Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS
作者:Wu, CY (Wu, Chaoyang)[ 1,2 ] ; Gonsamo, A (Gonsamo, Alemu)[ 2 ] ; Gough, CM (Gough, Christopher M.)[ 3 ] ; Chen, JM (Chen, Jing M.)[ 2 ] ; Xu, SG (Xu, Shiguang)[ 1 ]
REMOTE SENSING OF ENVIRONMENT
卷: 147  页: 79-88
DOI: 10.1016/j.rse.2014.03.001
出版年: MAY 5 2014

摘要
The phenology of vegetation exerts an important control over the terrestrial ecosystem carbon (C) cycle. Remote sensing of key phenological phases in forests (e.g., the spring onset and autumn end of growing season) remains challenging due to noise in time series and the limited seasonal variation of canopy greenness in evergreen forests. Using 94 site-years of C flux data from four deciduous broadleaf forests (DBF) and six evergreen needleleaf forests (ENF) in North America, we examine whether growing season phenology can be remotely sensed from mean vegetation indices (VIs) derived from spring (Apr.-May) and autumn (Sep.-Nov) observations. Five Vls were used based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, including the normalized difference vegetation index (NDVI), the land surface water index (LSWI), the enhanced vegetation index (EVI), the wide dynamic range vegetation index (WDRVI) and the optimized soil-adjusted vegetation index (OSAVI). Our results show that growing season transitions can be inferred from mean seasonal Vls, though the different Vls varied in their predictive strength across sites and plant functional types. Widely used NDVI and EVI exhibited limited potential in tracking growing season phenology of ENF ecosystems, while indices sensitive to water (i.e., LSWI) or less influenced by soil (i.e., OSAVI) may have unrevealed powers in indicating phenological transitions. OSAVI was shown to be a strong predictor of the end of the growing season in ENF ecosystems, suggesting that this VI may offer a new strategy for modeling the phenology of ENF sites. We conclude that combinations of multiple indices may improve the remote sensing of land surface phenology, as evidenced by the good agreement between modeled and observed growing season transitions and its length in our evaluation. (C) 2014 Elsevier Inc All rights reserved.

通讯作者地址: Wu, CY (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 ] Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada
[ 3 ] Virginia Commonwealth Univ, Dept Biol, Richmond, VA 23284 USA