Normalized Difference Vegetation Index（ NDVI） time series data are widely used to detect vegetation changes

identify vegetation phenology

and classify land cover. However

original NDVI data contain a great amount of noise that results from o bserving conditions. Therefore

noise should be detected and removed in practical applications. Generally

methods to remove noise and reconstruct high-quality NDVI time series data sets can be grouped into three types: threshold detection

filter

and curve fitting. Each method presets a certain number of parameters according to different land cover types or a specific study area

resulting in a lack of objective criteria to define noise. These three methods do not include noise detection when reconstructing NDVI data; thus

noise is removed based only on experience. In this paper

a noise detection method based on Dixon’s test is presented. The proposed method is suitable for a small sample. Through this method

we analyzed the statistical characteristics of NDVI data from the same period of different years for a given pixel. The outlier in the NDVI time series was then determined based on quality assessment data. The noise detection method was applied to two existing data reconstruction methods（ i. e.

changing weight filter and Savitzky-Golay filter methods） to reconstruct the NDVI data over 520 test pixels of 55 vegetation types and a region in Dongting Lake in China from 2001 to 2010. Dixon’s test reduces the dependence on a priori knowledge for the data reconstruction methods

and data quality can be improved effectively through the proposed noise detection method.