文献类型：期刊文献

英文题名：Aboveground Biomass Retrieval in Tropical and Boreal Forests Using L-Band Airborne Polarimetric Observations

作者：Wang, Mengjin[1] Zhang, Wangfei[1] Ji, Yongjie[2] Marino, Armando[3] Xu, Kunpeng[4] Zhao, Lei[4] Shi, Jianmin[1] Zhao, Han[1]

第一作者：Wang, Mengjin

通信作者：Zhang, WF[1]

机构：[1]Southwest Forestry Univ, Coll Forestry, Kunming 650224, Peoples R China;[2]Southwest Forestry Univ, Sch Geog & Ecotourism, Kunming 650224, Peoples R China;[3]Univ Stirling, Biol & Environm Sci, Stirling FK9 4LA, England;[4]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2023

卷号：14

期号：5

外文期刊名：FORESTS

收录：;EI(收录号：20232314180836);Scopus(收录号：2-s2.0-85160732776);WOS:【SCI-EXPANDED(收录号:WOS:000997252000001)】；

基金：The research was supported by National Natural Science Foundation of China (Grant No. 32160365, 42161059 and 31860240) and the Agriculture joint special project of Yunnan province (Grant No. 202301BD070001-058).

语种：英文

外文关键词：forest AGB; polarimetric SAR observations; L-band; parametric and nonparametric feature optimization inversion methods

摘要：Forests 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 (R-2 = 0.80, RMSE = 22.55 Mg/ha, rRMSE = 14.59%, MA%E = 12.21%) and boreal (R-2 = 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.