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Target Article
Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models
Xingguang Yana College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of China;b School of Natural Sciences, Bangor University, Bangor, UK;c Environment Centre Wales, Bangor University, Bangor, UKhttps://orcid.org/0009-0001-8280-4568View further author information
, Jing Lia College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaCorrespondence[email protected]
https://orcid.org/0000-0001-8095-0425View further author information
, https://orcid.org/0000-0001-8095-0425View further author information
Andrew R. Smithb School of Natural Sciences, Bangor University, Bangor, UK;c Environment Centre Wales, Bangor University, Bangor, UKhttps://orcid.org/0000-0001-8580-278XView further author information
, Di Yangd Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY, USAhttps://orcid.org/0000-0002-4010-6163View further author information
, Tianyue Maa College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaView further author information
, YiTing Sua College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaView further author information
& Jiahao Shaoa College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, People’s Republic of ChinaView further author information
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Pages 4471-4491
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Received 12 Jun 2023, Accepted 09 Oct 2023, Published online: 01 Nov 2023
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