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Weighted Double-Logistic Function Fitting Method for Reconstructing the High-Quality Sentinel-2 NDVI Time Series Data Set

文献类型： 外文期刊

作者： Yang, Yingpin 1 ; Luo, Jiancheng 1 ; Huang, Qiting 3 ; Wu, Wei 4 ; Sun, Yingwei 1 ;

作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China

2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China

3.Guangxi Acad Agr Sci, Agr Sci & Technol Informat Res Inst, Nanning 530007, Peoples R China

4.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310014, Zhejiang, Peoples R China

关键词： NDVI; time series; filter; Sentinel-2; noise reduction; double-logistic function

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2019 年 11 卷 20 期

页码：

收录情况： SCI

摘要： The time series (TS) of the normalized difference vegetation index (NDVI) has been widely used to trace the temporal and spatial variability of terrestrial vegetation. However, many factors such as atmospheric noise and radiometric correction residuals conceal the actual variation in the land surface, and thus hamper the TS information extraction. To minimize the negative effects of these noise factors, we propose a new method to produce a synthetic gap-free NDVI TS from the original contaminated observation. First, the key temporal points are identified from the NDVI time profiles based on a generally used rule-based strategy, making the TS segmented into several adjacent segments. Then, the observed data points in each segment are fitted with a weighted double-logistic function. The proposed dynamic weight reassignment process effectively emphasizes cloud-free points and deemphasizes cloud-contaminated points. Finally, the proposed method is evaluated on more than 3,000 test points from three selected Sentinel-2 tiles, and is compared with the generally used Savitzky-Golay (S-G) and harmonic analysis of time series (HANTS) methods from qualitative and quantitative aspects. The results indicate that the proposed method has a higher capability of retaining cloud-free data points and identifying outliers than the others, and can generate a gap-free NDVI time profile derived from a medium-resolution satellite sensor.

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