnal Variation of the Evaporation Duct Height and Its Relationship with Environmental Variables in the South China Sea","volume":"70","author":"Huang","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1175\/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2","article-title":"Improved Magnus Form Approximation of Saturation Vapor Pressure","volume":"35","author":"Alduchov","year":"1996","journal-title":"J. Appl. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1175\/BAMS-86-2-225","article-title":"The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications","volume":"86","author":"Lawrence","year":"2005","journal-title":"Bull. Amer. Meteorol. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1109\/TAP.2023.3240998","article-title":"Prediction of Over-the-Horizon Electromagnetic Wave Propagation in Evaporation Ducts Based on the Gated Recurrent Unit Network Model","volume":"71","author":"Wang","year":"2023","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1175\/JTECH-D-17-0156.1","article-title":"Statistical Analysis of the Quantified Relationship between Evaporation Duct and Oceanic Evaporation for Unstable Conditions","volume":"34","author":"Zhang","year":"2017","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Benhmammouch, O., Caouren, N., and Khenchaf, A. (2009, January 12\u201317). Modeling of roughness effects on electromagnetic waves propagation above sea surface using 3D parabolic equation. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5418218"},{"key":"ref_38","unstructured":"Skolnik, M.I. (1962). Introduction to Radar Systems, McGraw-Hill."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1002\/2015JD023331","article-title":"Microwave hyperspectral measurements for temperature and humidity atmospheric profiling from satellite: The clear-sky case","volume":"120","author":"Aires","year":"2015","journal-title":"J. Geophys. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1175\/1520-0450(2002)041<0434:LBEDMC>2.0.CO;2","article-title":"LKB-Based Evaporation Duct Model Comparison with Buoy Data","volume":"41","author":"Babin","year":"2002","journal-title":"J. Appl. Meteorol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1029\/RS020i004p00887","article-title":"Practical application of an evaporation duct model","volume":"20","author":"Paulus","year":"1985","journal-title":"Radio Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1175\/1520-0450(1997)036<0193:ANMOTO>2.0.CO;2","article-title":"A New Model of the Oceanic Evaporation Duct","volume":"36","author":"Babin","year":"1997","journal-title":"J. Appl. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s10546-006-9048-6","article-title":"50 years of the Monin-Obukhov similarity theory","volume":"119","author":"Foken","year":"2006","journal-title":"Bound. Layer Meteor."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1029\/95JC03205","article-title":"Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment","volume":"101","author":"Fairall","year":"1996","journal-title":"J. Geophys. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1175\/1520-0442(2003)016<0571:BPOASF>2.0.CO;2","article-title":"Bulk Parameterization of Air-Sea Fluxes: Updates and Verification for the COARE Algorithm","volume":"16","author":"Fairall","year":"2003","journal-title":"J. Clim."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1175\/1520-0450(1991)030<0327:FPOLSF>2.0.CO;2","article-title":"Flux Parameterization over Land Surfaces for Atmospheric Models","volume":"30","author":"Beljaars","year":"1991","journal-title":"J. Appl. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1007\/s13351-015-4127-6","article-title":"A New Evaporation Duct Climatology over the South China Sea","volume":"29","author":"Shi","year":"2015","journal-title":"J. Meteorol. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s10546-007-9177-6","article-title":"SHEBA flux-profile relationships in the stable atmospheric boundary layer","volume":"124","author":"Grachev","year":"2007","journal-title":"Bound. Layer Meteor."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"353","DOI":"10.2528\/PIER11012204","article-title":"A Four-Parameter M-Profile Model for the Evaporation Duct Estimation from Radar Clutter","volume":"114","author":"Zhang","year":"2011","journal-title":"Prog. Electromagn. Res."},{"key":"ref_50","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_51","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4203314","DOI":"10.1109\/TGRS.2023.3262749","article-title":"Ca-STANet: Spatiotemporal Attention Network for Chlorophyll-a Prediction with Gap-Filled Remote Sensing Data","volume":"61","author":"Ye","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e2020MS002203","DOI":"10.1029\/2020MS002203","article-title":"WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting","volume":"12","author":"Rasp","year":"2020","journal-title":"J. Adv. Model. Earth Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4227\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:31:22Z","timestamp":1760113882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"references-count":54,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224227"],"URL":"https:\/\/doi.org\/10.3390\/rs16224227","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,13]]}}}