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Original Articles

Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

, , &
Pages 5551-5568 | Received 26 Apr 2012, Accepted 11 Jan 2013, Published online: 08 May 2013
 

Abstract

Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.

Acknowledgements

Resolution-enhanced SeaWinds backscatter data were obtained from the NASA Scatterometer Climate Record Pathfinder project (http://www.scp.byu.edu). The daily temperature grid data were provided by the China Meteorological Administrator. This work was supported by the National Natural Science Foundation of China under grant No. 41101393, and the Major International Cooperation and Exchange Project ‘Comparative study on global environmental change using remote sensing technology’ under grant No. 41120114001.

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