{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:08:41Z","timestamp":1766268521384,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Startup Foundation for Introducing Talent of NUIST","award":["2022r040"],"award-info":[{"award-number":["2022r040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth\u2019s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China\u2019s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65\u20132.08 K for sample-based cross-validation, and 2.22\u20132.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets.<\/jats:p>","DOI":"10.3390\/rs15071753","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T06:34:07Z","timestamp":1679639647000},"page":"1753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Large-Scale Estimation of Hourly Surface Air Temperature Based on Observations from the FY-4A Geostationary Satellite"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3200-6525","authenticated-orcid":false,"given":"Zhenwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing 210044, China"},{"name":"Jiangsu Province Engineering Research Center of Collaborative Navigation\/Positioning and Smart Application, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yanzhi","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Guangxia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4400-0411","authenticated-orcid":false,"given":"Chen","family":"Liang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing Institute of Surveying and Mapping, Beijing 100038, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14288","DOI":"10.1073\/pnas.0606291103","article-title":"Global Temperature Change","volume":"103","author":"Hansen","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1175\/JTECH-D-11-00103.1","article-title":"An Overview of the Global Historical Climatology Network-Daily Database","volume":"29","author":"Menne","year":"2012","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9835","DOI":"10.1175\/JCLI-D-18-0094.1","article-title":"The Global Historical Climatology Network Monthly Temperature Dataset, Version 4","volume":"31","author":"Menne","year":"2018","journal-title":"J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.rse.2012.08.025","article-title":"Satellite Air Temperature Estimation for Monitoring the Canopy Layer Heat Island of Milan","volume":"127","author":"Pichierri","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.uclim.2014.10.008","article-title":"Heat Mortality in Berlin\u2014Spatial Variability at the Neighborhood Scale","volume":"10","author":"Schuster","year":"2014","journal-title":"Urban Clim."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2014.06.001","article-title":"MODIS Land Surface Temperature as an Index of Surface Air Temperature for Operational Snowpack Estimation","volume":"152","author":"Shamir","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/nclimate2237","article-title":"Consistent Increase in High Asia\u2019s Runoff Due to Increasing Glacier Melt and Precipitation","volume":"4","author":"Lutz","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1002\/(SICI)1097-0088(19971130)17:14<1559::AID-JOC211>3.0.CO;2-5","article-title":"Mapping Regional Air Temperature Fields Using Satellite-Derived Surface Skin Temperatures","volume":"17","author":"Vogt","year":"1997","journal-title":"Int. J. Climatol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.rse.2009.10.002","article-title":"Evaluation of MODIS Land Surface Temperature Data to Estimate Air Temperature in Different Ecosystems over Africa","volume":"114","author":"Vancutsem","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111791","DOI":"10.1016\/j.rse.2020.111791","article-title":"Hyperlocal Mapping of Urban Air Temperature Using Remote Sensing and Crowdsourced Weather Data","volume":"242","author":"Venter","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Du, Q. (2019). A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data. Remote Sens., 11.","DOI":"10.3390\/rs11070767"},{"key":"ref_12","first-page":"261","article-title":"Hourly Gridded Air Temperatures of South Africa Derived from MSG SEVIRI","volume":"78","author":"Meyer","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2012.10.034","article-title":"Estimation of Daily Maximum and Minimum Air Temperature Using MODIS Land Surface Temperature Products","volume":"130","author":"Zhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0034-4257(96)00216-7","article-title":"Estimation of Air Temperature from Remotely Sensed Surface Observations","volume":"60","author":"Prihodko","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1111\/0033-0124.00230","article-title":"Thermal Remote Sensing of Near Surface Environmental Variables: Application Over the Oklahoma Mesonet","volume":"52","author":"Czajkowski","year":"2000","journal-title":"Prof. Geogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.isprsjprs.2009.02.006","article-title":"Parameterization