{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:23Z","timestamp":1760146523633,"version":"build-2065373602"},"reference-count":62,"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":"Guiding Project of the Scientific Research Plan of the Hubei Provincial Department of Education","award":["B2023172","21-238-21-15","BK202405","42474057"],"award-info":[{"award-number":["B2023172","21-238-21-15","BK202405","42474057"]}]},{"name":"Guangxi Key Laboratory of Spatial Information and Measurement","award":["B2023172","21-238-21-15","BK202405","42474057"],"award-info":[{"award-number":["B2023172","21-238-21-15","BK202405","42474057"]}]},{"name":"Doctoral Startup Fund Project of Hubei University of Science and Technology","award":["B2023172","21-238-21-15","BK202405","42474057"],"award-info":[{"award-number":["B2023172","21-238-21-15","BK202405","42474057"]}]},{"name":"National Natural Science Foundation of China General Project","award":["B2023172","21-238-21-15","BK202405","42474057"],"award-info":[{"award-number":["B2023172","21-238-21-15","BK202405","42474057"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Zenith Tropospheric Delay (ZTD) is a significant error source affecting the accuracy of certain space geodetic measurements. This study evaluates the performance of Artificial Intelligence (AI) based meteorological models, such as Fengwu and Pangu, in estimating real-time ZTD. The results from these AI models were compared with those obtained from the Global Navigation Satellite System (GNSS), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5), and the third generation of the Global Pressure\u2013Temperature data model (GPT3) to assess their accuracy across different time intervals, seasons, and geographic locations. The findings reveal that AI-driven models, particularly Fengwu, offer higher long-term forecasting accuracy. An analysis of data from 81 stations throughout 2023 indicates that Fengwu\u2019s 7-day ZTD forecast achieved an RMSE of 2.85 cm when compared to GNSS-derived ZTD. However, in oceanic regions and areas with complex climatic dynamics, the Fengwu model exhibited a larger error compared to in other land regions. Additionally, seasonal variations and station altitude were found to influence the accuracy of ZTD predictions, emphasizing the need for detailed modeling in complex climatic zones.<\/jats:p>","DOI":"10.3390\/rs16224231","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T09:59:11Z","timestamp":1731491951000},"page":"4231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Methods and Evaluation of AI-Based Meteorological Models for Zenith Tropospheric Delay Prediction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3370-3955","authenticated-orcid":false,"given":"Si","family":"Xiong","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China"},{"name":"Research Center of Beidou+ Industrial Development of Key Research Institute of Humanities and Social Sciences in Hubei Province, Xianning 437100, China"}]},{"given":"Jiamu","family":"Mei","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China"},{"name":"Research Center of Beidou+ Industrial Development of Key Research Institute of Humanities and Social Sciences in Hubei Province, Xianning 437100, China"}]},{"given":"Xinchuang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China"},{"name":"Research Center of Beidou+ Industrial Development of Key Research Institute of Humanities and Social Sciences in Hubei Province, Xianning 437100, China"}]},{"given":"Ziyu","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China"},{"name":"Research Center of Beidou+ Industrial Development of Key Research Institute of Humanities and Social Sciences in Hubei Province, Xianning 437100, China"}]},{"given":"Liangke","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9156","DOI":"10.1002\/2014JB011552","article-title":"Tropospheric delay ray tracing applied in VLBI analysis","volume":"119","author":"Eriksson","year":"2014","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53-1-53-4","DOI":"10.1029\/2001GL014394","article-title":"Improved Mapping Functions for Atmospheric Refraction Correction in SLR","volume":"29","author":"Mendes","year":"2002","journal-title":"Geophys. