{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T22:53:04Z","timestamp":1775170384644,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Innovation Project of the Technology Innovation Center for Land Spatial Eco-restoration in Metropolitan Area, MNR","award":["CXZX202206"],"award-info":[{"award-number":["CXZX202206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg\/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg\/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg\/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 \u00b1 1.63 Mg\/ha, with a total of 11.00 \u00b1 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations.<\/jats:p>","DOI":"10.3390\/rs14215487","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T06:01:28Z","timestamp":1667282488000},"page":"5487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhi","family":"Tang","sequence":"first","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographical Science, East China Normal University, Shanghai 200241, China"},{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources of China, Shanghai 200241, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3847-0521","authenticated-orcid":false,"given":"Xiaosheng","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-9652","authenticated-orcid":false,"given":"Yonghua","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographical Science, East China Normal University, Shanghai 200241, China"},{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources of China, Shanghai 200241, China"},{"name":"Department of Geography, McGill University, Montreal, QC H4G 2Y8, Canada"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Land Spatial Eco-Restoration in Metropolitan Area, Ministry of Natural Resources of China, Shanghai 200003, China"},{"name":"Centre for Shanghai Municipal Construction Land and Land Consolidation, Shanghai Municipal Bureau of Planning and Natural Resources, Shanghai 200003, China"}]},{"given":"Zhongyang","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographical Science, East China Normal University, Shanghai 200241, China"},{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources of China, Shanghai 200241, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","unstructured":"FAO (2020). 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