{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:58:25Z","timestamp":1776851905086,"version":"3.51.2"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T00:00:00Z","timestamp":1535587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution LSTs, whereas the spatiotemporal fusion method can dynamically downscale LSTs; however, the resolution of downscaled LSTs is limited by the availability of the fine-resolution LSTs. Few studies have combined these two methods to generate high spatiotemporal resolution LSTs. This study proposes two strategies for combining regression and fusion methods to generate high spatiotemporal resolution LSTs, namely, the \u201cregression-then-fusion\u201d (R-F) and \u201cfusion-then-regression\u201d (F-R) methods, and discusses the criteria used to determine which strategy is better. The R-F and F-R have several advantages: (1) they fully exploit the information in the available data on the visible and near infrared (VNIR) and thermal infrared (TIR) bands; (2) they downscale the LST time series to a finer resolution corresponding to that of VNIR data; and (3) they inherit high spatial reconstructions from the regression method and dynamic temporal reconveyance from the fusion method. The R-F and F-R were tested with different start times and target times using Landsat 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer data. The results showed that the R-F performed better than the F-R when the regression error at the start time was smaller than that at the target time, and vice versa.<\/jats:p>","DOI":"10.3390\/rs10091382","type":"journal-article","created":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T10:30:06Z","timestamp":1535625006000},"page":"1382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["\u201cRegression-then-Fusion\u201d or \u201cFusion-then-Regression\u201d? A Theoretical Analysis for Generating High Spatiotemporal Resolution Land Surface Temperatures"],"prefix":"10.3390","volume":"10","author":[{"given":"Haiping","family":"Xia","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-7303","authenticated-orcid":false,"given":"Yunhao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutong","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixuan","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1016\/j.rse.2008.07.009","article-title":"A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.rse.2012.06.009","article-title":"Applications of a remote sensing-based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations","volume":"124","author":"Cammalleri","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2011.08.025","article-title":"Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources","volume":"122","author":"Anderson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"34","article-title":"Assessment with satellite data of the urban heat island effects in Asian mega cities","volume":"8","author":"Tran","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6805","DOI":"10.1080\/01431161.2012.692833","article-title":"MODIS and NOAA-AVHRR land surface temperature data detect a thermal anomaly preceding the 11 March 2011 Tohoku earthquake","volume":"33","author":"Zoran","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","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_8","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":"Guijun","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","unstructured":"Essa, W., Verbeiren, B., van der Kwast, J., and Batelaan, O. (2017). Improved Dis Trad for downscaling thermal MODIS imagery over urban areas. Remote Sens., 9.","DOI":"10.3390\/rs9121243"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.measurement.2018.04.092","article-title":"Thermal sharpening of land surface temperature maps based on the impervious surface index with the TsHARP method to ASTER satellite data: A case study from the metropolitan Kuala Lumpur, Malaysia","volume":"125","author":"Sattari","year":"2018","journal-title":"Measurement"},{"key":"ref_13","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":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"547","DOI":"10.14358\/PERS.75.5.547","article-title":"An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis","volume":"75","author":"Nichol","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","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_16","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_17","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_18","first-page":"459","article-title":"The use of intensity-hue-saturation transformations for merging spot panchromatic and multispectral image data","volume":"56","author":"Carper","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_19","first-page":"1067","article-title":"Multiresolution wavelet decomposition image merger of Landsat thematic mapper and spot panchromatic data","volume":"62","author":"Yocky","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/36.763274","article-title":"Multiresolution-based image fusion with additive wavelet decomposition","volume":"37","author":"Nunez","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cai, F., Tian, J., and Williams, T.K.A. (2018). Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions. Remote Sens., 10.","DOI":"10.3390\/rs10040527"},{"key":"ref_23","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_24","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_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":"3184","DOI":"10.3390\/rs4103184","article-title":"Downscaling land surface temperature in an urban area: A case study for Hamburg, Germany","volume":"4","author":"Bechtel","year":"2012","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4424","DOI":"10.3390\/rs70404424","article-title":"Advancing of land surface temperature retrieval using extreme learning machine and spatio-temporal adaptive data fusion algorithm","volume":"7","author":"Bai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/LGRS.2006.885869","article-title":"Feasibility of retrieving land-surface temperature from ASTER TIR bands using two-channel algorithms: A case study of agricultural areas","volume":"4","author":"Sobrino","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"178","article-title":"Evaluating a thermal image sharpening model over a mixed agricultural landscape in India","volume":"13","author":"Jeganathan","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"65","DOI":"10.14358\/PERS.84.2.65","article-title":"\u201cBlend-then-index\u201d or \u201cindex-then-blend\u201d: A theoretical analysis for generating high-resolution NDVI time series by STARFM","volume":"84","author":"Chen","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","first-page":"361","article-title":"Downscaling remotely sensed land surface temperatures: A comparison of typical methods","volume":"17","author":"Quan","year":"2013","journal-title":"J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2017.12.003","article-title":"An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes","volume":"206","author":"Quan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2014.03.037","article-title":"Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a gaussian volume model","volume":"149","author":"Quan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s11269-013-0337-9","article-title":"Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application","volume":"27","author":"Srivastava","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.scitotenv.2017.10.226","article-title":"Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece\u2014Climate change implications","volume":"616\u2013617","author":"Eleftheriou","year":"2018","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:10Z","timestamp":1760196130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,30]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["rs10091382"],"URL":"https:\/\/doi.org\/10.3390\/rs10091382","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,30]]}}}