{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T14:07:01Z","timestamp":1768918021052,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"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":["41830109"],"award-info":[{"award-number":["41830109"]}],"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":["41871254"],"award-info":[{"award-number":["41871254"]}],"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>Due to the advantage of geostationary satellites, Himawari-8\/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8\/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R2 for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R2 = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R2 increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training.<\/jats:p>","DOI":"10.3390\/rs14132967","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T04:12:01Z","timestamp":1655871121000},"page":"2967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Himawari-8\/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9245-1102","authenticated-orcid":false,"given":"Yuanlin","family":"Chen","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS), Beijing 100101, China"},{"name":"School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China"}]},{"given":"Meng","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS), Beijing 100101, China"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS), Beijing 100101, China"}]},{"given":"Zhongbin","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS), Beijing 100101, China"}]},{"given":"Jinhua","family":"Tao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS), Beijing 100101, China"}]},{"given":"Zhibao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China"}]},{"given":"Liangfu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS), Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.atmosenv.2014.06.019","article-title":"China Collection 2.0: The aerosol optical depth dataset from the synergetic retrieval of aerosol properties algorithm","volume":"95","author":"Xue","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.4209\/aaqr.2016.11.0484","article-title":"An improved aerosol optical depth map based on machine-learning and MODIS data: Development and application in south America","volume":"17","author":"Lanzaco","year":"2017","journal-title":"Aerosol Air Qual. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.atmosres.2017.08.018","article-title":"A campaign for investigating aerosol optical properties during winter hazes over Shijiazhuang, China","volume":"198","author":"Qin","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Oh, H.J., Ma, Y., and Kim, J. (2020). Human inhalation exposure to aerosol and health effect: Aerosol monitoring and modelling regional deposited doses. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17061923"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1016\/j.apr.2019.05.005","article-title":"Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey","volume":"10","author":"Zeydan","year":"2019","journal-title":"Atmos. Pollut. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1093\/rpd\/ncp231","article-title":"MODIS and OMI satellite observations supporting air quality monitoring","volume":"137","author":"Cacciari","year":"2009","journal-title":"Radiat. Prot. Dosim."},{"key":"ref_7","first-page":"100716","article-title":"Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS","volume":"26","author":"Handschuh","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12673","DOI":"10.1002\/2013JD020449","article-title":"Suomi-NPP VIIRS aerosol algorithms and data products","volume":"118","author":"Jackson","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Du, W., Qin, Z., Fan, J., Zhao, C., Huang, Q., Cao, K., and Abbasi, B. (2021). Land surface temperature retrieval from Fengyun-3D Medium Resolution Spectral Imager II (FY-3D MERSI-II) data with the improved Two-Factor Split-Window algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13245072"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/36.700992","article-title":"Multi-angle Imaging SpectroRadiometer (MISR)\u2014Instrument description and experiment overview","volume":"36","author":"Diner","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/36.763270","article-title":"Validation of the first algorithm applied for deriving the aerosol properties over the ocean using the POLDER\/ADEOS Measurements","volume":"37","author":"Goloub","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1175\/1520-0469(2002)059<0262:GTCARO>2.0.CO;2","article-title":"Global two-channel AVHRR retrievals of aerosol properties over the ocean for the period of NOAA-9 observations and preliminary retrievals using NOAA-7 and NOAA-11 data","volume":"59","author":"Geogdzhayev","year":"2002","journal-title":"J. Atmos. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.5194\/acp-7-3115-2007","article-title":"Simulation study of the aerosol information content in OMI spectral reflectance measurements","volume":"7","author":"Veihelmann","year":"2007","journal-title":"Atmos. Chem. