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

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Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019. / Chang, Zhongbing; Fan, Lei; Wigneron, Jean-Pierre; Wang, Ying-Ping; Ciais, Philippe; Chave, Jérôme; Fensholt, Rasmus; Chen, Jing M.; Yuan, Wenping; Ju, Weimin; Li, Xin; Jiang, Fei; Wu, Mousong; Chen, Xiuzhi; Qin, Yuanwei; Frappart, Frédéric; Li, Xiaojun; Wang, Mengjia; Liu, Xiangzhuo; Tang, Xuli; Hobeichi, Sanaa; Yu, Mengxiao; Ma, Mingguo; Wen, Jianguang; Xiao, Qing; Shi, Weiyu; Liu, Dexin; Yan, Junhua.

I: Journal of Remote Sensing (United States), Bind 3, 0005, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Chang, Z, Fan, L, Wigneron, J-P, Wang, Y-P, Ciais, P, Chave, J, Fensholt, R, Chen, JM, Yuan, W, Ju, W, Li, X, Jiang, F, Wu, M, Chen, X, Qin, Y, Frappart, F, Li, X, Wang, M, Liu, X, Tang, X, Hobeichi, S, Yu, M, Ma, M, Wen, J, Xiao, Q, Shi, W, Liu, D & Yan, J 2023, 'Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019', Journal of Remote Sensing (United States), bind 3, 0005. https://doi.org/10.34133/remotesensing.0005

APA

Chang, Z., Fan, L., Wigneron, J-P., Wang, Y-P., Ciais, P., Chave, J., Fensholt, R., Chen, J. M., Yuan, W., Ju, W., Li, X., Jiang, F., Wu, M., Chen, X., Qin, Y., Frappart, F., Li, X., Wang, M., Liu, X., ... Yan, J. (2023). Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019. Journal of Remote Sensing (United States), 3, [0005]. https://doi.org/10.34133/remotesensing.0005

Vancouver

Chang Z, Fan L, Wigneron J-P, Wang Y-P, Ciais P, Chave J o.a. Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019. Journal of Remote Sensing (United States). 2023;3. 0005. https://doi.org/10.34133/remotesensing.0005

Author

Chang, Zhongbing ; Fan, Lei ; Wigneron, Jean-Pierre ; Wang, Ying-Ping ; Ciais, Philippe ; Chave, Jérôme ; Fensholt, Rasmus ; Chen, Jing M. ; Yuan, Wenping ; Ju, Weimin ; Li, Xin ; Jiang, Fei ; Wu, Mousong ; Chen, Xiuzhi ; Qin, Yuanwei ; Frappart, Frédéric ; Li, Xiaojun ; Wang, Mengjia ; Liu, Xiangzhuo ; Tang, Xuli ; Hobeichi, Sanaa ; Yu, Mengxiao ; Ma, Mingguo ; Wen, Jianguang ; Xiao, Qing ; Shi, Weiyu ; Liu, Dexin ; Yan, Junhua. / Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019. I: Journal of Remote Sensing (United States). 2023 ; Bind 3.

Bibtex

@article{ba7bcb48aabd4bda93bd4d2fb6828d6b,

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

abstract = "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 R2 = 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.",

author = "Zhongbing Chang and Lei Fan and Jean-Pierre Wigneron and Ying-Ping Wang and Philippe Ciais and J{\'e}r{\^o}me Chave and Rasmus Fensholt and Chen, {Jing M.} and Wenping Yuan and Weimin Ju and Xin Li and Fei Jiang and Mousong Wu and Xiuzhi Chen and Yuanwei Qin and Fr{\'e}d{\'e}ric Frappart and Xiaojun Li and Mengjia Wang and Xiangzhuo Liu and Xuli Tang and Sanaa Hobeichi and Mengxiao Yu and Mingguo Ma and Jianguang Wen and Qing Xiao and Weiyu Shi and Dexin Liu and Junhua Yan",

note = "Publisher Copyright: {\textcopyright} 2023 Zhongbing Chang et al. Exclusive Licensee Aerospace Information Research Institute, Chinese Academy of Sciences.",

year = "2023",

doi = "10.34133/remotesensing.0005",

language = "English",

volume = "3",

journal = "Journal of Remote Sensing (United States)",

issn = "2097-0064",

publisher = "American Association for the Advancement of Science",

}

RIS

TY - JOUR

T1 - Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019

AU - Chang, Zhongbing

AU - Fan, Lei

AU - Wigneron, Jean-Pierre

AU - Wang, Ying-Ping

AU - Ciais, Philippe

AU - Chave, Jérôme

AU - Fensholt, Rasmus

AU - Chen, Jing M.

AU - Yuan, Wenping

AU - Ju, Weimin

AU - Li, Xin

AU - Jiang, Fei

AU - Wu, Mousong

AU - Chen, Xiuzhi

AU - Qin, Yuanwei

AU - Frappart, Frédéric

AU - Li, Xiaojun

AU - Wang, Mengjia

AU - Liu, Xiangzhuo

AU - Tang, Xuli

AU - Hobeichi, Sanaa

AU - Yu, Mengxiao

AU - Ma, Mingguo

AU - Wen, Jianguang

AU - Xiao, Qing

AU - Shi, Weiyu

AU - Liu, Dexin

AU - Yan, Junhua

PY - 2023

Y1 - 2023

N2 - 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 R2 = 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.

AB - 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 R2 = 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.

U2 - 10.34133/remotesensing.0005

DO - 10.34133/remotesensing.0005

M3 - Journal article

AN - SCOPUS:85152145188

VL - 3

JO - Journal of Remote Sensing (United States)

JF - Journal of Remote Sensing (United States)

SN - 2097-0064

M1 - 0005

ER -

ID: 390999365