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Big Earth Data Volume 4, 2020 - Issue 2: Understanding climate change using big Earth data
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A global terrestrial ecosystem respiration dataset (2001-2010) estimated with MODIS land surface temperature and vegetation indices

Jinlong Aia Department of Biology and Information Engineering, Yiyang Vocational & Technical College, Yiyang, ChinaORCID Iconhttps://orcid.org/0000-0001-8342-4068View further author information
ORCID Icon,
Shuyuan Xiaoa Department of Biology and Information Engineering, Yiyang Vocational & Technical College, Yiyang, ChinaView further author information
,
Hui Fenga Department of Biology and Information Engineering, Yiyang Vocational & Technical College, Yiyang, ChinaView further author information
,
Huan Wanga Department of Biology and Information Engineering, Yiyang Vocational & Technical College, Yiyang, ChinaView further author information
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Gensuo Jiab Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
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Yonghong Huc Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaView further author information
Pages 142-152 | Received 05 Feb 2020, Accepted 22 Apr 2020, Published online: 23 Jun 2020

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Gensuo Jia. (2020) The role of big Earth data in understanding climate change. Big Earth Data 4:2, pages 91-93.
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Torbern Tagesson, Julia Kelly, Guy Schurgers, Feng Tian, Jonas Ardö, Stephanie Horion, Anders Ahlström, Stefan Olin & Rasmus Fensholt. (2023) Increasing global ecosystem respiration between 1982 and 2015 from Earth observation‐based modelling. Global Ecology and Biogeography.
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Wei Shangguan, Zili Xiong, Vahid Nourani, Qingliang Li, Xingjie Lu, Lu Li, Feini Huang, Ye Zhang, Wenye Sun & Yongjiu Dai. (2023) A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning. Forests 14:5, pages 913.
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Lang Han, Gui‐Rui Yu, Zhi Chen, Xian‐Jin Zhu, Wei‐Kang Zhang, Tie‐Jun Wang, Li Xu, Shi‐Ping Chen, Shao‐Min Liu, Hui‐Min Wang, Jun‐Hua Yan, Jun‐Lei Tan, Fa‐Wei Zhang, Feng‐Hua Zhao, Ying‐Nian Li, Yi‐Ping Zhang, Li‐Qing Sha, Qing‐Hai Song, Pei‐Li Shi, Jiao‐Jun Zhu, Jia‐Bing Wu, Zhong‐Hui Zhao, Yan‐Bin Hao, Xi‐Bin Ji, Liang Zhao, Yu‐Cui Zhang, Shi‐Cheng Jiang, Feng‐Xue Gu, Zhi‐Xiang Wu, Yang‐Jian Zhang, Li Zhou, Ya‐Kun Tang, Bing‐Rui Jia, Gang Dong, Yan‐Hong Gao, Zheng‐De Jiang, Dan Sun, Jian‐Lin Wang, Qi‐Hua He, Xin‐Hu Li, Fei Wang, Wen‐Xue Wei, Zheng‐Miao Deng, Xiang‐Xiang Hao, Xiao‐Li Liu, Xi‐Feng Zhang, Xing‐Guo Mo, Yong‐Tao He, Xin‐Wei Liu, Hu Du & Zhi‐Lin Zhu. (2022) Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding. Global Biogeochemical Cycles 36:11.
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Xiao‐Ming Hu, Sharon M. Gourdji, Kenneth J. Davis, Qingyu Wang, Yao Zhang, Ming Xue, Sha Feng, Berrien Moore & Sean M. R. Crowell. (2021) Implementation of Improved Parameterization of Terrestrial Flux in WRF‐VPRM Improves the Simulation of Nighttime CO 2 Peaks and a Daytime CO 2 Band Ahead of a Cold Front . Journal of Geophysical Research: Atmospheres 126:10.
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