{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:56:01Z","timestamp":1781279761675,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of State Key Laboratory of Urban and Regional Ecology","award":["SKLURE2023-2-6"],"award-info":[{"award-number":["SKLURE2023-2-6"]}]},{"name":"Open Fund of State Key Laboratory of Urban and Regional Ecology","award":["OFSLRSS202119"],"award-info":[{"award-number":["OFSLRSS202119"]}]},{"name":"Open Fund of State Key Laboratory of Urban and Regional Ecology","award":["42101321"],"award-info":[{"award-number":["42101321"]}]},{"name":"Open Fund of State Key Laboratory of Urban and Regional Ecology","award":["2021M701653"],"award-info":[{"award-number":["2021M701653"]}]},{"name":"State Key Laboratory of Remote Sensing Science","award":["SKLURE2023-2-6"],"award-info":[{"award-number":["SKLURE2023-2-6"]}]},{"name":"State Key Laboratory of Remote Sensing Science","award":["OFSLRSS202119"],"award-info":[{"award-number":["OFSLRSS202119"]}]},{"name":"State Key Laboratory of Remote Sensing Science","award":["42101321"],"award-info":[{"award-number":["42101321"]}]},{"name":"State Key Laboratory of Remote Sensing Science","award":["2021M701653"],"award-info":[{"award-number":["2021M701653"]}]},{"name":"National Natural Science Foundation of China","award":["SKLURE2023-2-6"],"award-info":[{"award-number":["SKLURE2023-2-6"]}]},{"name":"National Natural Science Foundation of China","award":["OFSLRSS202119"],"award-info":[{"award-number":["OFSLRSS202119"]}]},{"name":"National Natural Science Foundation of China","award":["42101321"],"award-info":[{"award-number":["42101321"]}]},{"name":"National Natural Science Foundation of China","award":["2021M701653"],"award-info":[{"award-number":["2021M701653"]}]},{"name":"China Postdoctoral Science Foundation","award":["SKLURE2023-2-6"],"award-info":[{"award-number":["SKLURE2023-2-6"]}]},{"name":"China Postdoctoral Science Foundation","award":["OFSLRSS202119"],"award-info":[{"award-number":["OFSLRSS202119"]}]},{"name":"China Postdoctoral Science Foundation","award":["42101321"],"award-info":[{"award-number":["42101321"]}]},{"name":"China Postdoctoral Science Foundation","award":["2021M701653"],"award-info":[{"award-number":["2021M701653"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012\u20132021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R2 and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and \u22120.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.<\/jats:p>","DOI":"10.3390\/rs15184441","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:09:21Z","timestamp":1694423361000},"page":"4441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8870-9887","authenticated-orcid":false,"given":"Ning","family":"Wang","sequence":"first","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"},{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8766-8465","authenticated-orcid":false,"given":"Jia","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Su","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0986-6479","authenticated-orcid":false,"given":"Qingjiu","family":"Tian","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.isprsjprs.2022.03.009","article-title":"Downscaling Land Surface Temperature: A Framework Based on Geographically and Temporally Neural Network Weighted Autoregressive Model with Spatio-Temporal Fused Scaling Factors","volume":"187","author":"Wu","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111495","DOI":"10.1016\/j.rse.2019.111495","article-title":"Urban Air Temperature Model Using GOES-16 LST and a Diurnal Regressive Neural Network Algorithm","volume":"237","author":"Hrisko","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112361","DOI":"10.1016\/j.rse.2021.112361","article-title":"Modeling the Angular Effect of MODIS LST in Urban Areas: A Case Study of Toulouse, France","volume":"257","author":"Wang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.rse.2008.09.016","article-title":"The Yearly Land Cover Dynamics (YLCD) Method: An Analysis of Global Vegetation from NDVI and LST Parameters","volume":"113","author":"Julien","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1016\/j.rse.2010.04.012","article-title":"Spatial and Temporal Variations of Summer Surface Temperatures of Wet Polygonal Tundra in Siberia-Implications for MODIS LST Based Permafrost Monitoring","volume":"114","author":"Langer","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1080\/01431160110115041","article-title":"Land Surface Temperature and Emissivity Estimation from Passive Sensor Data: Theory and Practice-Current Trends","volume":"23","author":"Dash","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","first-page":"140","article-title":"A Simple Retrieval Method of Land Surface Temperature from AMSR-E Passive Microwave Data\u2014A Case Study over Southern China during the Strong Snow Disaster of 2008","volume":"13","author":"Chen","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2014.08.009","article-title":"Modeling Diurnal Land Temperature Cycles over Los Angeles Using Downscaled GOES Imagery","volume":"97","author":"Weng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2012.12.014","article-title":"Disaggregation of Remotely Sensed Land Surface Temperature: Literature Survey, Taxonomy, Issues, and Caveats","volume":"131","author":"Zhan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating Daily Land Surface Temperature at Landsat Resolution by Fusing Landsat and MODIS Data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2014.09.013","article-title":"Integrated Fusion of Multi-Scale Polar-Orbiting and Geostationary Satellite Observations for the Mapping of High Spatial and Temporal Resolution Land Surface Temperature","volume":"156","author":"Wu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/TGRS.2020.2999943","article-title":"Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network","volume":"59","author":"Yin","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/TGRS.2010.2060342","article-title":"Sharpening Thermal Imageries: A Generalized Theoretical Framework From an Assimilation Perspective","volume":"49","author":"Zhan","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0034-4257(03)00036-1","article-title":"Estimating Subpixel Surface Temperatures and Energy Fluxes from the Vegetation Index-Radiometric Temperature Relationship","volume":"85","author":"Kustas","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.rse.2006.10.006","article-title":"A Vegetation Index Based Technique for Spatial Sharpening of Thermal Imagery","volume":"107","author":"Agam","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.rse.2011.03.008","article-title":"High-Resolution