{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:58:00Z","timestamp":1773388680348,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202306070036"],"award-info":[{"award-number":["202306070036"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Characterizing the spatial distribution of particles smaller than 10 \u03bcm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m \u00d7 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.<\/jats:p>","DOI":"10.3390\/rs16132498","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T11:30:02Z","timestamp":1720438202000},"page":"2498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8056-7378","authenticated-orcid":false,"given":"Yue","family":"Yang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4240-595X","authenticated-orcid":false,"given":"Jan","family":"Cermak","sequence":"additional","affiliation":[{"name":"Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"},{"name":"Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7821-2308","authenticated-orcid":false,"given":"Yunping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Hou","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1016\/S0140-6736(12)61766-8","article-title":"A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990\u20132010: A systematic analysis for the Global Burden of Disease Study 2010","volume":"380","author":"Lim","year":"2012","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1001\/jama.287.9.1132","article-title":"Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution","volume":"287","author":"Pope","year":"2002","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1093\/eurheartj\/ehz135","article-title":"Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions","volume":"40","author":"Lelieveld","year":"2019","journal-title":"Eur. Heart J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/nature15371","article-title":"The contribution of outdoor air pollution sources to premature mortality on a global scale","volume":"525","author":"Lelieveld","year":"2015","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Stirnberg, R., Cermak, J., and Andersen, H. (2018). An analysis of factors influencing the relationship between satellite-derived AOD and ground-level PM10. Remote Sens., 10.","DOI":"10.3390\/rs10091353"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2019JD031380","DOI":"10.1029\/2019JD031380","article-title":"Mapping and understanding patterns of air quality using satellite data and machine learning","volume":"125","author":"Stirnberg","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1175\/BAMS-86-9-1249","article-title":"Improving National Air Quality Forecasts with Satellite Aerosol Observations","volume":"86","author":"Szykman","year":"2005","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cermak, J., Yang, K., Pauli, E., and Chen, Y. (2022). Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing. Remote Sens., 14.","DOI":"10.3390\/rs14184677"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.5194\/acp-11-7991-2011","article-title":"A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations","volume":"11","author":"Lee","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_10","first-page":"D14205","article-title":"Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach","volume":"114","author":"Gupta","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2015.07.020","article-title":"Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count","volume":"168","author":"You","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"117451","DOI":"10.1016\/j.atmosenv.2020.117451","article-title":"Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach","volume":"230","author":"Zheng","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2012.11.003","article-title":"Estimating ground-level PM2. 5 concentrations in the southeastern US using geographically weighted regression","volume":"121","author":"Hu","year":"2013","journal-title":"Environ. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5304","DOI":"10.1016\/j.atmosenv.2006.04.044","article-title":"Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe","volume":"40","author":"Koelemeijer","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"154279","DOI":"10.1016\/j.scitotenv.2022.154279","article-title":"Optimized neural network for daily-scale ozone prediction based on transfer learning","volume":"827","author":"Ma","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Taheri Shahraiyni, H., and Sodoudi, S.J.A. (2016). Statistical modeling approaches for PM10 prediction in urban areas; A review of 21st-century studies. Atmosphere, 7.","DOI":"10.3390\/atmos7020015"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"L10806","DOI":"10.1029\/2009GL038572","article-title":"Beijing Olympics as an aerosol field experiment","volume":"36","author":"Cermak","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.atmosenv.2012.06.024","article-title":"Combined model for PM10 forecasting in a large city","volume":"60","author":"Perez","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jhazmat.2017.07.050","article-title":"Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)","volume":"341","author":"Park","year":"2018","journal-title":"J. Hazard. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13338","DOI":"10.1002\/2017JD026922","article-title":"A simple and universal aerosol retrieval algorithm for Landsat series images over complex surfaces","volume":"122","author":"Wei","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, L., Wei, J., Bilal, M., Tian, X., Jia, C., Guo, Y., and Mi, X. (2016). Aerosol optical depth retrieval over bright areas using Landsat 8 OLI images. Remote Sens., 8.","DOI":"10.3390\/rs8010023"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105829","DOI":"10.1016\/j.atmosres.2021.105829","article-title":"High-resolution aerosol retrieval over urban areas using sentinel-2 data","volume":"264","author":"Yang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s12650-020-00704-4","article-title":"Visual analysis of meteorological satellite data via model-agnostic meta-learning","volume":"24","author":"Cheng","year":"2021","journal-title":"J. Vis."