{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T11:04:15Z","timestamp":1780571055584,"version":"3.54.1"},"reference-count":80,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Data sharing fundamental program for Construction of the National Science and Technology Infrastructure Platform of China","award":["Y719H71006"],"award-info":[{"award-number":["Y719H71006"]}]},{"name":"Function Development and Knowledge Innovation Program of Lanzhou Regional Center of Resources and Environmental Science Instrument, Chinese Academy of Sciences","award":["lg201405"],"award-info":[{"award-number":["lg201405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65\u20130.88, the root mean square error (RMSE) yielded a range of 1.6\u20133.4 \u00b0C, and the averaged bias was 0.6 \u00b0C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 \u00b0C to 1.5 \u00b0C with an average value of 1.1 \u00b0C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 \u00b0C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.<\/jats:p>","DOI":"10.3390\/s20154337","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T05:56:46Z","timestamp":1596520606000},"page":"4337","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Data Fusion Modeling Framework for Retrieval of Land Surface Temperature from Landsat-8 and MODIS Data"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2064-8616","authenticated-orcid":false,"given":"Guohui","family":"Zhao","sequence":"first","affiliation":[{"name":"Science Big Data Center of Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8905-9006","authenticated-orcid":false,"given":"Yaonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Science Big Data Center of Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9710-1868","authenticated-orcid":false,"given":"Junlei","family":"Tan","sequence":"additional","affiliation":[{"name":"Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanrun","family":"Ren","sequence":"additional","affiliation":[{"name":"Science Big Data Center of Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singh, S., Bhardwaj, A., Singh, A., Sam, L., Shekhar, M., Martin-Torres, J., and Zorzano, M.-P. 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