吴朝阳等:The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation
被阅读 1724 次
2014-04-04

The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation
作者:Wu, CY (Wu, Chaoyang)[ 1,2 ] ; Gonsamo, A (Gonsamo, Alemu)[ 2 ] ; Zhang, FM (Zhang, Fangmin)[ 3 ] ; Chen, JM (Chen, Jing M.)[ 2 ]
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷: 88  页: 69-79
出版年: FEB 2014

摘要
Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

通讯作者地址: Wu, CY (通讯作者)
Univ Toronto, Dept Geog, 100 St George St, Toronto, ON M5S 3G3, Canada.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada
[ 3 ] Nanjing Univ Informat Sci & Technol, Coll Appl Meteorol, Jiangsu Key Lab Agr Meteorol, Nanjing 210044, Jiangsu, Peoples R China