Evaluation of remote sensing extraction methods for vegetation phenology based on flux tower net ecosystem carbon exchange data

Taking the vegetation phenological metrics derived from the net ecosystem carbon exchange (NEE) data of 72 flux towers in North America as the references, a comprehensive evaluation was conducted on the three typical classes of remote sensing extraction methods (threshold method, moving average meth...

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Veröffentlicht in:Ying yong sheng tai xue bao 2012-02, Vol.23 (2), p.319-327
Hauptverfasser: Mou, Min-Jie, Zhu, Wen-Quan, Wang, Ling-Li, Xu, Ying-Jun, Liu, Jian-Hong
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container_title Ying yong sheng tai xue bao
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creator Mou, Min-Jie
Zhu, Wen-Quan
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description Taking the vegetation phenological metrics derived from the net ecosystem carbon exchange (NEE) data of 72 flux towers in North America as the references, a comprehensive evaluation was conducted on the three typical classes of remote sensing extraction methods (threshold method, moving average method, and function fitting method) for vegetation phenology from the aspects of feasibility and accuracy. The results showed that the local midpoint threshold method had the highest feasibility and accuracy for extracting vegetation phenology, followed by the first derivative method based on fitted Logistic function. The feasibility and accuracy of moving average method were determined by the moving window size. As for the MODJS 16 d composited time-series normalized difference vegetation index (NDVI), the moving average method had preferable performance when the window size was set as 15. The global threshold method performed quite poor in the feasibility and accuracy. Though the values of the phenological metrics e
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subjects Carbon - metabolism
Carbon Cycle
Climate
Conservation of Natural Resources
Data Collection
Ecosystem
Environmental Monitoring - methods
Plant Development
Plants - metabolism
Remote Sensing Technology
title Evaluation of remote sensing extraction methods for vegetation phenology based on flux tower net ecosystem carbon exchange data
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