{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:25:46Z","timestamp":1783967146571,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020084","name":"Guangzhou Science and Technology Bureau","doi-asserted-by":"publisher","award":["202206010016"],"award-info":[{"award-number":["202206010016"]}],"id":[{"id":"10.13039\/501100020084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020084","name":"Guangzhou Science and Technology Bureau","doi-asserted-by":"publisher","award":["42105073"],"award-info":[{"award-number":["42105073"]}],"id":[{"id":"10.13039\/501100020084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020084","name":"Guangzhou Science and Technology Bureau","doi-asserted-by":"publisher","award":["KYTZ202217"],"award-info":[{"award-number":["KYTZ202217"]}],"id":[{"id":"10.13039\/501100020084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202206010016"],"award-info":[{"award-number":["202206010016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42105073"],"award-info":[{"award-number":["42105073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYTZ202217"],"award-info":[{"award-number":["KYTZ202217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chengdu University of Information Technology Research Fund","award":["202206010016"],"award-info":[{"award-number":["202206010016"]}]},{"name":"Chengdu University of Information Technology Research Fund","award":["42105073"],"award-info":[{"award-number":["42105073"]}]},{"name":"Chengdu University of Information Technology Research Fund","award":["KYTZ202217"],"award-info":[{"award-number":["KYTZ202217"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.<\/jats:p>","DOI":"10.3390\/rs15153838","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T10:57:33Z","timestamp":1690973853000},"page":"3838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuyan","family":"Luo","sequence":"first","affiliation":[{"name":"Plateau Atmospheres and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6100-2673","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of China Meteorological Administration Atmospheric Sounding, School of Electrical Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taofeng","family":"Gu","sequence":"additional","affiliation":[{"name":"Guangzhou Meteorological Observatory, Guangzhou 511430, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenglin","family":"Wang","sequence":"additional","affiliation":[{"name":"Plateau Atmospheres and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Hainan International Commercial Aerospace Launch Co., Ltd., Wenchang 571300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Yue","sequence":"additional","affiliation":[{"name":"Guangzhou Emergency Warning Information Release Center, Guangzhou 511430, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangsheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangzhou Meteorological Observatory, Guangzhou 511430, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Langfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of China Meteorological Administration Atmospheric Sounding, School of Electrical Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyang","family":"Pu","sequence":"additional","affiliation":[{"name":"Key Laboratory of China Meteorological Administration Atmospheric Sounding, School of Electrical Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Tang","sequence":"additional","affiliation":[{"name":"Zhongshan Meteorological Service, Zhongshan 528400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Plateau Atmospheres and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6816","DOI":"10.1002\/jgrd.50483","article-title":"Validation of AIRS\/AMSU-A water vapor and temperature data with in situ aircraft observations from the surface to UT\/LS from 87\u00b0N\u201367\u00b0S","volume":"118","author":"Diao","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Osei, M.A., Amekudzi, L.K., Ferguson, C.R., and Danuor, S.K. (2020). Inter-comparison of AIRS temperature and relative humidity profiles with AMMA and DACCIWA radiosonde observations over West Africa. Remote Sens., 12.","DOI":"10.1002\/essoar.10502009.1"},{"key":"ref_3","first-page":"222","article-title":"Characteristics of the Atmosphere Remote Sensed by the Ground-Based 12-Channel Radiometer","volume":"22","author":"Liu","year":"2007","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_4","first-page":"214","article-title":"Infrared Remote Sensing of Clear Atmosphere and Related Inversion Problem. Part II: Experimental Study","volume":"21","author":"Li","year":"1997","journal-title":"Chin. J. Atmos. Sci."},{"key":"ref_5","first-page":"23","article-title":"A Preliminary Study of the Retrieval Methods for Atmosphere and Humidity profiles","volume":"20","author":"Wang","year":"2008","journal-title":"Chin. Remote Sens. Resour."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Qing, Z.P., Zhu, M., Zhang, F.G., Wang, H., Liu, Y., Shi, Z., and Yu, Q. (2022). Application of Random Forest Algorithm on Tornado Detection. Remote Sens., 14.","DOI":"10.3390\/rs14194909"},{"key":"ref_7","first-page":"390","article-title":"Quality Control method of single station surface air temperature data based on EEMD-CES","volume":"42","author":"Ye","year":"2019","journal-title":"Trans. Atmos. Sci."