Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America

	Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America
	Liu, Yuxia 1; Wu, Chaoyang 1; Peng, Dailiang 1; Xu, Shiguang 1; Gonsamo, Alemu 1; Jassal, Rachhpal S.1; Altaf Arain, M.1; Lu, Linlin 1; Fang, Bin 1; Chen, Jing M.1
刊名	Remote Sensing of Environment
	2016
卷号	176 页码:152-162
关键词	BROAD-BAND ALBEDO LEAF-AREA INDEX REFLECTANCE DISTRIBUTION FUNCTION REMOTE-SENSING DATA BIDIRECTIONAL REFLECTANCE ATMOSPHERIC CORRECTION GROUND MEASUREMENTS ALGORITHM RETRIEVALS GRASSLAND
通讯作者	Wu, Chaoyang (hefery@163.com)
英文摘要	Plant phenology plays a significant role in regulating carbon sequestration period of terrestrial ecosystems. Remote sensing of land surface phenology (LSP), i.e., the start and the end of the growing season (SOS and EOS, respectively) in evergreen needleleaf forests is particularly challenging due to their limited seasonal variability in canopy greenness. Using 107 site-years of CO2flux data at 14 evergreen needleleaf forest sites in North America, we developed a new model to estimate SOS and EOS based entirely on the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We found that the commonly used vegetation indices (VI), including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), were not able to detect SOS and EOS in these forests. The MODIS land surface temperature (LST) showed better performance in the estimation of SOS than did a single VI. Interestingly, the variability of LST (i.e., the coefficient of variation, CV_LST) was more useful than LST itself in detecting changes in forest LSP. Therefore, a new model using the product of VI and CV_LST was developed and it significantly improved the representation of LSP with mean errors of 11.7 and 5.6days for SOS and EOS, respectively. Further validation at five sites in the Long Term Ecological Research network (LTER) using camera data also indicated the applicability of the new approach. These results suggest that temperature variability plays a previously overlooked role in phenological modeling, and a combination of canopy greenness and temperature could be a useful way to enhance the estimation of evergreen needleleaf forest phenology of future ecosystem models. © 2016 Elsevier Inc.
学科主题	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20160501875894
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39154]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. University of the Chinese Academy of Sciences, Beijing, China 3. Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 4. Department of Geography and Program in Planning, University of Toronto, 100 St. George St., Toronto 5.ON, Canada 6. Faculty of Land and Food Systems, University of British Columbia, Vancouver 7.BC, Canada 8. School of Geography and Earth Sciences, McMaster Centre for Climate Change, McMaster University, Hamilton 9.ON, Canada 10. Department of Earth and Environmental Engineering, Columbia University, 500 W 120th St., New York
推荐引用方式 GB/T 7714	Liu, Yuxia,Wu, Chaoyang,Peng, Dailiang,et al. Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America[J]. Remote Sensing of Environment,2016,176:152-162.
APA	Liu, Yuxia.,Wu, Chaoyang.,Peng, Dailiang.,Xu, Shiguang.,Gonsamo, Alemu.,...&Chen, Jing M..(2016).Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America.Remote Sensing of Environment,176,152-162.
MLA	Liu, Yuxia,et al."Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America".Remote Sensing of Environment 176(2016):152-162.