Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration - Details

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Abstract：

Accurately　assessing　the　carbon　sink　and　spatial　distribution　pattern　of　China＇s　terrestrial　ecosystems　is　of　great　significance　to　the　implementation　of　climate　change　and　carbon　neutrality　strategy.　However,　the　views　of　various　studies　are　still　very　controversial　due　to　the　differences　in　carbon　sink　estimation　methods　and　data　sources.　In　this　study,　vegetation　net　primary　productivity　(NPP)　and　ecosystem　heterotrophic　respiration　(Rh)　estimation　models　were　constructed　based　on　machine　learning　methods　by　fusing　multisource　data,　such　as　remote　sensing　and　ground　observation　data.　The　magnitude　and　spatial　pattern　of　carbon　sink　in　China　from　2000　to　2018　were　then　revealed,　and　the　carbon　sink　capacity　of　various　ecosystems　was　quantitatively　assessed.　The　main　conclusions　include　the　following:　(1)　The　use　of　scale-matched　carbon　input　and　output　data　can　help　reduce　the　system　error　in　carbon　sink　estimation.　(2)　China＇s　terrestrial　ecosystem　carbon　sink　since　the　twenty-first　century　is　approximately　0.458　Pg　C/yr,　which　is　equivalent　to　22.72%　of　China＇s　anthropogenic　carbon　emissions.　(3)　Deciduous　forest　has　a　higher　carbon　sink　capacity　than　evergreen　forest,　while　coniferous　forest　has　a　more　stable　carbon　sink　capacity　than　broad-leaved　forest.　The　magnitude　and　spatial　distribution　of　carbon　sink　in　China　reported　in　this　study　provides　a　scientific　reference　for　achieving　carbon　neutrality　and　sustainable　development.　©　2023　Elsevier　B.V.

Keyword：

Carbon sink China Ecosystem type Machine learning Scale matching Spatial pattern

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[ 1 ] [Zeng J.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 2 ] [Zeng J.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 3 ] [Zhou T.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 4 ] [Zhou T.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 5 ] [Wang Q.]College of Environment & Safety Engineering, Fuzhou University, Fuzhou, 350116, China
[ 6 ] [Xu Y.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 7 ] [Xu Y.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 8 ] [Lin Q.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 9 ] [Lin Q.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 10 ] [Zhang Y.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 11 ] [Zhang Y.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 12 ] [Wu X.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 13 ] [Wu X.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 14 ] [Zhang J.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 15 ] [Zhang J.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
[ 16 ] [Liu X.]Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing, 100875, China
[ 17 ] [Liu X.]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

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Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration
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Source ：

Ecological Informatics

ISSN： 1574-9541

Year： 2023

Volume： 76

5 . 9

JCR@2023

5 . 9 0 0

JCR@2023

ESI HC Threshold：33

JCR Journal Grade：1

CAS Journal Grade：2

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WoS CC Cited Count： 0

SCOPUS Cited Count： 16

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 0

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