},{"key":"ref_8","unstructured":"Cimini, C., Marzano, F.S., Ciotti, P., Cimini, D., Westwater, E.R., Han, Y., Keihm, S.J., and Ware, R. (2004, January 22\u201326). Atmospheric Microwave Radiative Models Study Based on Ground-Based Multichannel Radiometer Observations in the 20\u201360 GHz Band. Proceedings of the Fourteenth ARM Science Team Meeting Proceedings, Albuquerque, NM, USA."},{"key":"ref_9","first-page":"252","article-title":"Retrieval of Atmospheric Temperature Profiles with Neural Network Inversion of Microwave Radiometer Data in 6 Channels Near 118.75 GHz","volume":"26","author":"Yao","year":"2006","journal-title":"J. Meteorol. Sci."},{"key":"ref_10","first-page":"1514","article-title":"Research of BP Neural Network for Microwave Radiometer Remote Sensing Retrieval of Temperature, Relative Humidity, Cloud Liquid Water Profiles","volume":"29","author":"Liu","year":"2010","journal-title":"J. Plateau. Meteorol."},{"key":"ref_11","first-page":"711","article-title":"Environmental Thermal Radiation Interference on Atmospheric Brightness Temperature Measurement with Ground-based K-band Microwave Radiometer","volume":"25","author":"Wang","year":"2014","journal-title":"J. Appl. Meteorol. Sci."},{"key":"ref_12","first-page":"54","article-title":"The Principle and Error Analysis of Microwave Radiometer MP-3000A","volume":"3","author":"Zhao","year":"2009","journal-title":"Desert Oasis Meteorol."},{"key":"ref_13","first-page":"193","article-title":"Cloud Influence on Atmospheric Humidity Profile Retrieval by Ground-based Microwave Radiometer","volume":"26","author":"Che","year":"2015","journal-title":"J. Appl. Meteorol. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105725","DOI":"10.1016\/j.jastp.2021.105725","article-title":"Analysis on the solar influence to brightness temperatures observed with a ground-based microwave radiometer","volume":"222","author":"Pan","year":"2021","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qi, Y.J., Fan, S.Y., Mao, J.J., Li, B., Guo, C.W., and Zhang, S.T. (2021). Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing. Atmosphere, 12.","DOI":"10.3390\/atmos12050551"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qi, Y., Fan, S., Li, B., Mao, J., and Lin, D. (2022). Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing. Atmosphere, 13.","DOI":"10.3390\/atmos13010074"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Q., Wei, M., Wang, Z., Jiang, S., and Chu, Y. (2021). Improving the Retrieval of Cloudy Atmospheric Profiles from Brightness Temperatures Observed with a Ground-Based Microwave Radiometer. Atmosphere, 12.","DOI":"10.3390\/atmos12050648"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1175\/1520-0450(1983)022<0807:AAPOTT>2.0.CO;2","article-title":"An Automatic Profiler of the Temperature, Wind and Humidity in the Troposphere","volume":"22","author":"Hogg","year":"1983","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_19","unstructured":"Rodgers, C.D. (2008). Inverse Methods for Atmospheric Sounding\u2014Theory and Practice, World Scientific."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s00703-018-0588-3","article-title":"An improvement of the retrieval of temperature and relative humidity profiles from a combination of active and passive remote sensing","volume":"131","author":"Che","year":"2018","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8427","DOI":"10.1109\/TGRS.2020.2987896","article-title":"A Deep Learning Approach to Improve the Retrieval of Temperature and Humidity Profiles from a Ground-Based Microwave Radiometer","volume":"58","author":"Yan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2015.09.009","article-title":"Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and the Advanced Microwave Scanning Radiometer","volume":"170","author":"Xue","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"243","article-title":"0\u201310 KM Temperature and Humidity Profiles Retrieval from Ground-based Microwave Radiometer","volume":"24","author":"Bao","year":"2018","journal-title":"J. Trop. Meteorol."},{"key":"ref_25","first-page":"97","article-title":"Atmospheric temperature and humidity profile retrievals based on BP neural network and genetic algorithm","volume":"36","author":"Zhang","year":"2020","journal-title":"J. Trop. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104678","DOI":"10.1016\/j.atmosres.2019.104678","article-title":"Linear correction method for improved atmospheric vertical profile retrieval based on ground-based microwave radiometer","volume":"232","author":"Zhao","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_27","first-page":"105962","article-title":"Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques","volume":"266","author":"Gregori","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_28","first-page":"7113","article-title":"Seasonal Forecast of Nonmonsoonal Winter Precipitation over the Eurasian Continent Using Machine-Learning Models","volume":"34","author":"Qian","year":"2021","journal-title":"J. Clim."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1175\/JCLI-D-21-0447.1","article-title":"Correction of Overestmation in Observed Land Surface Temperatures Baesd on Machine Learning Models","volume":"35","author":"Liu","year":"2022","journal-title":"J. Clim."