{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:35:30Z","timestamp":1771486530624,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Guiding Local Science and Technology Development Fund of Shandong\u2014Yellow River Basin Collaborative Science and Technology Innovation Special Project","award":["YDZX2023019"],"award-info":[{"award-number":["YDZX2023019"]}]},{"name":"Central Guiding Local Science and Technology Development Fund of Shandong\u2014Yellow River Basin Collaborative Science and Technology Innovation Special Project","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"Central Guiding Local Science and Technology Development Fund of Shandong\u2014Yellow River Basin Collaborative Science and Technology Innovation Special Project","award":["ZR2020QE281"],"award-info":[{"award-number":["ZR2020QE281"]}]},{"name":"Central Guiding Local Science and Technology Development Fund of Shandong\u2014Yellow River Basin Collaborative Science and Technology Innovation Special Project","award":["XDA19030402"],"award-info":[{"award-number":["XDA19030402"]}]},{"name":"Central Guiding Local Science and Technology Development Fund of Shandong\u2014Yellow River Basin Collaborative Science and Technology Innovation Special Project","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"Shandong Natural Science Foundation of China","award":["YDZX2023019"],"award-info":[{"award-number":["YDZX2023019"]}]},{"name":"Shandong Natural Science Foundation of China","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"Shandong Natural Science Foundation of China","award":["ZR2020QE281"],"award-info":[{"award-number":["ZR2020QE281"]}]},{"name":"Shandong Natural Science Foundation of China","award":["XDA19030402"],"award-info":[{"award-number":["XDA19030402"]}]},{"name":"Shandong Natural Science Foundation of China","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"CAS Strategic Priority Research Program","award":["YDZX2023019"],"award-info":[{"award-number":["YDZX2023019"]}]},{"name":"CAS Strategic Priority Research Program","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"CAS Strategic Priority Research Program","award":["ZR2020QE281"],"award-info":[{"award-number":["ZR2020QE281"]}]},{"name":"CAS Strategic Priority Research Program","award":["XDA19030402"],"award-info":[{"award-number":["XDA19030402"]}]},{"name":"CAS Strategic Priority Research Program","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["YDZX2023019"],"award-info":[{"award-number":["YDZX2023019"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["ZR2020QE281"],"award-info":[{"award-number":["ZR2020QE281"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["XDA19030402"],"award-info":[{"award-number":["XDA19030402"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstruction, while methods for predicting Chl-a concentrations remain limited. To address this issue, we adopted four deep learning approaches, including Convolutional LSTM Network (ConvLSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Eidetic 3D LSTM (E3D-LSTM), and Self-Attention ConvLSTM (SA-ConvLSTM) models, to predict Chl-a over the Yellow Sea and Bohai Sea (YBS) in China. Furthermore, 14 environmental variables obtained from the remote sensing data of Moderate-resolution Imaging Spectroradiometer (MODIS) and ECMWF Reanalysis v5 (ERA5) were utilized to predict the Chl-a concentrations in the study area. The results showed that all four models performed satisfactorily in predicting Chl-a concentrations in the YBS, with SA-ConvLSTM exhibiting a closer approximation to true values. Furthermore, we analyzed the impact of the Self-Attention Memory Module (SAM) on the prediction results. Compared to the ConvLSTM model, the SA-ConvLSTM model integrated with the SAM module better captured subtle large-scale variations within the study area. The SA-ConvLSTM model exhibited the highest prediction accuracy, and the one-month Pearson correlation coefficient reached 0.887. Our study provides an available approach for anticipating Chl-a concentrations over a large area of sea.<\/jats:p>","DOI":"10.3390\/rs15184486","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T21:41:12Z","timestamp":1694554872000},"page":"4486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Prediction of Sea Surface Chlorophyll-a Concentrations Based on Deep Learning and Time-Series Remote Sensing Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Lulu","family":"Yao","sequence":"first","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xiaopeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7387-1603","authenticated-orcid":false,"given":"Xiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5972-5474","authenticated-orcid":false,"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sammartino, M., Buongiorno Nardelli, B., Marullo, S., and Santoleri, R. 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