{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T17:08:32Z","timestamp":1776532112085,"version":"3.51.2"},"reference-count":68,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.<\/jats:p>","DOI":"10.3390\/rs15174318","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T09:24:53Z","timestamp":1693560293000},"page":"4318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6111-152X","authenticated-orcid":false,"given":"Simone","family":"Lolli","sequence":"first","affiliation":[{"name":"CNR-Institute of Methodologies for Environmental Analysis (IMAA), Contrada S. Loja snc, 85050 Tito Scalo, PZ, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mona, L., Amodeo, A., Pandolfi, M., and Pappalardo, G. (2006). Saharan dust intrusions in the Mediterranean area: Three years of Raman lidar measurements. J. Geophys. Res. Atmos., 111.","DOI":"10.1029\/2005JD006569"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14057","DOI":"10.5194\/acp-16-14057-2016","article-title":"Aerosol meteorology of Maritime Continent for the 2012 7SEAS southwest monsoon intensive study\u2014Part 2: Philippine receptor observations of fine-scale aerosol behavior","volume":"16","author":"Reid","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1175\/JAMC-D-15-0217.1","article-title":"Daytime cirrus cloud top-of-the-atmosphere radiative forcing properties at a midlatitude site and their global consequences","volume":"55","author":"Campbell","year":"2016","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11633","DOI":"10.5194\/acp-14-11633-2014","article-title":"Observations of rapid aerosol optical depth enhancements in the vicinity of polluted cumulus clouds","volume":"14","author":"Eck","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lolli, S., Khor, W.Y., Matjafri, M.Z., and Lim, H.S. (2019). Monsoon season quantitative assessment of biomass burning clear-sky aerosol radiative effect at surface by ground-based lidar observations in Pulau Pinang, Malaysia in 2014. Remote Sens., 11.","DOI":"10.3390\/rs11222660"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7025","DOI":"10.5194\/acp-17-7025-2017","article-title":"Fu\u2013Liou\u2013Gu and Corti\u2013Peter model performance evaluation for radiative retrievals from cirrus clouds","volume":"17","author":"Lolli","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1080\/10473289.2006.10464485","article-title":"Health effects of fine particulate air pollution: Lines that connect","volume":"56","author":"Pope","year":"2006","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1093\/nsr\/nwx117","article-title":"Aerosol and boundary-layer interactions and impact on air quality","volume":"4","author":"Li","year":"2017","journal-title":"Natl. Sci. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4894","DOI":"10.1002\/2015JD024601","article-title":"Assessment of aerosol optical property and radiative effect for the layer decoupling cases over the northern South China Sea during the 7-SEAS\/Dongsha Experiment","volume":"121","author":"Pani","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lolli, S. (2021). Is the air too polluted for outdoor activities? Check by using your photovoltaic system as an air-quality monitoring device. Sensors, 21.","DOI":"10.3390\/s21196342"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s40641-018-0089-y","article-title":"The radiative forcing of aerosol\u2013cloud interactions in liquid clouds: Wrestling and embracing uncertainty","volume":"4","author":"Feingold","year":"2018","journal-title":"Curr. Clim. Chang. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.5194\/amt-7-2389-2014","article-title":"EARLINET: Towards an advanced sustainable European aerosol lidar network","volume":"7","author":"Pappalardo","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_13","first-page":"09003","article-title":"Status of the NASA Micro Pulse Lidar Network (MPLNET): Overview of the network and future plans, new version 3 data products, and the polarized MPL","volume":"176","author":"Welton","year":"2018","journal-title":"EDP Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_15","first-page":"381","article-title":"Machine learning algorithms-a review","volume":"9","author":"Mahesh","year":"2020","journal-title":"Int. J. Sci. Res."},{"key":"ref_16","first-page":"33","article-title":"EZ Lidar\u2122: A new compact autonomous eye-safe scanning aerosol Lidar for extinction measurements and PBL height detection. Validation of the performances against other instruments and intercomparison campaigns","volume":"44","author":"Sauvage","year":"2011","journal-title":"Opt. Pura Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1175\/1520-0426(1998)015<1043:TETBRO>2.0.CO;2","article-title":"The extinction-to-backscatter ratio of tropospheric aerosol: A numerical study","volume":"15","author":"Ackermann","year":"1998","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1364\/OL.15.000746","article-title":"Measurement of atmospheric aerosol extinction profiles with a Raman lidar","volume":"15","author":"Ansmann","year":"1990","journal-title":"Opt. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1364\/AO.42.002571","article-title":"Examination of the traditional Raman lidar technique. I. Evaluating the temperature-dependent lidar equations","volume":"42","author":"Whiteman","year":"2003","journal-title":"Appl. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1117\/12.55766","article-title":"University of Wisconsin high spectral resolution lidar","volume":"30","author":"Grund","year":"1991","journal-title":"Opt. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.5194\/amt-6-3349-2013","article-title":"0.355-micrometer direct detection wind lidar under testing during a field campaign in consideration of ESA\u2019s ADM-Aeolus mission","volume":"6","author":"Lolli","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Comer\u00f3n, A., Mu\u00f1oz-Porcar, C., Rocadenbosch, F., Rodr\u00edguez-G\u00f3mez, A., and Sicard, M. (2017). Current research in lidar technology used for the remote sensing of atmospheric aerosols. Sensors, 17.","DOI":"10.3390\/s17061450"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1111\/j.1600-0889.2008.00396.x","article-title":"Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM 2006","volume":"61","author":"Freudenthaler","year":"2009","journal-title":"Tellus B Chem. Phys. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Haarig, M., Ansmann, A., Baars, H., Jimenez, C., Veselovskii, I., Engelmann, R., and Althausen, D. (2018). Depolarization and Lidar Ratios at 355, 532, and 1064 nm and Microphysical Properties of Aged Tropospheric and Stratospheric Canadian Wildfire Smoke, Copernicus GmbH.","DOI":"10.5194\/acp-18-11847-2018"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stull, R.B. (1988). An Introduction to Boundary Layer Meteorology, Springer Science & Business Media.","DOI":"10.1007\/978-94-009-3027-8"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1023\/A:1000258318944","article-title":"Lidar determination of the entrainment zone thickness at the top of the unstable marine atmospheric boundary layer","volume":"83","author":"Flamant","year":"1997","journal-title":"Bound. Layer Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.5194\/acp-21-4249-2021","article-title":"Atmospheric boundary layer height estimation from aerosol lidar: A new approach based on morphological image processing techniques","volume":"21","author":"Vivone","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8316","DOI":"10.1364\/AO.55.008316","article-title":"Approach to simultaneously denoise and invert backscatter and extinction from photon-limited atmospheric lidar observations","volume":"55","author":"Marais","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1175\/JTECH-D-15-0085.1","article-title":"Principal component analysis approach to evaluate instrument performances in developing a cost-effective reliable instrument network for atmospheric measurements","volume":"32","author":"Lolli","year":"2015","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.optcom.2004.01.017","article-title":"Noise reduction in lidar signal based on discrete wavelet transform","volume":"233","author":"Fang","year":"2004","journal-title":"Opt. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1063\/1.4822961","article-title":"Savitzky-Golay smoothing filters","volume":"4","author":"Press","year":"1990","journal-title":"Comput. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/9.855552","article-title":"Gaussian filters for nonlinear filtering problems","volume":"45","author":"Ito","year":"2000","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.optcom.2006.05.069","article-title":"Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method","volume":"267","author":"Wu","year":"2006","journal-title":"Opt. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1038\/s41598-020-57897-9","article-title":"Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data","volume":"10","author":"Chattopadhyay","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485128","article-title":"Tackling climate change with machine learning","volume":"55","author":"Rolnick","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, W., and Yan, B. (2020). Tropical cyclone intensity change prediction based on surrounding environmental conditions with deep learning. Water, 12.","DOI":"10.3390\/w12102685"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bochenek, B., and Ustrnul, Z. (2022). Machine learning in weather prediction and climate analyses\u2014Applications and perspectives. Atmosphere, 13.","DOI":"10.3390\/atmos13020180"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, R., Zhang, W., and Wang, X. (2020). Machine learning in tropical cyclone forecast modeling: A review. Atmosphere, 11.","DOI":"10.3390\/atmos11070676"},{"key":"ref_40","unstructured":"Coates, A., Ng, A., and Lee, H. (2011, January 11\u201313). An analysis of single-layer networks in unsupervised feature learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kumar, G., and Bhatia, P.K. (2014, January 27\u201329). A detailed review of feature extraction in image processing systems. Proceedings of the 2014 Fourth International Conference on Advanced Computing & Communication Technologies, Kochi, India.","DOI":"10.1109\/ACCT.2014.74"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104743","DOI":"10.1016\/j.engappai.2022.104743","article-title":"A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects","volume":"110","author":"Ezugwu","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.bspc.2017.07.010","article-title":"Techniques and algorithms for computer aided diagnosis of pigmented skin lesions\u2014A review","volume":"39","author":"Pathan","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.apr.2019.09.009","article-title":"Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980\u20132019)","volume":"11","author":"Govender","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"118163","DOI":"10.1016\/j.atmosenv.2020.118163","article-title":"Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements","volume":"247","author":"Rogozovsky","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.inffus.2015.06.005","article-title":"Decision forest: Twenty years of research","volume":"27","author":"Rokach","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.atmosenv.2019.05.070","article-title":"Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide","volume":"213","author":"Dhammapala","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_48","first-page":"309","article-title":"Application of ensemble learning techniques to model the atmospheric concentration of SO2","volume":"5","author":"Masih","year":"2019","journal-title":"Glob. J. Environ. Sci. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1111\/ejss.13123","article-title":"Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms","volume":"72","author":"Oliveira","year":"2021","journal-title":"Eur. J. Soil Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1007\/s00521-021-05757-6","article-title":"Flood disaster risk assessment based on random forest algorithm","volume":"34","author":"Zhu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","article-title":"Adaptive random forests for evolving data stream classification","volume":"106","author":"Gomes","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.eswa.2019.03.011","article-title":"Constraint learning based gradient boosting trees","volume":"128","author":"Israeli","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8063","DOI":"10.5194\/acp-20-8063-2020","article-title":"Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees","volume":"20","author":"Ivatt","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"18935","DOI":"10.1038\/s41598-021-96872-w","article-title":"Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia","volume":"11","author":"Hanoon","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1116817","DOI":"10.3389\/frsen.2023.1116817","article-title":"Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar","volume":"4","author":"McGill","year":"2023","journal-title":"Front. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3725","DOI":"10.1364\/AO.41.003725","article-title":"Cloud physics lidar: Instrument description and initial measurement results","volume":"41","author":"McGill","year":"2002","journal-title":"Appl. Opt."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"McGill, M.J., Yorks, J.E., Scott, V.S., Kupchock, A.W., and Selmer, P.A. (2015, January 12\u201313). The cloud-aerosol transport system (CATS): A technology demonstration on the international space station. Proceedings of the Lidar Remote Sensing for Environmental Monitoring XV. SPIE, San Diego, CA, USA.","DOI":"10.1117\/12.2190841"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yorks, J.E., Selmer, P.A., Kupchock, A., Nowottnick, E.P., Christian, K.E., Rusinek, D., Dacic, N., and McGill, M.J. (2021). Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data. Atmosphere, 12.","DOI":"10.3390\/atmos12050606"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zeng, S., Omar, A., Vaughan, M., Ortiz, M., Trepte, C., Tackett, J., Yagle, J., Lucker, P., Hu, Y., and Winker, D. (2020). Identifying aerosol subtypes from CALIPSO LiDAR profiles using deep machine learning. Atmosphere, 12.","DOI":"10.3390\/atmos12010010"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1175\/2009JTECHA1281.1","article-title":"Overview of the CALIPSO mission and CALIOP data processing algorithms","volume":"26","author":"Winker","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1175\/2009JTECHA1223.1","article-title":"CALIPSO lidar description and performance assessment","volume":"26","author":"Hunt","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"14511","DOI":"10.5194\/acp-18-14511-2018","article-title":"A neural network aerosol-typing algorithm based on lidar data","volume":"18","author":"Nicolae","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yang, S., Peng, F., von L\u00f6wis, S., Petersen, G.N., and Finger, D.C. (2021). Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland. Remote Sens., 13.","DOI":"10.3390\/rs13132433"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4335","DOI":"10.5194\/amt-14-4335-2021","article-title":"Deriving boundary layer height from aerosol lidar using machine learning: KABL and ADABL algorithms","volume":"14","author":"Rieutord","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10546-011-9643-z","article-title":"Evaluation of mixing-height retrievals from automatic profiling lidars and ceilometers in view of future integrated networks in Europe","volume":"143","author":"Haeffelin","year":"2012","journal-title":"Bound. Layer Meteorol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Sleeman, J., Halem, M., Yang, Z., Caicedo, V., Demoz, B., and Delgado, R. (October, January 26). A deep machine learning approach for lidar based boundary layer height detection. Proceedings of the IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, Online.","DOI":"10.1109\/IGARSS39084.2020.9324191"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"716951","DOI":"10.3389\/frsen.2021.716951","article-title":"Planetary boundary layer height estimates from ICESat-2 and CATS backscatter measurements","volume":"2","author":"Palm","year":"2021","journal-title":"Front. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"614029","DOI":"10.3389\/frsen.2020.614029","article-title":"Retrieving aerosol optical depth and high spatial resolution ocean surface wind speed from CALIPSO: A neural network approach","volume":"1","author":"Murphy","year":"2021","journal-title":"Front. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:45:07Z","timestamp":1760129107000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":68,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174318"],"URL":"https:\/\/doi.org\/10.3390\/rs15174318","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]}}}