of Air Temperature in High Temporal and Spatial Resolution from a Combination of the SEVIRI and MODIS Instruments","volume":"64","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s00704-004-0079-y","article-title":"Air Temperature Retrieval from Remote Sensing Data Based on Thermodynamics","volume":"80","author":"Sun","year":"2005","journal-title":"Theor. Appl. Climatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1080\/01431160310001624593","article-title":"Integrating AVHRR Satellite Data and NOAA Ground Observations to Predict Surface Air Temperature: A Statistical Approach","volume":"25","author":"Florio","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.rse.2014.04.024","article-title":"Predicting Spatiotemporal Mean Air Temperature Using MODIS Satellite Surface Temperature Measurements across the Northeastern USA","volume":"150","author":"Kloog","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s00704-011-0464-2","article-title":"Spatio-Temporal Prediction of Daily Temperatures Using Time-Series of MODIS LST Images","volume":"107","author":"Hengl","year":"2012","journal-title":"Theor. Appl. Climatol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1002\/2013JD020803","article-title":"Spatio-temporal Interpolation of Daily Temperatures for Global Land Areas at 1 Km Resolution","volume":"119","author":"Kilibarda","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Noi, P.T., Degener, J., and Kappas, M. (2017). Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. Remote Sens., 9.","DOI":"10.3390\/rs9050398"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111462","DOI":"10.1016\/j.rse.2019.111462","article-title":"Estimating Daily Average Surface Air Temperature Using Satellite Land Surface Temperature and Top-of-Atmosphere Radiation Products over the Tibetan Plateau","volume":"234","author":"Rao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2018.01.018","article-title":"Estimation of Daily Maximum and Minimum Air Temperatures in Urban Landscapes Using MODIS Time Series Satellite Data","volume":"137","author":"Yoo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111692","DOI":"10.1016\/j.rse.2020.111692","article-title":"Deep Learning-Based Air Temperature Mapping by Fusing Remote Sensing, Station, Simulation and Socioeconomic Data","volume":"240","author":"Shen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.isprsjprs.2021.10.022","article-title":"Hourly Mapping of Surface Air Temperature by Blending Geostationary Datasets from the Two-Satellite System of GOES-R Series","volume":"183","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1080\/10106049.2020.1837261","article-title":"Retrieval of Monthly Maximum and Minimum Air Temperature Using MODIS Aqua Land Surface Temperature Data over the United Arab Emirates","volume":"37","author":"Alqasemi","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"152538","DOI":"10.1016\/j.scitotenv.2021.152538","article-title":"Merging Framework for Estimating Daily Surface Air Temperature by Integrating Observations from Multiple Polar-Orbiting Satellites","volume":"812","author":"Zhang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2007.02.025","article-title":"Estimation of Diurnal Air Temperature Using MSG SEVIRI Data in West Africa","volume":"110","author":"Stisen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2010.08.010","article-title":"Air Temperature Estimation with MSG-SEVIRI Data: Calibration and Validation of the TVX Algorithm for the Iberian Peninsula","volume":"115","author":"Nieto","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1109\/JSTARS.2014.2320762","article-title":"Toward a Near Real-Time Product of Air Temperature Maps from Satellite Data and In Situ Measurements in Arid Environments","volume":"7","author":"Lazzarini","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, B., Erell, E., Hough, I., Shtein, A., Just, A.C., Novack, V., Rosenblatt, J., and Kloog, I. (2020). Estimation of Hourly near Surface Air Temperature Across Israel Using an Ensemble Model. Remote Sens., 12.","DOI":"10.3390\/rs12111741"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4","volume":"98","author":"Yang","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1175\/2011BAMS3015.1","article-title":"The Integrated Surface Database: Recent Developments and Partnerships","volume":"92","author":"Smith","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1007\/s00376-021-0425-3","article-title":"Fengyun Meteorological Satellite Products for Earth System Science Applications","volume":"38","author":"Xian","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s13351-017-6161-z","article-title":"Developing the Science Product Algorithm Testbed for Chinese Next-Generation Geostationary Meteorological Satellites: Fengyun-4 Series","volume":"31","author":"Min","year":"2017","journal-title":"J. Meteorol. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/TGRS.2008.2006180","article-title":"Developing Algorithm for Operational GOES-R Land Surface Temperature Product","volume":"47","author":"Yu","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.isprsjprs.2021.03.013","article-title":"Validation and Consistency Assessment of Land Surface Temperature from Geostationary and Polar Orbit Platforms: SEVIRI\/MSG and AVHRR\/Metop","volume":"175","author":"Trigo","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fan, J., Han, Q., Wang, S., Liu, H., Chen, L., Tan, S., Song, H., and Li, W. (2022). Evaluation of Fengyun-4A Detection Accuracy: A Case Study of the Land Surface Temperature Product for Hunan Province, Central China. Atmosphere, 13.","DOI":"10.3390\/atmos13121953"},{"key":"ref_42","unstructured":"Li, R., Li, H., Bian, Z., Cao, B., Du, Y., Sun, L., and Liu, Q. (August, January 28). High Temporal Resolution Land Surface Temperature Retrieval from Global Geostationary Satellite Data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Meng, Y., Zhou, J., Ma, J., and Long, Z. (2021, January 11\u201316). Investigation and Validation of The Chinese Fengyun-4a Land Surface Temperature Products In The Heihe River Basin. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553394"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-Validation Strategies for Data with Temporal, Spatial, Hierarchical, or Phylogenetic Structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109692","DOI":"10.1016\/j.ecolmodel.2021.109692","article-title":"Spatial Cross-Validation Is Not the Right Way to Evaluate Map Accuracy","volume":"457","author":"Wadoux","year":"2021","journal-title":"Ecol. Modell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108815","DOI":"10.1016\/j.ecolmodel.2019.108815","article-title":"Importance of Spatial Predictor Variable Selection in Machine Learning Applications\u2014Moving from Data Reproduction to Spatial Prediction","volume":"411","author":"Meyer","year":"2019","journal-title":"Ecol. Modell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1002\/joc.7060","article-title":"A Spatiotemporal Reconstruction of Daily Ambient Temperature Using Satellite Data in the Megalopolis of Central Mexico from 2003 to 2019","volume":"41","author":"Arfer","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zeng, L., Hu, Y., Wang, R., Zhang, X., Peng, G., Huang, Z., Zhou, G., Xiang, D., Meng, R., and Wu, W. (2021). 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale. Remote Sens., 13.","DOI":"10.3390\/rs13122355"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bahari, N.I.S., Muharam, F.M., Zulkafli, Z., Mazlan, N., and Husin, N.A. (2021). Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia. Remote Sens., 13.","DOI":"10.3390\/rs13132589"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4241","DOI":"10.5194\/essd-13-4241-2021","article-title":"An All-Sky 1 Km Daily Land Surface Air Temperature Product over Mainland China for 2003\u20132019 from MODIS and Ancillary Data","volume":"13","author":"Chen","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"100739","DOI":"10.1016\/j.uclim.2020.100739","article-title":"Mapping Urban Temperature Using Crowd-Sensing Data and Machine Learning","volume":"35","author":"Zumwald","year":"2021","journal-title":"Urban Clim."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1080\/15481603.2020.1766768","article-title":"Improvement of Spatial Interpolation Accuracy of Daily Maximum Air Temperature in Urban Areas Using a Stacking Ensemble Technique","volume":"57","author":"Cho","year":"2020","journal-title":"GIsci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, M., Wang, B., Cleverly, J., Liu, D.L., Feng, P., Zhang, H., Huete, A., Yang, X., and Yu, Q. (2020). Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau. Remote Sens., 12.","DOI":"10.3390\/rs12111722"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2018.05.034","article-title":"Developing a 1 Km Resolution Daily Air Temperature Dataset for Urban and Surrounding Areas in the Conterminous United States","volume":"215","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_55","first-page":"102295","article-title":"Creating 1-Km Long-Term (1980\u20132014) Daily Average Air Temperatures over the Tibetan Plateau by Integrating Eight Types of Reanalysis and Land Data Assimilation Products Downscaled with MODIS-Estimated Temperature Lapse Rates Based on Machine Learning","volume":"97","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:02:19Z","timestamp":1760122939000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,24]]},"references-count":55,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071753"],"URL":"https:\/\/doi.org\/10.3390\/rs15071753","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,24]]}}}