Abstract
More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l’ Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets.
Highlights
Analysis of Normal Difference Vegetation Index (NDVI) time-series data is becoming increasingly important for ecological research on environmental dynamics and climate change [1,2,3], vegetation dynamics [4,5,6,7,8], land cover change [9,10,11], and animal species distribution [12]
The Savitzky-Golay filter (S-G), changing-weight filter (CW), and Whittaker smoother (WS) techniques show better reconstructed effects than the other techniques, and the interpolation for data reconstruction (IDR) technique shows generally poor performance in terms of Root mean square error (RMSE), Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) for most vegetation types in the Heihe River Basin (Table 7). These findings are inconsistent with these of Hird and McDermid [15] who stated that Asymmetric Gaussian (A-G) and double logistic function (D-L) techniques perform better than S-G in terms of RMSE
The findings are supported by Zhu et al [26] and Jiang et al [27], and both of the results indicated that the S-G
Summary
Analysis of Normal Difference Vegetation Index (NDVI) time-series data is becoming increasingly important for ecological research on environmental dynamics and climate change [1,2,3], vegetation dynamics [4,5,6,7,8], land cover change [9,10,11], and animal species distribution [12]. Analyzing NDVI time-series data has been a useful tool for studying climate, vegetation, and animal distribution, and performance at large spatial and temporal scales [13]. They can be grouped into five types: (1) threshold methods, including the best index slope extraction technique (BISE) [20] and the modified BISE technique (M-BISE) [21]; (2) filter-based methods, including running medians (4253H) [22], the ARMD3-ARMA5 filter technique [23], the Savitzky-Golay filter technique (S-G) [24], the mean value iteration filter (MVI) technique [25], the changing-weight filter (CW) technique [26], and phenology-preserving filtering (PP) [27]; (3) function fitting methods, such as the fast Fourier transform (FFT) [28], the temporal window operation (TWO) [29], the harmonic analysis of time series (HANTS) [30], double logistic function fitting (D-L) [31,32,33,34], and asymmetric
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