Integrated Reconstruction Methods of Time series NDVI Dataset
Time series of Normalized Difference Vegetation Index(NDVI) datasets have been used in detecting the long term vegetation cover changes in regional,continental or global scales.They are also successfully applied to extract the biophysical parameters of vegetation cover.Normally,there are quite frequently fluctuations because of atmospheric condition and sensor effect in the NDVI dataset.According to the comparative analysis of five widely used NDVI reconstruction algorithms,two integrated approaches were developed based on standard deviation weight and characteristics of noise points respectively.The reconstructed results were validated and assessed by using some in suit NDVI measurements carried out during late May to early August,2009.The result shows that these two integrated methods are better than the five separate methods above.They do not only retain most of the original data,but also modify the noise to the utmost extent.NDVI time series datasets produced by these two approaches can be better applied in the researches on global and regional environmental change,vegetation dynamic,and so on.