Afficher la notice abrégée

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

hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorCHANG, Zhongbing
hal.structure.identifierSouthwest University [Chongqing]
dc.contributor.authorFAN, Lei
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorJ.-P., Wigneron
hal.structure.identifierCSIRO Atmospheric Research
dc.contributor.authorWANG, Ying-Ping
hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
dc.contributor.authorCIAIS, Philippe
hal.structure.identifierEvolution et Diversité Biologique [EDB]
dc.contributor.authorCHAVE, Jérôme
hal.structure.identifierIT University of Copenhagen [ITU]
dc.contributor.authorFENSHOLT, Rasmus
hal.structure.identifierUniversity of Toronto
dc.contributor.authorCHEN, Jing M.
hal.structure.identifierSun Yat-sen University [Guangzhou] [SYSU]
dc.contributor.authorYUAN, Wenping
hal.structure.identifierNanjing University [NJU]
dc.contributor.authorJU, Weimin
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorLI, Xin
hal.structure.identifierNanjing University [NJU]
dc.contributor.authorJIANG, Fei
hal.structure.identifierNanjing University [NJU]
dc.contributor.authorWU, Mousong
hal.structure.identifierSun Yat-sen University [Guangzhou] [SYSU]
dc.contributor.authorCHEN, Xiuzhi
hal.structure.identifierUniversity of Oklahoma [OU]
dc.contributor.authorQIN, Yuanwei
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
hal.structure.identifierLaboratoire d'études en Géophysique et océanographie spatiales [LEGOS]
dc.contributor.authorFRAPPART, Frédéric
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLI, Xiaojun
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWANG, Mengjia
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLIU, Xiangzhuo
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorTANG, Xuli
hal.structure.identifierUniversity of New South Wales [Sydney] [UNSW]
dc.contributor.authorHOBEICHI, Sanaa
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorYU, Mengxiao
hal.structure.identifierSouthwest University [Chongqing]
dc.contributor.authorMA, Mingguo
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorXIAO, Qing
dc.contributor.authorWEN, Jianguang
dc.contributor.authorSHI, Weiyu
dc.contributor.authorLIU, Dexin
dc.contributor.authorYAN, Junhua
dc.date.accessioned2024-04-08T11:44:48Z
dc.date.available2024-04-08T11:44:48Z
dc.date.issued2023
dc.identifier.issn2097-0064
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195169
dc.description.abstractEnOver 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<SUP>2</SUP> = 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<SUP>−1</SUP> from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year<SUP>−1</SUP>), Sichuan (+14.49 Tg C year<SUP>−1</SUP>), and Hunan (+11.42 Tg C year<SUP>−1</SUP>) 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.
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.enEstimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019
dc.typeArticle de revue
dc.identifier.doi10.34133/remotesensing.0005
dc.subject.halPlanète et Univers [physics]
bordeaux.journalJournal of remote sensing
bordeaux.volume3
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierinsu-03993153
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//insu-03993153v1
bordeaux.COinSctx_ver=Z39.88-2004&amp;rft_val_fmt=info:ofi/fmt:kev:mtx:journal&amp;rft.jtitle=Journal%20of%20remote%20sensing&amp;rft.date=2023&amp;rft.volume=3&amp;rft.eissn=2097-0064&amp;rft.issn=2097-0064&amp;rft.au=CHANG,%20Zhongbing&amp;FAN,%20Lei&amp;J.-P.,%20Wigneron&amp;WANG,%20Ying-Ping&amp;CIAIS,%20Philippe&amp;rft.genre=article


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée