{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:44:26Z","timestamp":1767084266215,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42171383","CUG2106212","KLIGIP-2023-B04"],"award-info":[{"award-number":["42171383","CUG2106212","KLIGIP-2023-B04"]}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["42171383","CUG2106212","KLIGIP-2023-B04"],"award-info":[{"award-number":["42171383","CUG2106212","KLIGIP-2023-B04"]}]},{"name":"Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["42171383","CUG2106212","KLIGIP-2023-B04"],"award-info":[{"award-number":["42171383","CUG2106212","KLIGIP-2023-B04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Medium- to high-resolution imagery is indispensable for various applications. Combining images from Landsat 8 and Sentinel-2 can improve the accuracy of observing dynamic changes on the Earth\u2019s surface. Many researchers use Sentinel-2 10 m resolution data in conjunction with Landsat 8 30 m resolution data to generate 10 m resolution data series. However, current fusion techniques have some algorithmic weaknesses, such as simple processing of coarse or fine images, which fail to extract image features to the fullest extent, especially in rapidly changing land cover areas. Facing the aforementioned limitations, we proposed a multiscale and attention mechanism-based residual spatiotemporal fusion network (MARSTFN) that utilizes Sentinel-2 10 m resolution data and Landsat 8 15 m resolution data as auxiliary data to upgrade Landsat 8 30 m resolution data to 10 m resolution. In this network, we utilized multiscale and attention mechanisms to extract features from coarse and fine images separately. Subsequently, the features outputted from all input branches are combined and further feature information is extracted through residual networks and skip connections. Finally, the features obtained from the residual network are merged with the feature information of the coarsely processed images from the multiscale mechanism to generate accurate prediction images. To assess the efficacy of our model, we compared it with existing models on two datasets. Results demonstrated that our fusion model outperformed baseline methods across various evaluation indicators, highlighting its ability to integrate Sentinel-2 and Landsat 8 data to produce 10 m resolution data.<\/jats:p>","DOI":"10.3390\/rs16061033","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T04:47:05Z","timestamp":1710478025000},"page":"1033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Learning-Based Spatiotemporal Fusion Architecture of Landsat 8 and Sentinel-2 Data for 10 m Series Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0571-4083","authenticated-orcid":false,"given":"Qing","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, No. 68 Jincheng Street, East Lake High-Tech Development Zone, Wuhan 430074, China"}]},{"given":"Ruixiang","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, No. 68 Jincheng Street, East Lake High-Tech Development Zone, Wuhan 430074, China"}]},{"given":"Jingan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}]},{"given":"Fan","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, No. 68 Jincheng Street, East Lake High-Tech Development Zone, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"key":"ref_1","first-page":"6","article-title":"Towards monitoring land-cover and land-use changes at a global scale: The Global Land Survey 2005","volume":"74","author":"Gutman","year":"2008","journal-title":"Photogramm. 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