{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T20:57:03Z","timestamp":1774904223562,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Provincial and Ministerial Level Key Laboratory Scientific Research Project","award":["2242023K30017"],"award-info":[{"award-number":["2242023K30017"]}]},{"name":"Provincial and Ministerial Level Key Laboratory Scientific Research Project","award":["BE2022820"],"award-info":[{"award-number":["BE2022820"]}]},{"name":"Jiangsu Provincial Key R&amp;D Programme (Social Devel-opment)","award":["2242023K30017"],"award-info":[{"award-number":["2242023K30017"]}]},{"name":"Jiangsu Provincial Key R&amp;D Programme (Social Devel-opment)","award":["BE2022820"],"award-info":[{"award-number":["BE2022820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg\/ha, with an average value of 64.20 Mg\/ha.<\/jats:p>","DOI":"10.3390\/rs16061074","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T09:39:31Z","timestamp":1710841171000},"page":"1074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6142-2982","authenticated-orcid":false,"given":"Xin","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211102, China"},{"name":"Key Laboratory of Safety and Risk Management on Transport Infrastructures, Ministry of Transport, PRC, Nanjing 210000, China"}]},{"given":"Jiejie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211102, China"}]},{"given":"Fanyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8230-9033","authenticated-orcid":false,"given":"Haibo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China"}]},{"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":[[2024,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, F., Tian, X., Zhang, H., and Jiang, M. 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