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Time-series terrestrial parameters derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA, or AQUA/MODIS data, such as Normalized Difference Vegetation Index (NDVI), Leaf Index Area (LAI), and Albedo, have been extensively applied to global climate change. However, the noise impedes these data from being further analyzed and used. In this paper, a wavelet-based method is used to remove the contaminated data from time-series observations, which can effectively maintain the temporal pattern and approximate the "true" signals. The method is composed of two steps: (a), time-series values are linearly interpolated with the help of quality flags and the blue band, and (b), time series are decomposed into different scales and the highest correlation among several adjacent scales is used, which is more robust and objective than the threshold-based method. Our objective was to reduce noise in MODIS NDVI, LAI, and Albedo time-series data and to compare this technique with the BISE algorithm, Fourier-based fitting method, and the Savitzky-Golay filter method. The results indicate that our newly developed method enhances the ability to remove noise in all three time-series data products.

Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products