作者:Yu, T (Yu, Tao)[ 1,2,3 ] ; Sun, R (Sun, Rui)[ 1,2,3 ] ; Xiao, ZQ (Xiao, Zhiqiang)[ 1,2,3 ] ; Zhang, Q (Zhang, Qiang)[ 1,2,3 ] ; Liu, G (Liu, Gang)[ 1,2,3 ] ; Cui, TX (Cui, Tianxiang)[ 1,2,3 ] ; Wang, JM (Wang, Juanmin)[ 1,2,3 ]

REMOTE SENSING

卷: 10  期: 2

文献号: 327

DOI: 10.3390/rs10020327

出版年: FEB 2018

文献类型:Article

摘要

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.

通讯作者地址: Sun, R; Xiao, ZQ (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Sun, R; Xiao, ZQ (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China.

通讯作者地址: Sun, R; Xiao, ZQ (通讯作者)

Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China