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Remote Sensing Technology and Application  2013, Vol. 28 Issue (1): 90-96    DOI: 10.11873/j.issn.1004-0323.2013.1.90
    
MODIS NDVI Time-series Data Reconstruction Integrating with the Quality Assessment Science Data Set(QA-SDS)
Fan Hui1,2
(1.Asian International Rivers Center of Yunnan University,Kunming 650091,China;
2.Yunnan Key Laboratory of International Rivers and Transboundary Eco-security,Kunming 650091,China)
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Abstract  

Satellite-derived NDVI time series are often contaminated by negative atmospheric conditions and sunsensor-surface viewing geometries.The reconstruction of high quality NDVI time-series is crucial to the detection of long-term vegetation cover changes and the remote sensing of vegetation phenology.In this paper,MOD13Q1 time-series data covered in Yunnan province were employed to address the performance effectiveness of time-series data reconstruction methods (e.g.linear interpolation,Savitzky-Golay filtering,asymmetric Gaussian and double logistic function-fitting) through integrating with different quality setting (e.g.UI5,UI5-CSS,UI3,UI3-CSS).The results show that seasonal and regional variations in the number and the maximum gap length of invalid pixels of time-series data were mainly controlled by local climate.A comparison of four selected methods revealed that the superiority of the robustness and fitting capability of asymmetric Gaussian and double logistic function-fitting methods over the other fitting techniques.The maximum gap length of invalid pixels in time-series data is an important data quality indicator reflecting the feasibility for meaningful reconstruction.Concentrated clouds and precipitation in the rainy season is a crucial factor of influencing the fitting accuracy of the reconstructed time-series data in some parts of the study area.The reconstructed NDVI time-series data show that the NDVI values are higher in the rainy season than those in the dry season,higher in the western than those in the eastern,higher in the southern than those in the northern, and higher in the river valley than those in the uplands in the study area.

Key words:  MODIS NDVI      Time-series analysis      Data quality evaluation      Yunnan province     
Received:  30 December 2011      Published:  21 June 2013
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Cite this article: 

Fan Hui. MODIS NDVI Time-series Data Reconstruction Integrating with the Quality Assessment Science Data Set(QA-SDS). Remote Sensing Technology and Application, 2013, 28(1): 90-96.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2013.1.90     OR     http://www.rsta.ac.cn/EN/Y2013/V28/I1/90

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