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5965","DOI":"10.5194\/amt-9-5965-2016","article-title":"Tropospheric Delay Parameters from Numerical Weather Models for Multi-GNSS Precise Positioning","volume":"9","author":"Lu","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4740","DOI":"10.1002\/grl.50891","article-title":"InSAR Observation and Numerical Modeling of the Water Vapor Signal during a Heavy Rain: A Case Study of the 2008 Seino Event, Central Japan","volume":"40","author":"Kinoshita","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s43020-024-00150-9","article-title":"A Novel Global Grid Model for Soil Moisture Retrieval Considering Geographical Disparity in Spaceborne GNSS-R","volume":"5","author":"Huang","year":"2024","journal-title":"Satell. Navig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15787","DOI":"10.1029\/92JD01517","article-title":"Gps Meteorology\u2014Remote-Sensing of Atmospheric Water-Vapor Using the Global Positioning System","volume":"97","author":"Bevis","year":"1992","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5170","DOI":"10.1002\/joc.5153","article-title":"Long-time Variations of Precipitable Water Vapour Estimated from GPS, MODIS and Radiosonde Observations in Turkey","volume":"37","author":"Gurbuz","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s00190-013-0662-z","article-title":"Troposphere Delays from Space Geodetic Techniques, Water Vapor Radiometers, and Numerical Weather Models over a Series of Continuous VLBI Campaigns","volume":"87","author":"Teke","year":"2013","journal-title":"J. Geod."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote Sensing Platforms and Sensors: A Survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/PL00012860","article-title":"The Role of Ground-Based GPS Meteorological Observations in Numerical Weather Prediction","volume":"4","author":"Gutman","year":"2001","journal-title":"GPS Solut."},{"key":"ref_11","unstructured":"Leandro, R., Santos, M., and Langley, R.B. (2006, January 18\u201320). UNB Neutral Atmosphere Models: Development and Performance. Proceedings of the 2006 National Technical Meeting of the Institute of Navigation, Monterey, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10291-007-0077-5","article-title":"UNB3m_pack: A Neutral Atmosphere Delay Package for Radiometric Space Techniques","volume":"12","author":"Leandro","year":"2008","journal-title":"GPS Solut."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5194\/npg-23-127-2016","article-title":"An Improved Global Zenith Tropospheric Delay Model GZTD2 Considering Diurnal Variations","volume":"23","author":"Yao","year":"2016","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5276","DOI":"10.1109\/TGRS.2018.2812850","article-title":"IGGtrop_SH and IGGtrop_rH: Two Improved Empirical Tropospheric Delay Models Based on Vertical Reduction Functions","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s43020-022-00088-w","article-title":"Refining the ERA5-Based Global Model for Vertical Adjustment of Zenith Tropospheric Delay","volume":"3","author":"Zhu","year":"2022","journal-title":"Satell. Navig."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s10291-021-01138-7","article-title":"A Global Grid Model for the Correction of the Vertical Zenith Total Delay Based on a Sliding Window Algorithm","volume":"25","author":"Huang","year":"2021","journal-title":"GPS Solut."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1007\/s11433-011-4530-7","article-title":"Establishment of a New Tropospheric Delay Correction Model over China Area","volume":"54","author":"Song","year":"2011","journal-title":"Sci. China Phys. Mech. Astron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1017\/S0373463300001107","article-title":"Assessment of EGNOS Tropospheric Correction Model","volume":"54","author":"Penna","year":"2001","journal-title":"J. Navig."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ding, M., Ding, J., Peng, Z., Su, M., and Sun, T. (2024). Developments of Empirical Models for Vertical Adjustment of Precipitable Water Vapor Measured by GNSS. Adv. Space Res.","DOI":"10.1016\/j.asr.2024.08.039"},{"key":"ref_20","first-page":"1","article-title":"A Global Grid Model for the Estimation of Zenith Tropospheric Delay Considering the Variations at Different Altitudes","volume":"2023","author":"Huang","year":"2023","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10291-022-01354-9","article-title":"An Improved Global Grid Model for Calibrating Zenith Tropospheric Delay for GNSS Applications","volume":"27","author":"Huang","year":"2023","journal-title":"GPS Solut."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s00190-007-0135-3","article-title":"Short Note: A Global Model of Pressure and Temperature for Geodetic Applications","volume":"81","author":"Boehm","year":"2007","journal-title":"J. Geod."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10291-014-0403-7","article-title":"Development of an Improved Empirical Model for Slant Delays in the Troposphere (GPT2w)","volume":"19","author":"Schindelegger","year":"2015","journal-title":"GPS Solut."