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Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05\u00b0 resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region.<\/jats:p>","DOI":"10.3390\/rs16183510","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T09:15:07Z","timestamp":1727082907000},"page":"3510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving the ERA5-Land Temperature Product through a Deep Spatiotemporal Model That Uses Fused Multi-Source Remote Sensing Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1275-5599","authenticated-orcid":false,"given":"Lei","family":"Xu","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatial-Temporal Big Data, Zhengzhou 450046, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinjin","family":"Du","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatial-Temporal Big Data, Zhengzhou 450046, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiwei","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatial-Temporal Big Data, Zhengzhou 450046, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiannan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fen","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatial-Temporal Big Data, Zhengzhou 450046, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weichen","family":"Mu","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatial-Temporal Big Data, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatial-Temporal Big Data, Zhengzhou 450046, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1235367","DOI":"10.1126\/science.1235367","article-title":"Quantifying the influence of climate on human conflict","volume":"341","author":"Hsiang","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1080\/19434472.2022.2061032","article-title":"Climate change in the Horn of Africa: Causations for violent extremism","volume":"16","author":"Regan","year":"2024","journal-title":"Behav. Sci. Terror. Political Aggress."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"117233","DOI":"10.1016\/j.envres.2023.117233","article-title":"Impact of climate change and anthropogenic activities on aquatic ecosystem\u2014A review","volume":"238","author":"Muruganandam","year":"2023","journal-title":"Environ. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104438","DOI":"10.1016\/j.scs.2023.104438","article-title":"Effects of urban lakes and neighbouring green spaces on air temperature and humidity and seasonal variabilities","volume":"91","author":"Zhao","year":"2023","journal-title":"Sustain. Cities Soc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Peng, X., Wu, W., Zheng, Y., Sun, J., Hu, T., and Wang, P. (2020). Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-67423-6"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9185","DOI":"10.1002\/2017JD026880","article-title":"A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series","volume":"122","author":"Good","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2017.01.001","article-title":"Characterizing the relationship between land use land cover change and land surface temperature","volume":"124","author":"Tran","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2385310","DOI":"10.1155\/2019\/2385310","article-title":"Estimation of Summer Air Temperature over China Using Himawari-8 AHI and Numerical Weather Prediction Data","volume":"2019","author":"Liu","year":"2019","journal-title":"Adv. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13351-023-2086-x","article-title":"CRA-40\/atmosphere\u2014The first-generation Chinese atmospheric reanalysis (1979\u20132018): System description and performance evaluation","volume":"37","author":"Liu","year":"2023","journal-title":"J. Meteorol. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., and Woollen, J. (2018). The NCEP\/NCAR 40-year reanalysis project. Renewable Energy, Routledge.","DOI":"10.4324\/9781315793245-16"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.1175\/JCLI-D-16-0758.1","article-title":"The modern-era retrospective analysis for research and applications, version 2 (MERRA-2)","volume":"30","author":"Gelaro","year":"2017","journal-title":"J. Clim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.2151\/jmsj.2015-001","article-title":"The JRA-55 reanalysis: General specifications and basic characteristics","volume":"93","author":"Kobayashi","year":"2015","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_14","unstructured":"Liu, Z., Shi, C., Zhou, Z., Jiang, L., Liang, X., Zhang, T., Liao, J., Liu, J., Wang, M., and Yao, S. (2017, January 13\u201317). CMA global reanalysis (CRA-40): Status and plans. Proceedings of the 5th International Conference on Reanalysis, Rome, Italy."},{"key":"ref_15","first-page":"66","article-title":"Applicability of ERA5 reanalysis of precipitation data in China","volume":"45","author":"Liu","year":"2022","journal-title":"Arid. Land Geogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"112437","DOI":"10.1016\/j.rse.2021.112437","article-title":"A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature","volume":"260","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113083","DOI":"10.1016\/j.rse.2022.113083","article-title":"Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data","volume":"278","author":"Ma","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"165","DOI":"10.5194\/essd-8-165-2016","article-title":"A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies","volume":"8","author":"Banzon","year":"2016","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_20","first-page":"102256","article-title":"Assessing