{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:27:13Z","timestamp":1783124833025,"version":"3.54.6"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFA0606800"],"award-info":[{"award-number":["2019YFA0606800"]}]},{"name":"National Key Research and Development Program of China","award":["41775021"],"award-info":[{"award-number":["41775021"]}]},{"name":"National Key Research and Development Program of China","award":["lzujbky-2023-ey10"],"award-info":[{"award-number":["lzujbky-2023-ey10"]}]},{"name":"National Key Research and Development Program of China","award":["lzujbky-2022-ct06"],"award-info":[{"award-number":["lzujbky-2022-ct06"]}]},{"name":"National Natural Science Foundation of China","award":["2019YFA0606800"],"award-info":[{"award-number":["2019YFA0606800"]}]},{"name":"National Natural Science Foundation of China","award":["41775021"],"award-info":[{"award-number":["41775021"]}]},{"name":"National Natural Science Foundation of China","award":["lzujbky-2023-ey10"],"award-info":[{"award-number":["lzujbky-2023-ey10"]}]},{"name":"National Natural Science Foundation of China","award":["lzujbky-2022-ct06"],"award-info":[{"award-number":["lzujbky-2022-ct06"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2019YFA0606800"],"award-info":[{"award-number":["2019YFA0606800"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["41775021"],"award-info":[{"award-number":["41775021"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["lzujbky-2023-ey10"],"award-info":[{"award-number":["lzujbky-2023-ey10"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["lzujbky-2022-ct06"],"award-info":[{"award-number":["lzujbky-2022-ct06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise cloud and aerosol identification hold paramount importance for a thorough comprehension of atmospheric processes, enhancement of meteorological forecasts, and mitigation of climate change. This study devised an automatic denoising cloud\u2013aerosol classification deep learning algorithm, successfully achieving cloud\u2013aerosol identification in atmospheric vertical profiles utilizing CALIPSO L1 data. The algorithm primarily consists of two components: denoising and classification. The denoising task integrates an automatic denoising module that comprehensively assesses various methods, such as Gaussian filtering and bilateral filtering, automatically selecting the optimal denoising approach. The results indicated that bilateral filtering is more suitable for CALIPSO L1 data, yielding SNR, RMSE, and SSIM values of 4.229, 0.031, and 0.995, respectively. The classification task involves constructing the U-Net model, incorporating self-attention mechanisms, residual connections, and pyramid-pooling modules to enhance the model\u2019s expressiveness and applicability. In comparison with various machine learning models, the U-Net model exhibited the best performance, with an accuracy of 0.95. Moreover, it demonstrated outstanding generalization capabilities, evaluated using the harmonic mean F1 value, which accounts for both precision and recall. It achieved F1 values of 0.90 and 0.97 for cloud and aerosol samples from the lidar profiles during the spring of 2019. The study endeavored to predict low-quality data in CALIPSO VFM using the U-Net model, revealing significant differences with a consistency of 0.23 for clouds and 0.28 for aerosols. Utilizing U-Net confidence and a 532 nm attenuated backscatter coefficient to validate medium- and low-quality predictions in two cases from 8 February 2019, the U-Net model was found to align more closely with the CALIPSO observational data and exhibited high confidence. Statistical comparisons of the predicted geographical distribution revealed specific patterns and regional characteristics in the distribution of clouds and aerosols, showcasing the U-Net model\u2019s proficiency in identifying aerosols within cloud layers.<\/jats:p>","DOI":"10.3390\/rs16050904","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T10:11:57Z","timestamp":1709547117000},"page":"904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Cloud\u2013Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Xingzhao","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qia","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Song","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiashun","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center of Western Ecological Security, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104893","DOI":"10.1016\/j.atmosres.2020.104893","article-title":"Aerosol classification in Europe, Middle East, North Africa and Arabian Peninsula based on AERONET Version 3","volume":"239","author":"Logothetis","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.atmosenv.2016.06.002","article-title":"An AERONET-based aerosol classification using the Mahalanobis distance","volume":"140","author":"Hamill","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3789","DOI":"10.5194\/amt-12-3789-2019","article-title":"Aerosol-type classification based on AERONET version 3 inversion products","volume":"12","author":"Shin","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.1016\/j.atmosenv.2010.05.035","article-title":"Characteristics of aerosol types from AERONET sunphotometer measurements","volume":"44","author":"Lee","year":"2010","journal-title":"Atmos. