{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:59:35Z","timestamp":1776783575169,"version":"3.51.2"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Peng Liu","award":["41971397"],"award-info":[{"award-number":["41971397"]}]},{"name":"Guojin He","award":["61731022"],"award-info":[{"award-number":["61731022"]}]},{"name":"Hui Zhang","award":["81871511"],"award-info":[{"award-number":["81871511"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aerosol Optical Depth (AOD) is a crucial parameter for various environmental and climate studies. Merging multi-sensor AOD products is an effective way to produce AOD products with more spatiotemporal integrity and accuracy. This study proposed a conditional generative adversarial network architecture (AeroCGAN) to improve the estimation of AOD. It first adopted MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) AOD data to training the initial model, and then transferred the trained model to Himawari data and obtained the estimation of 1-km-resolution, hourly Himawari AOD products. Specifically, the generator adopted an encoder\u2013decoder network for preliminary resolution enhancement. In addition, a three-dimensional convolutional neural network (3D-CNN) was used for environment features extraction and connected to a residual network for improving accuracy. Meanwhile, the sampled data and environment data were designed as conditions of the generator. The spatial distribution feature comparison and quantitative evaluation over an area of the North China Plain during the year 2017 have shown that this approach can better model the distribution of spatial features of AOD data and improve the accuracy of estimation with the help of local environment patterns.<\/jats:p>","DOI":"10.3390\/rs13193834","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"3834","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Luo","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3292-8551","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-0845","authenticated-orcid":false,"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Jianbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Bingze","family":"Song","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Yuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Guojin","family":"He","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, L. (2021). High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China. Remote Sens., 13.","DOI":"10.3390\/rs13122324"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105343","DOI":"10.1016\/j.atmosres.2020.105343","article-title":"The impact of different aerosol properties and types on direct aerosol radiative forcing and efficiency using AERONET version 3","volume":"250","author":"Logothetis","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12813","DOI":"10.5194\/acp-20-12813-2020","article-title":"Detection and attribution of wildfire pollution in the Arctic and northern midlatitudes using a network of Fourier-transform infrared spectrometers and GEOS-Chem","volume":"20","author":"Lutsch","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118253","DOI":"10.1016\/j.atmosenv.2021.118253","article-title":"Impacts of Urbanization on Atmospheric Circulation and Aerosol Transport in a Coastal Environment Simulated by the WRF-Chem Coupled with Urban Canopy Model","volume":"249","author":"Kim","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.atmosenv.2019.06.004","article-title":"Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation","volume":"213","author":"Tao","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112221","DOI":"10.1016\/j.rse.2020.112221","article-title":"A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification","volume":"253","author":"Su","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1080\/10643389.2019.1665944","article-title":"Satellite remote sensing of aerosol optical depth: Advances, challenges, and perspectives","volume":"50","author":"Wei","year":"2020","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zou, B., Liu, N., Wang, W., Feng, H., and Lin, Y. (2020). An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products. Remote Sens., 12.","DOI":"10.3390\/rs12071102"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.08.017","article-title":"Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation","volume":"157","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zeng, C., Gong, W., Wang, L., Sun, K., Shen, H., Zhu, Z., and Zhu, Z. (2017). Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis. Remote Sens., 9.","DOI":"10.3390\/rs9040340"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Go, S., Kim, J., Sang, S.P., Kim, M., and Im, J. (2020). Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product. Remote Sens., 12.","DOI":"10.3390\/rs12233987"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/LGRS.2016.2520480","article-title":"High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression","volume":"13","author":"Zou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5971","DOI":"10.1080\/2150704X.2014.943321","article-title":"Observation of an agricultural biomass burning in central and east China using merged aerosol optical depth data from multiple satellite missions","volume":"35","author":"Xue","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118146","DOI":"10.1016\/j.atmosenv.2020.118146","article-title":"Probabilistic merging and verification of monthly gridded aerosol products","volume":"247","author":"Yang","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, L., Shi, R., Zhang, L., Zhang, J., and Gao, W. (2014, January 17\u201321). The data fusion of aerosol optical thickness using universal kriging and stepwise regression in East China. Proceedings of the SPIE Optical Engineering and Applications, San Diego, CA, USA.","DOI":"10.1117\/12.2061764"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2013.08.007","article-title":"Geostatistical