Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019  

作　　者：Zhongbing Chang Lei Fan Jean-Pierre Wigneron Ying-Ping Wang Philippe Ciais Jérôme Chave Rasmus Fensholt Jing MChen Wenping Yuan Weimin Ju Xin Li Fei Jiang Mousong Wu Xiuzhi Chen Yuanwei Qin Frédéric Frappart Xiaojun Li Mengjia Wang Xiangzhuo Liu Xuli Tang Sanaa Hobeichi Mengxiao Yu Mingguo Ma Jianguang Wen Qing Xiao Weiyu Shi Dexin Liu Junhua Yan 

机构地区：[1]Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems,South China Botanical Garden,Chinese Academy of Sciences,Guangzhou 510650,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station,School of Geographical Sciences,Southwest University,Chongqing 400715,China [4]INRAE,UMR1391 ISPA,Universite de Bordeaux,F-33140 Villenave d’Ornon,France [5]CSIRO Oceans and Atmosphere,Aspendale,VIC 3195,Australia [6]Laboratoire des Sciences du Climat et de l’Environnement,CEA/CNRS/UVSQ/Universite Paris Saclay,Gif-sur-Yvette,France [7]Laboratoire Evolution et Diversite Biologique,Universite Paul Sabatier,Toulouse,France [8]Department of Geosciences and Natural Resource Management,University of Copenhagen,Copenhagen,Denmark [9]Department of Geography and Program in Planning,University of Toronto,Toronto,ON M5S 3G3,Canada [10]College of Geographical Science,Fujian Normal University,Fuzhou 3500007,Fujian,China [11]School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai 519082,Guangdong,China [12]Southern Marine Science and Engineering Guangdong Laboratory,Zhuhai 519000,Guangdong,China [13]Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,International Institute for Earth System Science,Nanjing University,Nanjing 210023,China [14]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China [15]Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing 100101,China [16]CAS Center for Excellence in Tibetan Plateau Earth Sciences,Chinese Academy of Sciences,Beijing 100101,China [17]Department of Microbiology and Plant Biology,Center for Spatial Analysis,University of Oklahoma,Norman,OK,USA [18]LEGOS,Universite de Toulouse,CNES,CNRS,IRD,UPS-14 avenue Edouard Belin,31400 Toulouse,France [19]State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal Univers

出　　处：《Journal of Remote Sensing》2023年第1期19-34,共16页国际遥感学报（英文）

基　　金：supported by the National Science Fund for Distinguished Young Scholars(41825020);the National Natural Science Foundation of China(42171339);the Postdoctoral Start-Up Project of Southwest University(SWU020016);the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA05050200).

摘　　要：Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among studies.Using 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and 2019.Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other VODs.An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019.The performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to 2019.At the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to 2019.Most of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

关 键 词：estimation REMOTE MICROWAVE 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]