Research on the Reconstructing of Time-series NDVI Data

Abstract

Although the Normalized Difference Vegetation Index(NDVI) time-series data derived from NOAA/AVHRR,SPOT/VEGETATION and MODIS,has been successfully used in research regarding global vegetation change,land cover classification and biophysical parameters inversion.However,due to effect of cloud and atmospheric conditions,residual noise in the NDVI time-series data will induce erroneous results in our further quantitive analysis.In this paper,some general reconstructing methods are introduced,including Maximum Value Compositing(MVC),the Best Index Slope Extraction(BISE),Media Iteration Filter(MIF),Temporal Window Operation(TWO),Fourier Transform(FT) and Savitzky-Golay Filter(S-G Filter).With the development of change detection research,it is necessary to reconstruct the NDVI time-series data sets in order to provide high-quality data for the study of vegetation response to global climate change.