{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:53:00Z","timestamp":1774630380765,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41801244"],"award-info":[{"award-number":["41801244"]}]},{"name":"National Natural Science Foundation of China","award":["42074008"],"award-info":[{"award-number":["42074008"]}]},{"name":"National Natural Science Foundation of China","award":["51979040"],"award-info":[{"award-number":["51979040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.<\/jats:p>","DOI":"10.3390\/rs14133022","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"3022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"given":"Fanyi","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Surveying and Mapping Engineering, School of Transportation, Southeast University, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6142-2982","authenticated-orcid":false,"given":"Xin","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Surveying and Mapping Engineering, School of Transportation, Southeast University, Nanjing 211189, China"}]},{"given":"Haibo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2459-4619","authenticated-orcid":false,"given":"Mi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","first-page":"1177","article-title":"Landscape performance assessment of phase I of greenway around Qingshan Lake National Forest Park, Zhejiang Province","volume":"37","author":"Tang","year":"2020","journal-title":"J. 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