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s00190-017-1066-2","article-title":"VMF3\/GPT3: Refined Discrete and Empirical Troposphere Mapping Functions","volume":"92","author":"Landskron","year":"2018","journal-title":"J. Geod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1002\/grl.50288","article-title":"GPT2: Empirical Slant Delay Model for Radio Space Geodetic Techniques","volume":"40","author":"Lagler","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1029\/RS006i003p00357","article-title":"Tropospheric Effect on Electromagnetically Measured Range: Prediction from Surface Weather Data","volume":"6","author":"Hopfield","year":"1971","journal-title":"Radio Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF02521844","article-title":"Contributions to the Theory of Atmospheric Refraction","volume":"105","author":"Saastamoinen","year":"1972","journal-title":"Bull. G\u00e9od."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1029\/JB083iB04p01825","article-title":"An Easily Implemented Algorithm for the Tropospheric Range Correction","volume":"83","author":"Black","year":"1978","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106247","DOI":"10.1016\/j.atmosres.2022.106247","article-title":"High-Precision GNSS PWV Retrieval Using Dense GNSS Sites and in-Situ Meteorological Observations for the Evaluation of MERRA-2 and ERA5 Reanalysis Products over China","volume":"276","author":"Huang","year":"2022","journal-title":"Atmos. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e2021EA001796","DOI":"10.1029\/2021EA001796","article-title":"A Global Assessment of Precipitable Water Vapor Derived from GNSS Zenith Tropospheric Delays with ERA5, NCEP FNL, and NCEP GFS Products","volume":"8","author":"Chen","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cao, L., Zhang, B., Li, J., Yao, Y., Liu, L., Ran, Q., and Xiong, Z. (2021). A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products. Remote Sens., 13.","DOI":"10.3390\/rs13132644"},{"key":"ref_32","unstructured":"Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., and Su, R. (2023). FengWu: Pushing the Skillful Global Medium-Range Weather Forecast beyond 10 Days Lead. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/s41586-023-06185-3","article-title":"Accurate Medium-Range Global Weather Forecasting with 3D Neural Networks","volume":"619","author":"Bi","year":"2023","journal-title":"Nature"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1126\/science.adi2336","article-title":"Learning Skillful Medium-Range Global Weather Forecasting","volume":"382","author":"Lam","year":"2023","journal-title":"Science"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1038\/s41612-023-00512-1","article-title":"FuXi: A Cascade Machine Learning Forecasting System for 15-Day Global Weather Forecast","volume":"6","author":"Chen","year":"2023","journal-title":"NPJ Clim. Atmos. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1016\/j.asr.2024.02.039","article-title":"A Deep Learning-Based Model for Tropospheric Wet Delay Prediction Based on Multi-Layer 1D Convolution Neural Network","volume":"73","author":"Bi","year":"2024","journal-title":"Adv. Space Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s00190-023-01722-4","article-title":"TropNet: A Deep Spatiotemporal Neural Network for Tropospheric Delay Modeling and Forecasting","volume":"97","author":"Lu","year":"2023","journal-title":"J. Geod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"20200097","DOI":"10.1098\/rsta.2020.0097","article-title":"Can Deep Learning Beat Numerical Weather Prediction?","volume":"379","author":"Schultz","year":"2021","journal-title":"Phil. Trans. R. Soc. A"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1038\/s41612-024-00638-w","article-title":"Do AI Models Produce Better Weather Forecasts than Physics-Based Models? A Quantitative Evaluation Case Study of Storm Ciar\u00e1n","volume":"7","author":"Dacre","year":"2024","journal-title":"NPJ Clim. Atmos. Sci."},{"key":"ref_40","unstructured":"Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., and Azizzadenesheli, K. (2022). FourCastNet: A Global Data-Driven High-Resolution Weather Model Using Adaptive Fourier Neural Operators. arXiv."},{"key":"ref_41","unstructured":"Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q. (2022). Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"54051","DOI":"10.1088\/1748-9326\/ad41f0","article-title":"Improvement of Disastrous Extreme Precipitation Forecasting in North China by Pangu-Weather AI-Driven Regional WRF Model","volume":"19","author":"Xu","year":"2024","journal-title":"Environ. Res. Lett."},{"key":"ref_43","first-page":"29","article-title":"GPS-PWV estimation and validation with radiosonde data and numerical weather prediction model in Antarctica","volume":"17","year":"2012","journal-title":"GPS Solut."