{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:25:52Z","timestamp":1774513552443,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T00:00:00Z","timestamp":1595030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2016YFC0400101"],"award-info":[{"award-number":["No.2016YFC0400101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Fund of China","award":["No. 41671331"],"award-info":[{"award-number":["No. 41671331"]}]},{"name":"Natural Science Fund of China","award":["No.41701483"],"award-info":[{"award-number":["No.41701483"]}]},{"name":"BNU Interdisciplinary Research Foundation for the First-Year Doctoral Candidates","award":["No. BNUXKJC1907"],"award-info":[{"award-number":["No. BNUXKJC1907"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.<\/jats:p>","DOI":"10.3390\/rs12142312","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:59:38Z","timestamp":1595242778000},"page":"2312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Junming","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3803-8170","authenticated-orcid":false,"given":"Yunjun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Yongxia","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Efficiency Utilization of Agricultural Water Resources, Ministry of Agriculture, School of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Yuhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8586-4243","authenticated-orcid":false,"given":"Kun","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xiaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7564-6509","authenticated-orcid":false,"given":"Ke","family":"Shang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1910-4286","authenticated-orcid":false,"given":"Xiangyi","family":"Bei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xiaozheng","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1111\/ejss.12142","article-title":"Evaluating the influence of surface soil moisture and soil surface roughness on optical directional reflectance factors","volume":"65","author":"Croft","year":"2014","journal-title":"Eur. 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