{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:58:22Z","timestamp":1776851902524,"version":"3.51.2"},"reference-count":51,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"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":["41871242"],"award-info":[{"award-number":["41871242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42041005"],"award-info":[{"award-number":["42041005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research (STEP) program","award":["2019QZKK0304"],"award-info":[{"award-number":["2019QZKK0304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a crucial biophysical parameter related closely to the land\u2013atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time tp in Zhangye and Beijing of China. Further, the predicted LST at tp was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at tp. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas.<\/jats:p>","DOI":"10.3390\/rs13193885","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T21:39:29Z","timestamp":1632865169000},"page":"3885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Framework for Generating High Spatiotemporal Resolution Land Surface Temperature in Heterogeneous Areas"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9256-5644","authenticated-orcid":false,"given":"Xinming","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoning","family":"Song","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Leng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotao","family":"Li","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5524-2718","authenticated-orcid":false,"given":"Liang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9564-2994","authenticated-orcid":false,"given":"Da","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuohao","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1080\/01431161.2018.1460513","article-title":"Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product","volume":"40","author":"Duan","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"2680","DOI":"10.1109\/TGRS.2020.3002821","article-title":"Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2009.07.017","article-title":"Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation","volume":"113","author":"Stathopoulou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.rse.2018.02.062","article-title":"Two-source energy balance modeling of evapotranspiration in alpine grasslands","volume":"209","author":"Castelli","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10666-014-9426-2","article-title":"The effect of urban expansion on urban surface temperature in Shenyang, China: An analysis with Landsat imagery","volume":"20","author":"Lu","year":"2015","journal-title":"Environ. Model. Assess."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1109\/JSTARS.2020.3048823","article-title":"Interannual spatiotemporal variations of land surface temperature in China from 2003 to 2018","volume":"14","author":"Yu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, W., He, J., Wu, Y., Xiong, D., Wen, F., and Li, A. (2019). An analysis of land surface temperature trends in the central Himalayan region based on MODIS products. Remote Sens., 11.","DOI":"10.3390\/rs11080900"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2019.02.006","article-title":"Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures","volume":"224","author":"Xia","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2013.03.023","article-title":"Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration","volume":"135","author":"Bindhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","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_20","first-page":"1396","article-title":"Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring","volume":"17","author":"Wu","year":"2015","journal-title":"Environ. SCI-Proc. IMP"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Yang, G.J., Weng, Q.H., Pu, R.L., Gao, F., Sun, C.H., Li, H., and Zhao, C.J. (2016). Evaluation of ASTER-like daily land surface temperature by fusing ASTER and MODIS data during the HiWATER-MUSOEXE. Remote Sens., 8.","DOI":"10.3390\/rs8010075"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xia, H.P., Chen, Y.H., Zhao, Y.T., and Chen, Z.Y. (2018). \u201cRegression-then-Fusion\u201d or \u201cFusion-then-Regression\u201d? A theoretical analysis for generating high spatiotemporal resolution land surface temperatures. Remote Sens., 10.","DOI":"10.3390\/rs10091382"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2015.08.015","article-title":"A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature","volume":"169","author":"Moosavi","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.rse.2011.01.004","article-title":"Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area","volume":"115","author":"Yang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, J., Liu, S.M., Li, M.S., Zhan, W.F., Xu, Z.W., and Xu, T.R. (2016). Quantification of the scale effect in downscaling remotely sensed land surface temperature. Remote Sens., 8.","DOI":"10.3390\/rs8120975"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 MODIS land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2019.02.020","article-title":"Validation of Collection 6 MODIS land surface temperature product using in situ measurements","volume":"225","author":"Duan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1109\/JSTARS.2020.3014586","article-title":"An efficient framework for producing Landsat based land surface temperature data using google earth engine","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/2150704X.2015.1130877","article-title":"Towards an operational method for land surface temperature retrieval from Landsat 8 data","volume":"7","author":"Zhang","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_34","first-page":"1","article-title":"Correlation analysis of land surface temperature and topographic elements in Hangzhou, China","volume":"10","author":"Peng","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_35","first-page":"57","article-title":"Vertical accuracy assessment of SRTM Ver 4.1 and ASTER GDEM Ver 2 using GPS measurements in central west of Tunisia","volume":"8","author":"Chaieb","year":"2016","journal-title":"J. GIS"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1109\/JSTARS.2016.2514367","article-title":"Downscaling of Landsat and MODIS land surface temperature over the heterogeneous urban area of Milan","volume":"9","author":"Bonafoni","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tang, K., Zhu, H.C., and Ni, P. (2021). Spatial downscaling of land surface temperature over heterogeneous regions using random forest regression considering spatial features. Remote Sens., 13.","DOI":"10.3390\/rs13183645"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1109\/JSTARS.2020.2968809","article-title":"Spatial downscaling of MODIS land surface temperature based on geographically weighted autoregressive model","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, Y.B., Cao, C., Pan, X., Li, X.L., 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_41","first-page":"23","article-title":"Downscaling land surface temperatures with multi-spectral and multi-resolution images","volume":"18","author":"Zhan","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TGRS.2007.904834","article-title":"Land surface emissivity retrieval from different VNIR and TIR sensors","volume":"46","author":"Sobrino","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1080\/01431161.2018.1547448","article-title":"Analysis of remotely-sensed ecological indexes\u2019 influence on urban thermal environment dynamic using an integrated ecological index: A case study of Xi\u2019an, China","volume":"40","author":"Zhu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.rse.2018.12.008","article-title":"A practical method for reducing terrain effect on land surface temperature using random forest regression","volume":"221","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/JSTARS.2016.2519099","article-title":"Evaluation of disaggregation methods for downscaling MODIS land surface temperature to Landsat spatial resolution in Barrax test site","volume":"9","author":"Bisquert","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"111863","DOI":"10.1016\/j.rse.2020.111863","article-title":"Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach","volume":"246","author":"Long","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111931","DOI":"10.1016\/j.rse.2020.111931","article-title":"Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS\/Terra land products and MSG geostationary satellite data","volume":"247","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3885\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:06:28Z","timestamp":1760166388000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,28]]},"references-count":51,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193885"],"URL":"https:\/\/doi.org\/10.3390\/rs13193885","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,28]]}}}