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Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
by
Tao Yu 1,2, Rui Sun 1,2,*
, Zhiqiang Xiao 1,2,*, Qiang Zhang 1,2, Gang Liu 1,2, Tianxiang Cui 1,2 and Juanmin Wang 1,2
, Zhiqiang Xiao 1,2,*, Qiang Zhang 1,2, Gang Liu 1,2, Tianxiang Cui 1,2 and Juanmin Wang 1,2
1
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 327; https://doi.org/10.3390/rs10020327
Received: 28 December 2017 / Revised: 4 February 2018 / Accepted: 18 February 2018 / Published: 22 February 2018
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li)
Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP) and annual net primary production (NPP) are contained in MODerate Resolution Imaging Spectroradiometer (MODIS) products (MOD17), which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS) LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.
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Keywords:
GPP; NPP; MODIS; validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Yu, T.; Sun, R.; Xiao, Z.; Zhang, Q.; Liu, G.; Cui, T.; Wang, J. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. Remote Sens. 2018, 10, 327.
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