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s10291-022-01390-5","article-title":"An Investigation of a Voxel-Based Atmospheric Pressure and Temperature Model","volume":"27","author":"Sun","year":"2023","journal-title":"GPS Solut."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, L., Guo, L., Liu, L., Chen, H., Chen, J., and Xie, S. (2020). Evaluation of the ZWD\/ZTD Values Derived from MERRA-2 Global Reanalysis Products Using GNSS Observations and Radiosonde Data. Sensors, 20.","DOI":"10.3390\/s20226440"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/TGRS.2015.2456099","article-title":"A Comprehensive Evaluation and Analysis of the Performance of Multiple Tropospheric Models in China Region","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"R\u00fceger, J.M. (2002, January 19\u201326). Refractive Index Formulae for Radio Waves. Proceedings of the FIG XXII International Congress, Washington, DC, USA."},{"key":"ref_48","unstructured":"Mendes, V. (1999). Modeling the Neutral-Atmospheric Propagation Delay in Radiometric Space Techniques. [Ph.D. Dissertation, University of New Brunswick]."},{"key":"ref_49","unstructured":"Wallace, J.M. (2006). Atmospheric Science: An Introductory Survey, Elsevier."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1002\/2014GL060271","article-title":"An Improved Model for Calculating Tropospheric Wet Delay","volume":"41","author":"Dousa","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"B4","DOI":"10.1029\/2011JB008916","article-title":"The Development and Evaluation of the Earth Gravitational Model 2008 (EGM2008)","volume":"117","author":"Pavlis","year":"2012","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_52","unstructured":"(2024, October 13). The EGM2008 Global Gravitational Model. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2008AGUFM.G22A..01P\/abstract."},{"key":"ref_53","first-page":"72","article-title":"Research on GPS Inversion of Atmospheric Precipitable Water Based on In-terpolated Atmospheric Pressure","volume":"33","author":"Liu","year":"2013","journal-title":"Geod. Geodyn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1002\/2015JD024181","article-title":"Water Vapor-Weighted Mean Temperature and Its Impact on the Determination of Precipitable Water Vapor and Its Linear Trend","volume":"121","author":"Wang","year":"2016","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10291-020-01014-w","article-title":"Considering Different Recent Advancements in GNSS on Real-Time Zenith Troposphere Estimates","volume":"24","author":"Hadas","year":"2020","journal-title":"GPS Solut."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s10291-017-0644-3","article-title":"A Simplified GNSS Tropospheric Delay Model Based on the Nonlinear Hypothesis","volume":"21","author":"Sun","year":"2017","journal-title":"GPS Solut."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Yao, Y., Cao, X., Zhou, F., and Xia, P. (2018). An Optimal Tropospheric Tomography Method Based on the Multi-GNSS Observations. Remote Sens., 10.","DOI":"10.3390\/rs10020234"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s10291-020-0974-4","article-title":"An improved GNSS tropospheric tomography method with the GPT2w model","volume":"24","author":"Zhao","year":"2020","journal-title":"GPS Solut."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00190-023-01804-3","article-title":"Monitoring Urban Heat Island Intensity Based on GNSS Tomography Technique","volume":"98","author":"Xia","year":"2024","journal-title":"J. Geod."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10291-023-01401-z","article-title":"An Adaptive-Degree Layered Function-Based Method to GNSS Tropospheric Tomography","volume":"27","author":"Zhang","year":"2023","journal-title":"GPS Solut."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ding, N., Tan, X., Liu, X., He, Z., Zhang, Y., Wang, Y., Zhang, S., Holden, L., and Zhang, K. (2023). Adaptive Voxel-Based Model for the Dynamic Determination of Tomographic Region. Remote Sens., 15.","DOI":"10.3390\/rs15020492"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1038\/s41586-023-06963-z","article-title":"Global Population Profile of Tropical Cyclone Exposure from 2002 to 2019","volume":"626","author":"Jing","year":"2024","journal-title":"Nature"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4231\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:31:41Z","timestamp":1760113901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"references-count":62,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224231"],"URL":"https:\/\/doi.org\/10.3390\/rs16224231","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,11,13]]}}}