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5659","DOI":"10.1175\/JCLI-D-18-0756.1","article-title":"Ocean Salinity as a Precursor of Summer Rainfall over the East Asian Monsoon Region","volume":"32","author":"Chen","year":"2019","journal-title":"J. Clim."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1175\/JTECH-D-18-0087.1","article-title":"On the Use of Geophysical Parameters for the Top-of-Atmosphere Shortwave Clear-Sky Radiance-to-Flux Conversion in EarthCARE","volume":"36","author":"Tornow","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101394","DOI":"10.1016\/j.apr.2022.101394","article-title":"Establishment of aerosol optical depth dataset in the Sichuan Basin by the random forest approach","volume":"13","author":"Jiang","year":"2022","journal-title":"Atmos. Pollut. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2009). Learning Deep Architectures for AI, Now Publishers Inc.","DOI":"10.1561\/9781601982957"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural. Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"425","DOI":"10.3390\/s17020425","article-title":"A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals","volume":"17","author":"Zhang","year":"2017","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1175\/2010JTECHA1479.1","article-title":"A Study of a Retrieval Method for Temperature and Humidity Profiles from Microwave Radiometer Observations Based on Principal Component Analysis and Stepwise Regression","volume":"28","author":"Tan","year":"2011","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_37","first-page":"325","article-title":"Evaluation of microwave radiometer inversion products","volume":"38","author":"Liu","year":"2010","journal-title":"Meteorol. Sci. Technol."},{"key":"ref_38","first-page":"89","article-title":"Applicability analysis of temperature and humidity data from RPG-HATPRO microwave Radiometer inversion","volume":"33","author":"Li","year":"2017","journal-title":"J. Meteorol. Environ."},{"key":"ref_39","first-page":"125","article-title":"Study on Neural Network Algorithm for Atmospheric Profile Based on Microwave Radiometer in Plateau Region","volume":"41","author":"Tian","year":"2021","journal-title":"Plateau. Mt. Meteorol. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1175\/1520-0450(1995)034<2243:DOCVSF>2.0.CO;2","article-title":"Determination of Cloud Vertical Structure from Upper-Air Observations","volume":"34","author":"Wang","year":"1995","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1175\/1520-0442(1995)008<0550:CLTFAC>2.0.CO;2","article-title":"Cloud Layer Thicknesses from a Combination of Surface and Upper-Air Observations","volume":"8","author":"Poore","year":"1995","journal-title":"J. Clim."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s41324-019-00299-5","article-title":"Improving accuracy of land surface temperature prediction model based on deep-learning","volume":"28","author":"Choe","year":"2020","journal-title":"Spat. Inf. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"23840","DOI":"10.1109\/ACCESS.2021.3056568","article-title":"Emerging Technologies of Deep Learning Models Development for Pavement Temperature Prediction","volume":"9","author":"Milad","year":"2021","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hou, P., Guo, P., Wu, P., Wang, J., Gangopadhyay, A., and Zhang, Z. (2020, January 14\u201317). A Deep Learning Model for Detecting Dust in Earth\u2019s Atmosphere from Satellite Remote Sensing Data. Proceedings of the IEEE International Conference on Smart Computing SMARTCOMP, Bologna, Italy.","DOI":"10.1109\/SMARTCOMP50058.2020.00045"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14285","DOI":"10.1038\/s41598-018-32511-1","article-title":"Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.ygeno.2012.04.003","article-title":"Random Forests for Genomic Data Analysis","volume":"99","author":"Chen","year":"2012","journal-title":"Genomics"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s11634-018-0318-1","article-title":"An Efficient Random Forests Algorithm for High Dimensional Data Classification","volume":"12","author":"Wang","year":"2018","journal-title":"Adv. Data Anal. Classif."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/10485252.2017.1404598","article-title":"Multiple Predicting K-fold Cross-Validation for Model Selection","volume":"30","author":"Jung","year":"2018","journal-title":"J. Nonparametr. Stat."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/10485252.2015.1010532","article-title":"A K-fold Averaging Cross-Validation Procedure","volume":"27","author":"Jung","year":"2015","journal-title":"J. Nonparametr. Stat."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance Evaluation of Classification Algorithms by K-fold and leave-one-out Cross Validation","volume":"48","author":"Wang","year":"2015","journal-title":"Pattern Recognit."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:24:07Z","timestamp":1760127847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":55,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153838"],"URL":"https:\/\/doi.org\/10.3390\/rs15153838","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,1]]}}}