{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:17:27Z","timestamp":1776874647914,"version":"3.51.2"},"reference-count":51,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41372330"],"award-info":[{"award-number":["41372330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The land surface temperature (LST) images obtained by thermal infrared remote sensing sensors are of great significance for numerous fields of research. However, the low spatial resolution is a drawback of LST images. Downscaling is an effective way to solve this problem. The traditional downscaling methods, however, have various drawbacks, including their low temporal and spectral resolutions, difficult processes, numerous errors, and single downscaling factor. They also rely on two or more separate satellite platforms. These drawbacks can be partially compensated for by the Sentinel-3 satellite\u2019s ability to acquire LST and multispectral images simultaneously. This paper proposes a downscaling model based on Sentinel-3 satellite and ASTER GDEM images\u2014D-DisTrad\u2014and compares the effects of the D-DisTrad model with DisTrad model and TsHARP model over four sites and four seasons. The mean bias (MB) range of the D-DisTrad model is \u22120.001\u20130.017 K, the mean absolute error (MAE) range is 0.103\u20130.891 K, and the root mean square error (RMSE) range is 0.220\u20131.235 K. The Pearson correlation coefficient (PCC) and R2 ranges are 0.938\u20130.994 and 0.889\u20130.989, respectively. The D-DisTrad model has the smallest error, the highest correlation, and the best visual effect, and can eliminate some \u201cmosaic\u201d effects in the original image. This paper shows that the D-DisTrad model can improve the spatial resolution and visual effects of LST images while maintaining high temporal resolution, and discusses the influence of the terrain and land cover on LST data.<\/jats:p>","DOI":"10.3390\/rs14225752","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:32:16Z","timestamp":1668479536000},"page":"5752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Generating Daily Land Surface Temperature Downscaling Data Based on Sentinel-3 Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2450-6060","authenticated-orcid":false,"given":"Zhoujin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lichun","family":"Sui","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s00704-014-1250-8","article-title":"The summer surface urban heat island of Bucharest (Romania) retrieved from MODIS images","volume":"121","author":"Cheval","year":"2014","journal-title":"Theor. Appl. Climatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.ocecoaman.2015.03.008","article-title":"Evaluation of surface urban heat island (SUHI) effect on coastal zone: The case of Istanbul Megacity","volume":"118","author":"Dihkan","year":"2015","journal-title":"Ocean Coast Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.ecolind.2012.01.001","article-title":"Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators\u2014An application for the city of Leipzig (Germany)","volume":"18","author":"Schwarz","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Feizizadeh, B., and Blaschke, T. (2012, January 22\u201327). Thermal remote sensing for examining the relationship between urban Land surface Temperature and land use\/cover in Tabriz city, Iran. Proceedings of the International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351056"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1007\/s10708-020-10178-4","article-title":"Spatio-temporal dynamic land cover changes and their impacts on the urban thermal environment in the Chittagong metropolitan area, Bangladesh","volume":"86","author":"Gazi","year":"2020","journal-title":"GeoJournal"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maffei, C., Alfieri, S.M., and Menenti, M. (2018). Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies. Remote Sens., 10.","DOI":"10.3390\/rs10111777"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.isprsjprs.2021.09.016","article-title":"Combining multi-spectral and thermal remote sensing to predict forest fire characteristics","volume":"181","author":"Maffei","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"100368","article-title":"A comparison between day and night land surface temperatures using acquired satellite thermal infrared data in a winter wheat field","volume":"19","author":"Abdullah","year":"2020","journal-title":"Remote Sens. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Islam, S., and Ma, M. (2018). Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016. ISPRS Int. J. Geoinf., 7.","DOI":"10.3390\/ijgi7120486"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16849","DOI":"10.3390\/rs71215857","article-title":"On the Role of Land Surface Temperature as Proxy of Soil Moisture Status for Drought Monitoring in Europe","volume":"7","author":"Cammalleri","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","first-page":"e01092","article-title":"Multi-temporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China","volume":"23","author":"Chi","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10712-008-9037-z","article-title":"Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data","volume":"29","author":"Kalma","year":"2008","journal-title":"Surv. Geophys."},{"key":"ref_13","unstructured":"Cao, X., Bao, A., and Li, L. (2009, January 4\u20135). A Study of Retrieval Land Surface Temperature and Evapotranspiration in Response to LUCC Based on Remote Sensing Data in Sanggong River. Proceedings of the International Conference on Environmental Science and Information Application Technology, Wuhan, China."},{"key":"ref_14","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\u2013radiometric temperature relationship","volume":"85","author":"Kustas","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., and Sobrino, J.A. (2019). Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sens., 11.","DOI":"10.3390\/rs11010048"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"153652","DOI":"10.1016\/j.scitotenv.2022.153652","article-title":"Combining GOES-R and ECOSTRESS land surface temperature data to investigate diurnal variations of surface urban heat island","volume":"823","author":"Chang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5000523","DOI":"10.1109\/TGRS.2021.3079879","article-title":"Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product","volume":"60","author":"Hulley","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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_19","first-page":"1808","article-title":"Land surface temperature downscaling in urban area: A case study of Beijing","volume":"25","author":"Li","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_20","first-page":"609","article-title":"Spatial Downscaling of MODIS Land Surface Temperature: Recent Research Trends, Challenges, and Future Directions","volume":"36","author":"Yoo","year":"2020","journal-title":"Korean J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cao, C., Pan, X., Li, X., and Zhu, X. (2017). Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression. Remote Sens., 9.","DOI":"10.3390\/rs9080789"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, R., Gao, W., and Peng, W. (2020). Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou. Remote Sens., 12.","DOI":"10.3390\/rs12132134"},{"key":"ref_23","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":"Feng","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_25","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_26","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_27","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_28","first-page":"3443","article-title":"Spatial downscaling of land surface temperature based on MODIS data","volume":"35","author":"Li","year":"2016","journal-title":"Chin. J. Ecol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.apsusc.2016.11.226","article-title":"Downscaling urban land surface temperature based on multi-scale factor","volume":"42","author":"Yang","year":"2017","journal-title":"Sci. Surv. Mapp."},{"key":"ref_30","first-page":"6","article-title":"Land Surface Temperature Downscaling Based on Multiple Factors","volume":"35","author":"Liu","year":"2020","journal-title":"Remote Sens. Inf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2017.10.046","article-title":"Spatio-temporal fusion for daily Sentinel-2 images","volume":"204","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with Erts. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFEETERS","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0273-1177(89)90481-X","article-title":"Remote sensing of arid soil surface color with Landsat thematic mapper","volume":"9","author":"Escadafal","year":"1989","journal-title":"Adv. Space Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1080\/014311698214578","article-title":"Pixel block intensity modulation: Adding spatial detail to TM band 6 thermal imagery","volume":"19","author":"Guo","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, G., Xue, H., and Feng, L. (2010, January 18\u201320). Assessment of ASTER GDEM Performance by Comparing with SRTM and ICESat\/GLAS Data in Central China. Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China.","DOI":"10.1109\/GEOINFORMATICS.2010.5567970"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huryna, H., Cohen, Y., Karnieli, A., Panov, N., Kustas, W.P., and Agam, N. (2019). Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11192304"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, Y., Ma, M., Zhu, X., and Ge, W. (2020). Research on spatial characteristics of metropolis development using nighttime light data: NTL based spatial characteristics of Beijing. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0242663"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yan, H., Zhou, G., and Lu, X. (2015, January 9). Comparative Analysis of Surface Soil Moisture Retrieval Using VSWI and TVDI in Karst Areas. Proceedings of the International Conference on Intelligent Earth Observing and Applications, Guilin, China.","DOI":"10.1117\/12.2207397"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"38","article-title":"Spatial Downscaling Research of the Land Surface Temperature in Karst Region","volume":"37","author":"Yin","year":"2021","journal-title":"Geogr. Geoinf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhu, L., Li, J., Wu, C., Yang, B., Li, Q., and Gong, H. (2009, January 12\u201317). Comparison of LST retrieval algorithms between single-channel and split-windows for high-resolution infrared camera. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5416922"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1080\/17538947.2022.2088873","article-title":"Estimation of 30\u2005m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+ data and machine learning methods","volume":"15","author":"Wang","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103692","DOI":"10.1016\/j.infrared.2021.103692","article-title":"Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions","volume":"115","author":"Zare","year":"2021","journal-title":"Infrared. Phys. Technol."},{"key":"ref_45","first-page":"4688","article-title":"A generalized single-channel method for retrieving land surface temperature from remote sensing data","volume":"108","author":"Sobrino","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/LGRS.2014.2312032","article-title":"Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data","volume":"11","author":"Sobrino","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112682","DOI":"10.1016\/j.rse.2021.112682","article-title":"On the land emissivity assumption and Landsat-derived surface urban heat islands: A global analysis","volume":"265","author":"Chakraborty","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Qin, Z., Li, W., Gao, M., and Zhang, H. (2006, January 13\u201314). Estimation of land surface emissivity for Landsat TM6 and its application to Lingxian Region in north China. Proceedings of the Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, Stockholm, Sweden.","DOI":"10.1117\/12.689310"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/JSTARS.2013.2272053","article-title":"Retrieving High-Resolution Surface Soil Moisture by Downscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data","volume":"7","author":"Song","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"L24406","DOI":"10.1029\/2007GL031485","article-title":"Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America","volume":"34","author":"Sun","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","first-page":"1","article-title":"A Taylor Expansion Algorithm for Spatial Downscaling of MODIS Land Surface Temperature","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5752\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:54Z","timestamp":1760145474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":51,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225752"],"URL":"https:\/\/doi.org\/10.3390\/rs14225752","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}