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Evaluation of Harmonic Analysis of Time Series (HANTS): impact of gaps on time series reconstruction

机译:时间序列谐波分析的评估(HANTS):间隙对时间序列重构的影响

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In recent decades, researchers have developed methods and models to reconstruct time series of irregularly spaced observations from satellite remote sensing, among which the widely used Harmonic Analysis of Time Series (HANTS) method. Many studies based on time series reconstructed with HANTS documented the excellent performance of this method. While some limitations of HANTS have been noticed in these applications, there is no dedicated study on a systematic evaluation on the performance of the HANTS method. In this study, we evaluated the impact of gaps on the time series reconstruction of NDVI by HANTS. For global representativeness, a simulated NDVI time series dataset was constructed for four generic patterns and was applied as a reference dataset. Then random gaps were introduced into the reference series and both the reference and gapped series were reconstructed by harmonic analysis. The deviations between the two reconstructed results were used to evaluate statistically the accuracy of harmonic analysis under different gap conditions. The size of maximum gap (MGS), the number of loss (NL) and the number of gaps (NG) were selected to parameterize the gap distribution. The results showed that MGS, NL and NG were significant factors in the process of reconstruction and the two terminals and the peak of the series are crucial positions. MGS and NL should not be too large in the time series for all seasonal or non-seasonal case; otherwise the reconstructed series is not reliable. These conclusions can be taken as a reference to indicate the reliability of HANTS for particular cases towards the definition of a quality indicator of any time series.
机译:近几十年来,研究人员已经开发出了从卫星遥感重建不规则空间观测的时间序列的方法和模型,其中包括广泛使用的时间序列谐波分析(HANTS)方法。基于HANTS重建的基于时间序列的许多研究证明了该方法的出色性能。尽管在这些应用中已注意到HANTS的一些局限性,但尚无专门研究对HANTS方法的性能进行系统评估。在这项研究中,我们评估了缺口对HANTS重建NDVI时间序列的影响。为了获得全局代表性,针对四个通用模式构建了一个模拟的NDVI时间序列数据集,并将其用作参考数据集。然后将随机间隙引入参考序列,并通过谐波分析重建参考序列和带隙序列。两次重建结果之间的偏差用于统计评估不同间隙条件下谐波分析的准确性。选择最大间隙(MGS)的大小,损失数(NL)和间隙数(NG)来参数化间隙分布。结果表明,MGS,NL和NG是重建过程中的重要因素,两个末端和序列的峰值是关键位置。对于所有季节性或非季节性情况,MGS和NL的时间序列都不应过大;否则重建的序列是不可靠的。这些结论可作为参考,以指示HANTS在特定情况下对定义任何时间序列的质量指标的可靠性。

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