{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T18:28:54Z","timestamp":1783362534190,"version":"3.54.6"},"reference-count":79,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest aboveground biomass (AGB) is of great significance since it represents large carbon storage and may reduce global climate change. However, there are still considerable uncertainties in forest AGB estimates, especially in rugged regions, due to the lack of effective algorithms to remove the effects of topography and the lack of comprehensive comparisons of methods used for estimation. Here, we systematically compare the performance of three sources of remote sensing data used in forest AGB estimation, along with three machine-learning algorithms using extensive field measurements (N = 1058) made in the Khingan Mountains of north-eastern China in 2008. The datasets used were obtained from the LiDAR-based Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation satellite (ICESat\/GLAS), the optical-based Moderate Resolution Imaging Spectroradiometer (MODIS), and the SAR-based Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR). We show that terrain correction is effective for this mountainous study region and that the combination of terrain-corrected GLAS and PALSAR features with Random Forest regression produces the best results at the plot scale. Including further MODIS-based features added little power for prediction. Based upon the parsimonious data source combination, we created a map of AGB circa 2008 and its uncertainty, which yields a coefficient of determination (R2) of 0.82 and a root mean squared error of 16.84 Mg ha\u22121 when validated with field data. Forest AGB values in our study area were within the range 79.81 \u00b1 16.00 Mg ha\u22121, ~25% larger than a previous, SAR-based, analysis. Our result provides a historic benchmark for regional carbon budget estimation.<\/jats:p>","DOI":"10.3390\/rs14041039","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:48:41Z","timestamp":1645476521000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3843-2434","authenticated-orcid":false,"given":"Xiaoyi","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5851-6374","authenticated-orcid":false,"given":"Caixia","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanting","family":"Lv","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7189-0803","authenticated-orcid":false,"given":"Jinfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-4235","authenticated-orcid":false,"given":"Guishan","family":"Cui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Geography and Ocean Sciences, Yanbian University, Yanji 133002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1126\/science.1184984","article-title":"Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate","volume":"329","author":"Beer","year":"2010","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1038\/ngeo328","article-title":"Carbon cycle: Fertilizing change","volume":"1","author":"Bonan","year":"2008","journal-title":"Nat. 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