Abstract
Vegetation phenology tracks plants’ lifecycle events and reveals the response of vegetation to global climate change. Microwave backscatter is insensitive to signal degradation from solar illumination and atmospheric effects and thus provides a useful tool for phenology monitoring. In this chapter, we analyzed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine its effectiveness for land surface phenology monitoring across eastern Asia. The spatial pattern of annual mean backscatter follows regional vegetation type distributions. The Start Of Season (SOS) and End Of Season (EOS) were derived from the backscatter time series and compared with MODIS (Moderate Resolution Imaging Spectroradiometer) phenology products from 2003 to 2007. The failure of phenology metric detection for backscatter time series is caused by snow coverage and limited vegetation activity in arid areas. For tropical and semi-arid areas where optical observation is unavailable, backscatter data can provide valid phenological information. Due to their sensitivity to different factors, temporal discrepancies were observed between phenology products from backscatter and MODIS time series. Overall, the results indicate that SeaWinds backscatter provides an alternative view of vegetation phenology that is independent of optical sensors and can be applied to global phenology studies.
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Acknowledgments
The resolution-enhanced SeaWinds backscatter data were obtained from the NASA Scatterometer Climate Record Pathfinder project (http://www.scp.byu.edu). This work was supported by the National Natural Science Foundation of China under grant No. 41471369 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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Lu, L., Guo, H., Wang, C. (2015). Land Surface Phenology Monitoring with SeaWinds Scatterometer Time Series in Eastern Asia. In: Kuenzer, C., Dech, S., Wagner, W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-15967-6_18
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DOI: https://doi.org/10.1007/978-3-319-15967-6_18
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