Estimation of Marine Primary Productivity From Satellite-Derived Phytoplankton Absorption Data
作者:Ma, S (Ma, Sheng)[ 1 ] ; Tao, Z (Tao, Zui)[ 1 ] ; Yang, XF (Yang, Xiaofeng)[ 1 ] ; Yu, Y (Yu, Yang)[ 1 ] ; Zhou, X (Zhou, Xuan)[ 1 ] ; Ma, WT (Ma, Wentao)[ 1,2 ] ; Li, ZW (Li, Ziwei)[ 1 ]
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷: 7  期: 7  页: 3084-3092  特刊: SI
DOI: 10.1109/JSTARS.2014.2298863
出版年: JUL 2014

摘要
The global ocean net primary production (NPP) was estimated from satellite-derived information using a phytoplankton pigment absorption (alpha(ph))-based model. Satellite-derived spectral-averaged alpha(ph) was used as the key predictor of phytoplankton photosynthetic efficiency in the model. The alpha(ph)-based model yields an annual integrated NPP of approximately 55 Pg C year(-1) for the global oceans over the period 2003-2010. The accuracy of the model was validated by comparing it with in situ NPP at three sites, and the logarithmic root-mean-square error of the model was approximately 0.18. The model performance was also compared with two existing NPP models (chlorophyll-based model and carbon-based model) in terms of spatial distribution, seasonal cycles, and accuracy. The comparison results indicated that the alpha(ph)-based model has improved accuracy in describing NPP variation for monthly timescales compared with the other two models. We were surprised to find that the spatial distribution of global ocean NPP provided by the alpha(ph)-based model is more similar to the carbon-based model than the chlorophyll-based model. Although many additional studies need to be conducted, the performance of the alpha(ph)-based model in this work may encourage us to estimate ocean NPP from satellite-derived phytoplankton pigment absorption.
通讯作者地址: Tao, Z (通讯作者)
 Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
 [ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
 [ 2 ] Ocean Univ China, Coll Phys & Environm Oceanog, Qingdao 266100, Peoples R China