{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:50:28Z","timestamp":1782931828667,"version":"3.54.5"},"reference-count":60,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS202114"],"award-info":[{"award-number":["OFSLRSS202114"]}]},{"name":"Jackson School of Geosciences, University of Texas at Austin","award":["OFSLRSS202114"],"award-info":[{"award-number":["OFSLRSS202114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data and then generated air temperature data with 3H characteristics: High resolution, High spatio-temporal continuity (spatially seamless and temporally continuous), and High accuracy simultaneously. The DBN model was developed and applied for two different urban regions: Wuhan Metropolitan Area (WMA) in China, and Austin, Texas, USA. The model has a excellent ability to fit the complex nonlinear relationship between temperature and different predictive variables. After various adjustments to the model structure and different combinations of input variables, the daily 500-m air temperature in Wuhan Metropolitan Area (WMA) was initially generated by fusing remote sensing, reanalysis and in situ measurement products. The ten-fold cross-validation results indicated that the DBN model achieved promising results with the RMSE of 1.086 \u00b0C, MAE of 0.839 \u00b0C, and R2 of 0.986. Compared with conventional data fusion algorithms, the DBN model also exhibited better performance. In addition, the detailed evaluation of the model on spatial and temporal scales proved the advantages of using DBN model to generate 3H temperature data. The spatial transferability of the model was tested by conducting a validation experiment for Austin, USA. In general, the results and fine-scale analyses show that the employed framework is effective to generate 3H temperature, which is valuable for urban climate and urban heat island research.<\/jats:p>","DOI":"10.3390\/rs14153536","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T01:42:13Z","timestamp":1658713333000},"page":"3536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1017-742X","authenticated-orcid":false,"given":"Xiang","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9051-9989","authenticated-orcid":false,"given":"Tailai","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2366-2491","authenticated-orcid":false,"given":"Aminjon","family":"Gulakhmadov","sequence":"additional","affiliation":[{"name":"Research Center of Ecology and Environment in Central Asia, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Institute of Water Problems, Hydropower and Ecology of the National Academy of Sciences of Tajikistan, Dushanbe 734042, Tajikistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Song","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xihui","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China"},{"name":"Centre for Severe Weather and Climate and Hydro-Geological Hazards, Wuhan 430074, China"},{"name":"School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5039-6774","authenticated-orcid":false,"given":"Jiangyuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2815-3266","authenticated-orcid":false,"given":"Shuzhe","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9671-6569","authenticated-orcid":false,"given":"Won-Ho","family":"Nam","sequence":"additional","affiliation":[{"name":"School of Social Safety and Systems Engineering, Institute of Agricultural Environmental Science, National Agricultural Water Research Center, Hankyong National University, 327 Jungang-ro, Anseong-si 17579, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3521-9972","authenticated-orcid":false,"given":"Nengcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Luojia Laboratory, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1848-5080","authenticated-orcid":false,"given":"Dev","family":"Niyogi","sequence":"additional","affiliation":[{"name":"Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78712, USA"},{"name":"Department of Civil, Architecture, and Environmental 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