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dc.contributor.authorWang, Mengjinen_UK
dc.contributor.authorZhang, Wangfeien_UK
dc.contributor.authorJi, Yongjieen_UK
dc.contributor.authorMarino, Armandoen_UK
dc.contributor.authorXu, Kunpengen_UK
dc.contributor.authorZhao, Leien_UK
dc.contributor.authorShi, Jianminen_UK
dc.contributor.authorZhao, Hanen_UK
dc.description.abstractForests play a crucial part in regulating global climate change since their aboveground biomass (AGB) relates to the carbon cycle, and its changes affect the main carbon pools. At present, the most suitable available SAR data for wall-to-wall forest AGB estimation are exploiting an L-band polarimetric SAR. However, the saturation issues were reported for AGB estimation using L-band backscatter coefficients. Saturation varies depending on forest structure. Polarimetric information has the capability to identify different aspects of forest structure and therefore shows great potential for reducing saturation issues and improving estimation accuracy. In this study, 121 polarimetric decomposition observations, 10 polarimetric backscatter coefficients and their derived observations, and six texture features were extracted and applied for forest AGB estimation in a tropical forest and a boreal forest. A parametric feature optimization inversion model (Multiple linear stepwise regression, MSLR) and a nonparametric feature optimization inversion model (fast iterative procedure integrated into a K-nearest neighbor nonparameter algorithm, KNNFIFS) were used for polarimetric features optimization and forest AGB inversion. The results demonstrated the great potential of L-band polarimetric features for forest AGB estimation. KNNFIFS performed better both in tropical (R2 = 0.80, RMSE = 22.55 Mg/ha, rRMSE = 14.59%, MA%E = 12.21%) and boreal (R2 = 0.74, RMSE = 19.82 Mg/ha, rRMSE = 20.86%, MA%E = 20.19%) forests. Non-model-based polarimetric features performed better compared to features extracted by backscatter coefficients, model-based decompositions, and texture. Polarimetric observations also revealed site-dependent performances.en_UK
dc.publisherMDPI AGen_UK
dc.relationWang M, Zhang W, Ji Y, Marino A, Xu K, Zhao L, Shi J & Zhao H (2023) Aboveground Biomass Retrieval in Tropical and Boreal Forests Using L-Band Airborne Polarimetric Observations. <i>Forests</i>, 14 (5), p. 887.
dc.rightsCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
dc.titleAboveground Biomass Retrieval in Tropical and Boreal Forests Using L-Band Airborne Polarimetric Observationsen_UK
dc.typeJournal Articleen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderNational Natural Science Foundation of Chinaen_UK
dc.contributor.funderNational Natural Science Foundation of Chinaen_UK
dc.contributor.funderNational Natural Science Foundation of Chinaen_UK
dc.contributor.funderAgriculture joint special project of Yunnan provinceen_UK
dc.contributor.affiliationSouthwest Forestry Universityen_UK
dc.contributor.affiliationSouthwest Forestry Universityen_UK
dc.contributor.affiliationSouthwest Forestry Universityen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationChinese Academy of Forestryen_UK
dc.contributor.affiliationChinese Academy of Forestryen_UK
dc.contributor.affiliationSouthwest Forestry Universityen_UK
dc.contributor.affiliationSouthwest Forestry Universityen_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorWang, Mengjin|en_UK
local.rioxx.authorZhang, Wangfei|0000-0002-2147-5246en_UK
local.rioxx.authorJi, Yongjie|0000-0001-8012-4115en_UK
local.rioxx.authorMarino, Armando|0000-0002-4531-3102en_UK
local.rioxx.authorXu, Kunpeng|en_UK
local.rioxx.authorZhao, Lei|en_UK
local.rioxx.authorShi, Jianmin|en_UK
local.rioxx.authorZhao, Han|en_UK
local.rioxx.project42161059|National Natural Science Foundation of China|en_UK
local.rioxx.project32160365|National Natural Science Foundation of China|en_UK
local.rioxx.project31860240|National Natural Science Foundation of China|en_UK
local.rioxx.project202301BD070001-058|Agriculture joint special project of Yunnan province|en_UK
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