NDVI时间序列数据集重建方法述评

Research on the Reconstructing of Time-series NDVI Data

基于NOAA/AVHRR、SPOT/VEGETAT ION以及MOD IS等卫星影像得到的归一化植被指数(NDV I,N orm alized D ifference V egetation Index)时序资料已经在植被动态变化监测、宏观植被覆盖分类和植物生物物理参数反演方面得到了广泛的应用,但由于受云层、天气等因素的影响,NDV I数据集存在大量的噪声,因此对NDV I时间序列数据集进行重建,提高NDV I数据集质量的研究逐步受到关注。对近年来普遍使用的几种NDV I时间序列数据集重建方法(最大值合成、最佳指数斜率提取、中值迭代滤波、时间窗内的线性内插、傅里叶变换、S-G滤波)进行了详细介绍并评述了这些方法的优缺点。

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.