scaling effect in downscaling land surface temperature in a heterogenous urban environment","volume":"96","author":"Pu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1109\/JSTARS.2023.3239109","article-title":"Statistical downscaling of temperature distributions in southwest China by using terrain-guided attention network","volume":"16","author":"Liu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","first-page":"136","article-title":"Downscaling of surface air temperature over the Tibetan Plateau based on DEM","volume":"73","author":"Ding","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100636","DOI":"10.1016\/j.uclim.2020.100636","article-title":"Land use regression modeling of microscale urban air temperatures in greater Vancouver, Canada","volume":"32","author":"Tsin","year":"2020","journal-title":"Urban Clim."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.scitotenv.2017.08.252","article-title":"Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity","volume":"618","author":"Shi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1111\/2041-210X.13099","article-title":"Identifying animal species in camera trap images using deep learning and citizen science","volume":"10","author":"Willi","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.1002\/qj.3410","article-title":"Predicting weather forecast uncertainty with machine learning","volume":"144","author":"Scher","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100312","DOI":"10.1016\/j.cscee.2023.100312","article-title":"Utilizing time series data from 1961 to 2019 recorded around the world and machine learning to create a Global Temperature Change Prediction Model","volume":"7","author":"Malakouti","year":"2023","journal-title":"Case Stud. Chem. Environ. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e2020MS002109","DOI":"10.1029\/2020MS002109","article-title":"Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere","volume":"12","author":"Weyn","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2019.12.030","article-title":"Transductive LSTM for time-series prediction: An application to weather forecasting","volume":"125","author":"Karevan","year":"2020","journal-title":"Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.procs.2018.08.153","article-title":"Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting","volume":"135","author":"Salman","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111358","DOI":"10.1016\/j.rse.2019.111358","article-title":"Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102472","DOI":"10.1016\/j.seares.2024.102472","article-title":"Time series prediction of sea surface temperature based on BiLSTM model with attention mechanism","volume":"198","author":"Zrira","year":"2024","journal-title":"J. Sea Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108327","DOI":"10.1016\/j.buildenv.2021.108327","article-title":"CNN-LSTM architecture for predictive indoor temperature modeling","volume":"206","author":"Elmaz","year":"2021","journal-title":"Build. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105522","DOI":"10.1016\/j.atmosres.2021.105522","article-title":"Evaluation of a climate simulation over the Yellow River Basin based on a regional climate model (REMO) within the CORDEX","volume":"254","author":"Pang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: A state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Dutra","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_37","unstructured":"Store, C.C.D. (2019). Land cover classification gridded maps from 1992 to present derived from satellite observations. Copernic. Clim. Change Serv., 7\u20139."},{"key":"ref_38","unstructured":"Rose, A., McKee, J., Sims, K., Bright, E., Reith, A., and Urban, M. (2020). LandScan Global 2019, LandScan Global, Oak Ridge National Laboratory."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2019, January 9\u201312). The Performance of LSTM and BiLSTM in Forecasting Time Series. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"ref_43","first-page":"235","article-title":"Review of climate change in the Yellow River Basin","volume":"41","author":"Wang","year":"2021","journal-title":"J. Desert Res."},{"key":"ref_44","first-page":"1048","article-title":"Characteristics of climate change in the Yellow River basin during recent 40 years","volume":"51","author":"Huang","year":"2020","journal-title":"J. Hydraul. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6455","DOI":"10.1002\/joc.7206","article-title":"Historical and projected climate change over three major river basins in China from Fifth and Sixth Coupled Model Intercomparison Project models","volume":"41","author":"Zhu","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_46","first-page":"750","article-title":"A downscaling method for land surface air temperature of ERA5 reanalysis dataset under complex terrain conditions in mountainous areas","volume":"24","author":"Yu","year":"2022","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sebbar, B.E., Khabba, S., Merlin, O., Simonneaux, V., Hachimi, C.E., Kharrou, M.H., and Chehbouni, A. (2023). Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions. Atmosphere, 14.","DOI":"10.3390\/atmos14040610"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:48Z","timestamp":1760112108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,21]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183510"],"URL":"https:\/\/doi.org\/10.3390\/rs16183510","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,21]]}}}