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.1002\/2015JD023322","article-title":"An analysis of global aerosol type as retrieved by MISR","volume":"120","author":"Kahn","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"D07206","DOI":"10.1029\/2011JD016839","article-title":"Fog- and cloud-induced aerosol modification observed by the Aerosol Robotic Network (AERONET)","volume":"117","author":"Eck","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yu, Y., Zhang, W., Luo, T., and Wang, X. (2019). Cloud Detection from FY-4A\u2019s Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches. Remote Sens., 11.","DOI":"10.3390\/rs11243035"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"L19803","DOI":"10.1029\/2007GL030135","article-title":"Initial performance assessment of CALIOP","volume":"34","author":"Winker","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, J., Zheng, Y., Shen, X., Xing, L., and Che, H. (2021). Global Aerosol Classification Based on Aerosol Robotic Network (AERONET) and Satellite Observation. Remote Sens., 13.","DOI":"10.3390\/rs13061114"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4241","DOI":"10.5194\/acp-10-4241-2010","article-title":"Detection of dust aerosol by combining CALIPSO active lidar and passive IIR measurements","volume":"10","author":"Chen","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"703","DOI":"10.5194\/amt-12-703-2019","article-title":"Discriminating between clouds and aerosols in the CALIOP version 4","volume":"12","author":"Liu","year":"2019","journal-title":"1 data products. Atmos. Meas. Tech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4539","DOI":"10.5194\/amt-13-4539-2020","article-title":"CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles","volume":"13","author":"Avery","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6107","DOI":"10.5194\/amt-11-6107-2018","article-title":"The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm","volume":"11","author":"Kim","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_14","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. (2021). Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning. Atmosphere, 12.","DOI":"10.3390\/atmos12010010"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.atmosres.2012.03.013","article-title":"Effect of CALIPSO cloud\u2013aerosol discrimination (CAD) confidence levels on observations of aerosol properties near clouds","volume":"116","author":"Yang","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_16","first-page":"2485","article-title":"A Survey of Cloud Detection Techniques For Satellite Images","volume":"2","author":"Chandran","year":"2015","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, R., Hu, J., Song, Z., Wang, Y., Zhou, X., Zhao, L., and Chen, B. (2023). The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis. Remote Sens., 15.","DOI":"10.20944\/preprints202308.1565.v1"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2008.09.003","article-title":"Noise reduction of NDVI time series: An empirical comparison of selected techniques","volume":"113","author":"Hird","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2679","DOI":"10.1007\/s12665-013-2325-z","article-title":"A review of applying second-generation wavelets for noise removal from remote sensing data","volume":"70","author":"Ebadi","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1016\/j.ijleo.2015.04.029","article-title":"Remote sensing image noise reduction using wavelet coefficients based on OMP","volume":"126","author":"Wu","year":"2015","journal-title":"Optik"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Satya, P.M., Jagadish, S., Satyanarayana, V., and Singh, M.K. (2021, January 7\u20139). Stripe Noise Removal from Remote Sensing Images. Proceedings of the 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India.","DOI":"10.1109\/ISPCC53510.2021.9609457"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1175\/2009JTECHA1229.1","article-title":"The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance","volume":"26","author":"Liu","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s13351-014-4032-4","article-title":"An Overview of Passive and Active Dust Detection Methods Using Satellite Measurements","volume":"28","author":"Chen","year":"2014","journal-title":"J. Meteorol. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.asr.2018.07.004","article-title":"Evaluation and utilization of CloudSat and CALIPSO data to analyze the impact of dust aerosol on the microphysical properties of cirrus over the Tibetan Plateau","volume":"63","author":"Pan","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Samagaio, G., de Moura, J., Novo, J., and Ortega, M. (2017, January 11\u201315). Optical coherence tomography denoising by means of a fourier butterworth Filter-Based approach. Proceedings of the Image Analysis and Processing-ICIAP 2017: 19th International Conference, Catania, Italy. Part II 19.","DOI":"10.1007\/978-3-319-68548-9_39"},{"key":"ref_27","first-page":"1359","article-title":"Noise removal in compound image using median filter","volume":"2","author":"Maheswari","year":"2010","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"V79","DOI":"10.1190\/geo2012-0232.1","article-title":"Noise reduction by vector median filtering","volume":"78","author":"Liu","year":"2013","journal-title":"Geophysics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TIP.2008.2006658","article-title":"Multiresolution bilateral filtering for image denoising","volume":"17","author":"Zhang","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","first-page":"1093","article-title":"Noise Reduction Techniques using Bilateral Based Filter","volume":"4","author":"Sonia","year":"2017","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_31","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015., Springer International Publishing."