中文摘要：

受多重因素的影响，MODISNDVI数据产品中存在着大量的噪声，需要进行去噪重建。针对目前几种常用的NDVI时间序列数据去云方法，如HANTS法、SPLINE插值法以及Savizky—Golay法，以山东省MODIS NDVI时间序列数据（一年的）作为检验数据，从不同角度比较几种算法的去云能力和使用范围。结果表明：SPLINE插值法的去噪效果取决于云掩模数据的质量，但有时会产生异常值；HANTS算法和Savizky—Golay算法会改变几乎所有像元的值，得到一个比较平滑的时间序列曲线，但这两种算法的输人参数没有统一标准，需多次试验才能确定最优参数。

英文摘要：

Although composite data present lower atmospheric contamination than raw time series, MODIS NDVI products are still contaminated by clouds, especially when cloud cover lasts longer than the composite period, e. g. , in the rainy season. The long - time cloud cover will weaken the application of MODIS NDVI time series data. To remove the effect of these clouds from NDVI data and reconstruct high - quality NDVI data, the authors propose three algorithms for cloud removal, namely SPLINE function, HANTS and Savizky - Golay. The capabilities of the three algorithms in cloud removal was compared with each other in this study, with the MODIS NDVI time series data in Shandong province serving as the test data. The results show that the three algorithms can remove the effect of cloud from NDVI time series data effectively, with each algorithm having its own advantages and disadvantages. For the algorithm of SPLINE function, the result of cloud removal mainly depends on the quality of cloud data and sometimes extreme values will occur; this algorithm fails to change the values of pixels which have not been contaminated atmospherically. When HANTS and Savizky - Golay algorithms are used, most of the pixels will lose their original values, and the parameters have to be determined after conducting many experiments because there is no objective rule to set them.