inverse modeling for super-resolution mapping of continuous spatial processes","volume":"139","author":"Wang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1080\/10106049.2013.827750","article-title":"Statistical data fusion of multi-sensor AOD over the Continental United States","volume":"29","author":"Puttaswamy","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/JSTARS.2019.2891566","article-title":"Remote-Sensing Image Denoising with Multi-Sourced Information","volume":"12","author":"Liu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/JSTARS.2016.2598859","article-title":"Active Deep Learning for Classification of Hyperspectral Images","volume":"10","author":"Liu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.apr.2020.09.003","article-title":"Air quality predictions with a semi-supervised bidirectional LSTM neural network","volume":"12","author":"Zhang","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4034","DOI":"10.1002\/2015JD024571","article-title":"Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method","volume":"121","author":"Tang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.atmosenv.2019.01.045","article-title":"High-resolution daily AOD estimated to full coverage using the random forest model approach in the Beijing-Tianjin-Hebei region","volume":"203","author":"Zhao","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112006","DOI":"10.1016\/j.rse.2020.112006","article-title":"Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method","volume":"249","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"8243","DOI":"10.5194\/acp-19-8243-2019","article-title":"Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China","volume":"19","author":"Liu","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.solener.2019.01.096","article-title":"3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction","volume":"181","author":"Zhao","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.neucom.2014.09.058","article-title":"Hyperspectral anomaly change detection with slow feature analysis","volume":"151","author":"Wu","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e2020GL091469","DOI":"10.1029\/2020GL091469","article-title":"Spatiotemporal heterogeneity of aerosol and cloud properties over the southeast Atlantic: An observational analysis","volume":"48","author":"Chang","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.atmosenv.2018.10.019","article-title":"Impacts of a newly-developed aerosol climatology on numerical weather prediction using a global atmospheric forecasting model","volume":"197","author":"Choi","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_31","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_33","first-page":"1","article-title":"Spatial interpolation using conditional generative adversarial neural networks","volume":"48","author":"Zhu","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e2020GL092032","DOI":"10.1029\/2020GL092032","article-title":"PrecipGAN: Merging Microwave and Infrared Data for Satellite Precipitation Estimation using Generative Adversarial Network","volume":"48","author":"Wang","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"17051","DOI":"10.1029\/96JD03988","article-title":"Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer","volume":"102","author":"Kaufman","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10063","DOI":"10.5194\/acp-16-10063-2016","article-title":"Effects of aerosol-radiation interaction on precipitation during biomass-burning season in East China","volume":"16","author":"Xin","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s11430-018-9260-0","article-title":"Numerical simulation of the influence of aerosol radiation effect on urban boundary layer","volume":"61","author":"Wang","year":"2018","journal-title":"Sci. China Earth Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lai, W., Huang, J., Ahuja, N., and Yang, M. (2017, January 21\u201326). Deep laplacian pyramid networks for fast and accurate super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/acp-18-2949-2018","article-title":"Aerosol optical characteristics and their vertical distributions under enhanced haze pollution events: Effect of the regional transport of different aerosol types over eastern China","volume":"18","author":"Sun","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"128560","DOI":"10.1016\/j.chemosphere.2020.128560","article-title":"Aerosol optical properties and its type classification based on multiyear joint observation campaign in North China Plain megalopolis","volume":"273","author":"Zheng","year":"2021","journal-title":"Chemosphere"},{"key":"ref_41","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv."},{"key":"ref_42","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.neucom.2019.03.106","article-title":"Ultra-Dense GAN for Satellite Imagery Super-Resolution","volume":"398","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_44","first-page":"33","article-title":"Space-Time Ground-Level PM2.5 Distribution at the Yangtze River Delta: A Comparison of Kriging, LUR, and Combined BME-LUR Techniques","volume":"36","author":"Xiao","year":"2020","journal-title":"J. Environ. Inform."},{"key":"ref_45","unstructured":"Chao, D., Chen, C.L., He, K., and Tang, X. (2014). Learning a Deep Convolutional Network for Image Super-Resolution. European Conference on Computer Vision, Springer."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3834\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:04:54Z","timestamp":1760166294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3834"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":45,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193834"],"URL":"https:\/\/doi.org\/10.3390\/rs13193834","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,25]]}}}