{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:22:04Z","timestamp":1767705724944,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2019YFC1509202"],"award-info":[{"award-number":["2019YFC1509202"]}]},{"name":"the National Science Foundation of China","award":["41772350, 61371189, and 41701513"],"award-info":[{"award-number":["41772350, 61371189, and 41701513"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is an important parameter indispensable for studying the substance and energy exchanges between the land surface and the atmosphere, climate changes, and other related aspects. However, due to cloud cover, there are many null values in MODIS (Moderate Resolution Imaging Spectroradiometer) LST data, which prevents such data from being widely used. Therefore, an LST reconstruction method is proposed by combining data decomposition with data prediction\u2014SSA (Singular Spectrum Analysis) and BiLSTM (Bidirectional Long Short-Term Memory). This method consists of two major processes, namely, rough LST reconstruction based on the SSA model and refined LST reconstruction based on the BiLSTM model. The accuracy of the proposed method is verified through \u201cremoval\u2013reconstruction\u2013comparison\u201d using remote sensing data and measured data. The verification results show that when the rate of original missing values in the LST time series for the study area is lower than 10%, the RMSE is smaller than 1.1 K, and the correlation coefficient is more significant than 0.98. Even when the rate of missing data is 40% and 50%, the proposed method remains accurate, the values of RMSE are 1.8331 K and 2.2929 K, and the importance of R2 are 0.9856 and 0.9800, respectively. The proposed method is compared with other existing LST reconstruction methods. The results of the comparative analysis indicate that the proposed method is superior to other methods in terms of reconstruction accuracy and stability. Moreover, the LST data reconstructed using the proposed method are highly consistent with the measured data, which further proves the accuracy of this method in LST reconstruction. The research findings provide a new technique and idea for accurate LST reconstruction.<\/jats:p>","DOI":"10.3390\/rs14040958","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T21:36:24Z","timestamp":1645047384000},"page":"958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["MODIS Land Surface Temperature Product Reconstruction Based on the SSA-BiLSTM Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianyong","family":"Cui","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Song","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinjian","family":"Shan","sequence":"additional","affiliation":[{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-1013","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.5194\/essd-12-2555-2020","article-title":"A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017","volume":"12","author":"Zhao","year":"2020","journal-title":"Earth Syst. 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