},{"key":"ref_24","first-page":"1","article-title":"Generalizing from a few examples: A survey on few-shot learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tseng, G., Kerner, H., Nakalembe, C., and Becker-Reshef, I. (2021, January 19\u201325). Learning to predict crop type from heterogeneous sparse labels using meta-learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00122"},{"key":"ref_26","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., Wang, S., Korner, M., and Lobell, D. (2020, January 14\u201319). Meta-learning for few-shot land cover classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00108"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.scitotenv.2013.05.062","article-title":"Short-term effects of PM2.5, PM10 and PM2.5\u201310 on daily mortality in the Netherlands","volume":"463\u2013464","author":"Janssen","year":"2013","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tao, Z., Kokas, A., Zhang, R., Cohan, D.S., and Wallach, D. (2016). Inferring atmospheric particulate matter concentrations from Chinese social media data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0161389"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, A., Qi, Q., Jiang, L., Zhou, F., and Wang, J. (2013). Population exposure to PM2.5 in the urban area of Beijing. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0063486"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4344","DOI":"10.1016\/j.atmosenv.2011.05.051","article-title":"Mapping particulate matter in alpine regions with satellite and ground-based measurements: An exploratory study for data assimilation","volume":"45","author":"Emili","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_32","first-page":"1","article-title":"Aerosol Retrieval Algorithm for Sentinel-2 Images Over Complex Urban Areas","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.5194\/amt-6-1747-2013","article-title":"MODIS 3 km aerosol product: Applications over land in an urban\/suburban region","volume":"6","author":"Munchak","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.atmosenv.2018.12.004","article-title":"MODIS Collection 6.1 aerosol optical depth products over land and ocean: Validation and comparison","volume":"201","author":"Wei","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.scitotenv.2018.11.086","article-title":"A novel spatiotemporal convolutional long short-term neural network for air pollution prediction","volume":"654","author":"Wen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_37","unstructured":"Mu\u00f1oz Sabater, J. (2024, July 04). ERA5-Land hourly data from 1950 to 1980. Volume 10. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/10.24381\/cds.e2161bac?tab=overview."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"150","DOI":"10.3390\/atmos6010150","article-title":"Variations in PM10, PM2. 5 and PM1. 0 in an urban area of the Sichuan Basin and their relation to meteorological factors","volume":"6","author":"Li","year":"2015","journal-title":"Atmosphere"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Andersen, H., Cermak, J., Stirnberg, R., Fuchs, J., Kim, M., and Pauli, E. (2021). Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning. Tellus B Chem. Phys. Meteorol., 73.","DOI":"10.1080\/16000889.2021.1971925"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1175\/BAMS-D-16-0301.1","article-title":"PM2.5 pollution in China and how it has been exacerbated by terrain and meteorological conditions","volume":"99","author":"Wang","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6733","DOI":"10.5194\/acp-18-6733-2018","article-title":"Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China","volume":"18","author":"Leung","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6223","DOI":"10.5194\/acp-18-6223-2018","article-title":"Random forest meteorological normalisation models for Swiss PM 10 trend analysis","volume":"18","author":"Grange","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9627776","DOI":"10.1155\/2021\/9627776","article-title":"Air Quality Prediction Model Based on Spatiotemporal Data Analysis and Metalearning","volume":"2021","author":"Zhang","year":"2021","journal-title":"Wirel. Commun. Mobile Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105622","DOI":"10.1016\/j.knosys.2020.105622","article-title":"Predicting concentration levels of air pollutants by transfer learning and recurrent neural network","volume":"192","author":"Fong","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"116885","DOI":"10.1016\/j.atmosenv.2019.116885","article-title":"Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques","volume":"214","author":"Ma","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"14008","DOI":"10.1007\/s11356-016-6565-9","article-title":"Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers","volume":"23","author":"Chellali","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.atmosenv.2005.10.036","article-title":"Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece","volume":"40","author":"Grivas","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s11270-007-9341-0","article-title":"Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean city","volume":"182","author":"Papanastasiou","year":"2007","journal-title":"Water Air Soil Pollut."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/S1352-2310(99)00316-7","article-title":"Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile","volume":"34","author":"Trier","year":"2000","journal-title":"Atmos. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_52","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5161","DOI":"10.5194\/amt-12-5161-2019","article-title":"Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi","volume":"12","author":"Zheng","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_54","first-page":"251","article-title":"Estimation of PM10 concentration from Landsat 8 OLI satellite imagery over Delhi, India","volume":"8","author":"Saraswat","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.5194\/acp-17-3097-2017","article-title":"Classification of summertime synoptic patterns in Beijing and their associations with boundary layer structure affecting aerosol pollution","volume":"17","author":"Miao","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.5194\/acp-19-1097-2019","article-title":"Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea","volume":"19","author":"Park","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111888","DOI":"10.1016\/j.jenvman.2020.111888","article-title":"Particulate matter (PM2.5 and PM10) generation map using MODIS Level-1 satellite images and deep neural network","volume":"281","author":"Imani","year":"2021","journal-title":"J. Environ. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2498\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:11:44Z","timestamp":1760109104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2498"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,8]]},"references-count":57,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132498"],"URL":"https:\/\/doi.org\/10.3390\/rs16132498","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,8]]}}}