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3180","DOI":"10.1109\/TGRS.2006.879540","article-title":"Deep blue retrievals of Asian aerosol properties during ACE-Asia","volume":"44","author":"Hsu","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.5194\/amt-5-1761-2012","article-title":"Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS","volume":"5","author":"Sayer","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"33","DOI":"10.15191\/nwajom.2018.0604","article-title":"Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI)","volume":"6","author":"Schmit","year":"2018","journal-title":"J. Oper. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1016\/j.asr.2021.11.018","article-title":"A comprehensive geometric quality assessment approach for MSG SEVIRI imagery","volume":"69","author":"Kocaman","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, J., Shu, J., and Guo, W. (2020). Biases characteristics assessment of the Advanced Geosynchronous Radiation Imager (AGRI) measurement on board Fengyun\u20134A geostationary satellite. Remote Sens., 12.","DOI":"10.3390\/rs12182871"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Molina Garcia, V., Sasi, S., Efremenko, D.S., and Loyola, D. (2019). Improvement of EPIC\/DSCOVR image registration by means of automatic coastline detection. Remote Sens., 11.","DOI":"10.3390\/rs11151747"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.atmosres.2018.02.021","article-title":"A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8","volume":"207","author":"Yan","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Levy, R.C., Remer, L.A., and Dubovik, O. (2007). Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. J. Geophys. Res. Atmos., 112.","DOI":"10.1029\/2006JD007815"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1175\/1520-0442(1999)012<1268:SEOAOC>2.0.CO;2","article-title":"Scale effects on averaging of cloud droplet and aerosol number concentrations: Observations and models","volume":"12","author":"Gultepe","year":"1999","journal-title":"J. Clim."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1175\/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2","article-title":"Variability of absorption and optical properties of key aerosol types observed in worldwide locations","volume":"59","author":"Dubovik","year":"2002","journal-title":"J. Atmos. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/01431169508954410","article-title":"Operational bi-angle approach to retrieve the Earth surface albedo from AVHRR data in the visible band","volume":"16","author":"Xue","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.5194\/amt-7-2411-2014","article-title":"Retrieval of aerosol optical depth over land surfaces from AVHRR data","volume":"7","author":"Mei","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1109\/JSTARS.2015.2396491","article-title":"Development and validation of a robust algorithm for retrieving aerosol optical depth over land from MODIS Data","volume":"8","author":"Huang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Levy, R.C., Remer, L.A., Mattoo, S., Vermote, E.F., and Kaufman, Y.J. (2007). Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res. Atmos., 112.","DOI":"10.1029\/2006JD007811"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9296","DOI":"10.1002\/jgrd.50712","article-title":"Enhanced Deep Blue aerosol retrieval algorithm: The second generation","volume":"118","author":"Hsu","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TGRS.2004.824067","article-title":"Aerosol properties over bright-reflecting source regions","volume":"42","author":"Hsu","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012059","DOI":"10.1088\/1755-1315\/20\/1\/012059","article-title":"Accuracy assessment of Terra-MODIS aerosol optical depth retrievals","volume":"20","author":"Safarpour","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1109\/TGRS.2018.2854743","article-title":"A Dark Target method for Himawari-8\/AHI aerosol retrieval: Application and validation","volume":"57","author":"Ge","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yu, F., and Wu, X. (2016). Radiometric inter-calibration between Himawari-8 AHI and S-NPP VIIRS for the solar reflective bands. Remote Sens., 8.","DOI":"10.3390\/rs8030165"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"193","DOI":"10.2151\/jmsj.2018-039","article-title":"Common retrieval of aerosol properties for imaging satellite sensors","volume":"96B","author":"Yoshida","year":"2018","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.atmosenv.2018.11.024","article-title":"Validation of Himawari-8 aerosol optical depth retrievals over China","volume":"199","author":"Zhang","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105248","DOI":"10.1016\/j.atmosres.2020.105248","article-title":"Evaluation and possible uncertainty source analysis of JAXA Himawari-8 aerosol optical depth product over China","volume":"248","author":"Gao","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vapnik, V., and Vapnik, V. (1995). The Natural of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_37","unstructured":"Hempel, S., Shetty, K., Shekelle, P.G., Rubenstein, L.V., Danz, M., Johnsen, B., and Dalal, S.J. (2012). Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.ecolind.2018.04.022","article-title":"Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000\u20132016) on the controls of fire activity in Namibia from spatial predictive models","volume":"91","author":"Mayr","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"150721","DOI":"10.1016\/j.scitotenv.2021.150721","article-title":"Machine learning-based estimation of ground-level NO2 concentrations over China","volume":"807","author":"Chi","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105821","DOI":"10.1016\/j.atmosres.2021.105821","article-title":"Ground-level NO2 concentration estimation based on OMI tropospheric NO2 and its spatiotemporal characteristics in typical regions of China","volume":"264","author":"Chi","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zeng, Q.L., Chen, L.F., Zhu, H., Wang, Z.F., Wang, X.H., Zhang, L., Gu, T.Y., Zhu, G.Y., and Zhang, Y. (2018). Satellite-based estimation of hourly PM2.5 concentrations using a Vertical-Humidity Correction method from Himawari-AOD in Hebei. Sensors, 18.","DOI":"10.3390\/s18103456"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Taheri Shahraiyni, H., and Sodoudi, S. (2016). Statistical modeling approaches for PM10 prediction in urban areas; A review of 21st-century studies. Atmosphere, 7.","DOI":"10.3390\/atmos7020015"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"073514","DOI":"10.1117\/1.JRS.7.073514","article-title":"Global bias adjustment for MODIS aerosol optical thickness using neural network","volume":"7","author":"Albayrak","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"379","DOI":"10.5194\/amt-4-379-2011","article-title":"An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals","volume":"4","author":"Hyer","year":"2011","journal-title":"Atmos. Meas. Tech."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kolios, S., and Hatzianastassiou, N. (2019). Quantitative aerosol optical depth detection during dust outbreaks from meteosat Imagery using an Artificial Neural Network model. Remote Sens., 11.","DOI":"10.3390\/rs11091022"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107042","DOI":"10.1016\/j.jqsrt.2020.107042","article-title":"Machine learning algorithms for retrievals of aerosol and ocean color products from FY-3D MERSI-II instrument","volume":"250","author":"Fan","year":"2020","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dutra, E., Munoz-Sabater, J., Boussetta, S., Komori, T., Hirahara, S., and Balsamo, G. (2020). Environmental lapse rate for high-resolution land surface downscaling: An application to ERA5. Earth Space Sci., 7.","DOI":"10.1029\/2019EA000984"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(98)00031-5","article-title":"AERONET\u2014A federated instrument network and data archive for aerosol characterization","volume":"66","author":"Holben","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"116846","DOI":"10.1016\/j.atmosenv.2019.116846","article-title":"Spatiotemporal variations and trends of MODIS C6.1 Dark Target and Deep Blue merged aerosol optical depth over China during 2000\u20132017","volume":"214","author":"Xie","year":"2019","journal-title":"Atmos. Enviroment"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., Levy, R., and Reid, J.S. (2011). Multiangle Implementation of Atmospheric Correction (MAIAC): 2. Aerosol algorithm. J. Geophys. Res., 116.","DOI":"10.1029\/2010JD014986"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5741","DOI":"10.5194\/amt-11-5741-2018","article-title":"MODIS Collection 6 MAIAC algorithm","volume":"11","author":"Lyapustin","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/2017JD027339","article-title":"Meteorological and land surface properties impacting sea breeze extent and aerosol distribution in a dry environment","volume":"123","author":"Igel","year":"2018","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jes.2020.04.045","article-title":"Estimation of aerosol optical depth in relation to meteorological parameters over eastern and western routes of China Pakistan economic corridor","volume":"99","author":"Khalid","year":"2021","journal-title":"J. Environ. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_58","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6078","DOI":"10.1016\/j.atmosenv.2008.03.043","article-title":"Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China","volume":"42","author":"Chen","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"13601","DOI":"10.5194\/acp-18-13601-2018","article-title":"Impact of low-pressure systems on winter heavy air pollution in the northwest Sichuan Basin, China","volume":"18","author":"Ning","year":"2018","journal-title":"Atmos. Chem. Phys."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/2967\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:36:40Z","timestamp":1760139400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/2967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,21]]},"references-count":60,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14132967"],"URL":"https:\/\/doi.org\/10.3390\/rs14132967","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,21]]}}}