Urban Thermal Sharpener (HUTS)","volume":"115","author":"Dominguez","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6458","DOI":"10.1109\/TGRS.2016.2585198","article-title":"Spatial Downscaling of MODIS Land Surface Temperatures Using Geographically Weighted Regression: Case Study in Northern China","volume":"54","author":"Duan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2016.03.006","article-title":"Downscaling Land Surface Temperatures at Regional Scales with Random Forest Regression","volume":"178","author":"Hutengs","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/LGRS.2013.2257668","article-title":"Downscaling Geostationary Land Surface Temperature Imagery for Urban Analysis","volume":"10","author":"Keramitsoglou","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sismanidis, P., Keramitsoglou, I., Kiranoudis, C.T., and Bechtel, B. (2016). Assessing the Capability of a Downscaled Urban Land Surface Temperature Time Series to Reproduce the Spatiotemporal Features of the Original Data. Remote Sens., 8.","DOI":"10.3390\/rs8040274"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TGRS.2009.2033180","article-title":"A Novel Method to Estimate Subpixel Temperature by Fusing Solar-Reflective and Thermal-Infrared Remote-Sensing Data with an Artificial Neural Network","volume":"48","author":"Yang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1109\/JSTARS.2019.2896923","article-title":"Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature","volume":"12","author":"Li","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2017.04.008","article-title":"A Framework for the Retrieval of All-Weather Land Surface Temperature at a High Spatial Resolution from Polar-Orbiting Thermal Infrared and Passive Microwave Data","volume":"195","author":"Duan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1080\/01431161.2018.1508920","article-title":"A Physically Based Algorithm for Retrieving Land Surface Temperature under Cloudy Conditions from AMSR2 Passive Microwave Measurements","volume":"40","author":"Huang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112104","DOI":"10.1016\/j.rse.2020.112104","article-title":"Estimating Lake Temperature Profile and Evaporation Losses by Leveraging MODIS LST Data","volume":"251","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"180072","DOI":"10.2136\/vzj2018.04.0072","article-title":"The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China","volume":"17","author":"Liu","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"D11109","DOI":"10.1029\/2004JD005566","article-title":"Estimation of Surface Long Wave Radiation and Broadband Emissivity Using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature\/Emissivity Products","volume":"110","author":"Wang","year":"2005","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1175\/JAMC-D-18-0256.1","article-title":"A Global Analysis of Land Surface Temperature Diurnal Cycle Using MODIS Observations","volume":"58","author":"Sharifnezhadazizi","year":"2019","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mao, F., Li, X., Du, H., Zhou, G., Han, N., Xu, X., Liu, Y., Chen, L., and Cui, L. (2017). Comparison of Two Data Assimilation Methods for Improving MODIS LAI Time Series for Bamboo Forests. Remote Sens., 9.","DOI":"10.3390\/rs9050401"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s00271-011-0287-z","article-title":"Estimating Seasonal Evapotranspiration from Temporal Satellite Images","volume":"30","author":"Singh","year":"2012","journal-title":"Irrig. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3453","DOI":"10.5194\/amt-10-3453-2017","article-title":"Smoothing Data Series by Means of Cubic Splines: Quality of Approximation and Introduction of a Repeating Spline Approach","volume":"10","author":"Wendt","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, H., Pu, R., and Liu, X. (2016). A New Image Processing Procedure Integrating PCI-RPC and ArcGIS-Spline Tools to Improve the Orthorectification Accuracy of High-Resolution Satellite Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8100827"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.04.024","article-title":"Estimating Air Surface Temperature in Portugal Using MODIS LST Data","volume":"124","author":"Benali","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"78","DOI":"10.2747\/1548-1603.43.1.78","article-title":"Statistical Estimation of Daily Maximum and Minimum Air Temperatures from MODIS LST Data over the State of Mississippi","volume":"43","author":"Mostovoy","year":"2006","journal-title":"Gisci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, Y.Z., Cai, W.H., and Yang, J. (2017). Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China. Remote Sens., 9.","DOI":"10.3390\/rs9050410"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ermida, S.L., Soares, P., Mantas, V., G\u00f6ttsche, F.-M., and Trigo, I.F. (2020). Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sens., 12.","DOI":"10.3390\/rs12091471"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2014.03.016","article-title":"Validation of Remotely Sensed Surface Temperature over an Oak Woodland Landscape\u2014The Problem of Viewing and Illumination Geometries","volume":"148","author":"Ermida","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/TGRS.2020.2998945","article-title":"Temperature-Based and Radiance-Based Validation of the Collection 6 MYD11 and MYD21 Land Surface Temperature Products Over Barren Surfaces in Northwestern China","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"111188","DOI":"10.1016\/j.rse.2019.05.007","article-title":"Supplement of the Radiance-Based Method to Validate Satellite-Derived Land Surface Temperature Products over Heterogeneous Land Surfaces","volume":"230","author":"Yu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112612","DOI":"10.1016\/j.rse.2021.112612","article-title":"A Simple yet Robust Framework to Estimate Accurate Daily Mean Land Surface Temperature from Thermal Observations of Tandem Polar Orbiters","volume":"264","author":"Hong","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Guo, Z., Wang, N., Shen, B., Gu, Z., Wu, Y., and Chen, A. (2021). Recent Spatiotemporal Trends in Glacier Snowline Altitude at the End of the Melt Season in the Qilian Mountains, China. Remote Sens., 13.","DOI":"10.3390\/rs13234935"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4441\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:47:59Z","timestamp":1760129279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":44,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184441"],"URL":"https:\/\/doi.org\/10.3390\/rs15184441","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]}}}