{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:26:33Z","timestamp":1771025193646,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFF0703900"],"award-info":[{"award-number":["2021YFF0703900"]}]},{"name":"National Key Research and Development Program of China","award":["41601212"],"award-info":[{"award-number":["41601212"]}]},{"name":"National Key Research and Development Program of China","award":["42206035"],"award-info":[{"award-number":["42206035"]}]},{"name":"National Key Research and Development Program of 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Council","award":["JCYJ20190806170814498"],"award-info":[{"award-number":["JCYJ20190806170814498"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["2021YFF0703900"],"award-info":[{"award-number":["2021YFF0703900"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["41601212"],"award-info":[{"award-number":["41601212"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["42206035"],"award-info":[{"award-number":["42206035"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["42171323"],"award-info":[{"award-number":["42171323"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["42001286"],"award-info":[{"award-number":["42001286"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["KCXFZ202002011006298"],"award-info":[{"award-number":["KCXFZ202002011006298"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["KCXFZ20201221173613035"],"award-info":[{"award-number":["KCXFZ20201221173613035"]}]},{"name":"Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program)","award":["JCYJ20190806170814498"],"award-info":[{"award-number":["JCYJ20190806170814498"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The unmixing-based spatiotemporal fusion model is one of the effective ways to solve limitations in temporal and spatial resolution tradeoffs in a single satellite sensor. By using fusion data from different satellite platforms, high resolution in both temporal and spatial domains can be produced. However, due to the ill-posed characteristic of the unmixing function, the model performance may vary due to the different model setups. The key factors affecting the model stability most and how to set up the unmixing strategy for data downscaling remain unknown. In this study, we use the multisource land surface temperature as the case and focus on the three major factors to analyze the stability of the unmixing-based fusion model: (1) the definition of the homogeneous change regions (HCRs), (2) the unmixing levels, and (3) the number of HCRs. The spatiotemporal data fusion model U-STFM was used as the baseline model. The results show: (1) The clustering-based algorithm is more suitable for detecting HCRs for unmixing. Compared with the multi-resolution segmentation algorithm and k-means algorithm, the ISODATA clustering algorithm can more accurately describe LST\u2019s temporal and spatial changes on HCRs. (2) For the U-STFM model, applying the unmixing processing at the change ratio level can significantly reduce the additive and multiplicative noise of the prediction. (3) There is a tradeoff effect between the number of HCRs and the solvability of the linear unmixing function. The larger the number of HCRs (less than the available MODIS pixels), the more stable the model is. (4) For the fusion of the daily 30 m scale LST product, compared with STARFM and ESTARFM, the modified U-STFM (iso_USTFM) achieved higher prediction accuracy and a lower error (R\u00a02: 0.87 and RMSE:1.09 k). With the findings of this study, daily fine-scale LST products can be predicted based on the unmixing-based spatial\u2013temporal model with lower uncertainty and stable prediction.<\/jats:p>","DOI":"10.3390\/rs15040901","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T02:56:08Z","timestamp":1675738568000},"page":"901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Stability Analysis of Unmixing-Based Spatiotemporal Fusion Model: A Case of Land Surface Temperature Product Downscaling"],"prefix":"10.3390","volume":"15","author":[{"given":"Min","family":"Li","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"given":"Shanxin","family":"Guo","sequence":"additional","affiliation":[{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China"}]},{"given":"Jinsong","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China"}]},{"given":"Yuguang","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4575-0836","authenticated-orcid":false,"given":"Luyi","family":"Sun","sequence":"additional","affiliation":[{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3276-1130","authenticated-orcid":false,"given":"Longlong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China"}]},{"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7083-3583","authenticated-orcid":false,"given":"Hongming","family":"Yao","sequence":"additional","affiliation":[{"name":"Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","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. 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