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zunair, H., and Ben Hamza, A. (2021). Sharp U-Net: Depthwise convolutional network for biomedical image segmentation. Comput. Biol. Med., 136.","DOI":"10.1016\/j.compbiomed.2021.104699"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"155194","DOI":"10.1109\/ACCESS.2019.2948476","article-title":"Skip Connection U-Net for White Matter Hyperintensities Segmentation From MRI","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7283","DOI":"10.1007\/s00521-021-06876-w","article-title":"U-Net skip-connection architectures for the automated counting of microplastics","volume":"34","author":"Lee","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"113452","DOI":"10.1016\/j.rse.2023.113452","article-title":"Semantic segmentation of water bodies in very high-resolution satellite and aerial images","volume":"287","author":"Wieland","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"113800","DOI":"10.1016\/j.rse.2023.113800","article-title":"A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level","volume":"298","author":"Lobert","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, Y.Z., Wang, Y., Huang, Y.H., Xiang, P., Liu, W.X., Lai, Q.Q., Gao, Y.Y., Xu, M.S., and Guo, Y.F. (2023). RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images. Comput. Methods Programs Biomed., 231.","DOI":"10.1016\/j.cmpb.2023.107437"},{"key":"ref_38","first-page":"103453","article-title":"FCD-AttResU-Net: An improved forest change detection in Sentinel-2 satellite images using attention residual U-Net","volume":"122","author":"Kalinaki","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109225","DOI":"10.1016\/j.sigpro.2023.109225","article-title":"Single UHD image dehazing via Interpretable Pyramid Network","volume":"214","author":"Xiao","year":"2024","journal-title":"Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/TGRS.2005.855071","article-title":"A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data","volume":"43","author":"Blackwell","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111446","DOI":"10.1016\/j.rse.2019.111446","article-title":"Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN)","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"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":"112652","DOI":"10.1016\/j.rse.2021.112652","article-title":"A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms","volume":"265","author":"Fayad","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112468","DOI":"10.1016\/j.rse.2021.112468","article-title":"CNN-based burned area mapping using radar and optical data","volume":"260","author":"Tanase","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_45","first-page":"486","article-title":"Long short-term memory\u2014Fully connected (LSTM-FC) neural network for PM2","volume":"220","author":"Zhao","year":"2019","journal-title":"5 concentration prediction. Chemosphere"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"128585","DOI":"10.1016\/j.physa.2023.128585","article-title":"Structure and performance of fully connected neural networks: Emerging complex network properties","volume":"615","author":"Scabini","year":"2023","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_47","first-page":"1","article-title":"Xgboost: Extreme gradient boosting","volume":"1","author":"Chen","year":"2015","journal-title":"R Package Version"},{"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, San Francisco, CA USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112316","DOI":"10.1016\/j.rse.2021.112316","article-title":"Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"112971","DOI":"10.1016\/j.rse.2022.112971","article-title":"Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data","volume":"273","author":"Yang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"113483","DOI":"10.1016\/j.rse.2023.113483","article-title":"Continuous mapping of aboveground biomass using Landsat time series","volume":"288","author":"Baccini","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"120021","DOI":"10.1016\/j.atmosenv.2023.120021","article-title":"Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots in China","volume":"312","author":"Chen","year":"2023","journal-title":"Atmos. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/904\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:08:54Z","timestamp":1760105334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":52,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050904"],"URL":"https:\/\/doi.org\/10.3390\/rs16050904","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,4]]}}}