归一化差值植被指数NDVI(Normalized Difference Vegetation Index)时序数据已被广泛应用于植被变化监测、植被物候识别和土地覆盖分类等领域,但受观测条件限制,NDVI原始数据中包含大量噪声,在实际应用时需对其进行检测并去除。目前常用的NDVI数据去噪重建方法主要包括阈值检测法、滤波拟合法及曲线拟合法3类。各方法在应用时均需根据不同的土地覆盖类型或特定的研究区域设置一定数量的经验参数,对噪声的定义缺乏客观标准;此外,这3类方法都没有进行专门的噪声检测,在进行NDVI数据重建时只是根据经验进行噪声判断。本文提出了一种基于狄克松(Dixon)检验法、适用于对小样本进行检测的数理统计噪声检测方法,该方法首先对同一像元、同一时段、不同年份的NDVI时序数据进行统计分析,然后再结合质量评估数据的分析结果,最终给出NDVI是否异常的判断。运用狄克松检验法对噪声进行检测,然后结合已有的两种数据重建方法--变权重滤波法和Savitzky-Golay方法,基于2001年-2010年250 m分辨率的MODIS NDVI时序数据,对覆盖中国55种植被类型共520个测试样点及洞庭湖测试区域进行了NDVI时序数据重建实验,结果表明,狄克松检验法降低了对先验知识的依赖程度,应用该方法对NDVI时序数据中的噪声进行检测预处理后,可以有效提高变权重和Savitzky-Golay方法的数据重建质量。

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 observing 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.