A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples
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摘要: 随着遥感数据的不断积累,植被遥感产品逐渐形成了完善的时间序列数据,这些数据对阐明生态系统动态变化及分析有关的驱动因素具有重要价值。然而,云遮挡、仪器误差等因素严重制约着植被遥感产品的观测质量,往往造成连续观测数据的缺失。对存在数据缺失的序列进行时空重建是准确提取序列变化特征的重要前提,时空重建就是充分利用遥感数据的时空相关性对数据缺失进行插值以及平滑滤波,以重建完整时间序列。本研究主要以植被遥感时间序列数据为例,对以往常用的时间序列重建方法进行了简要回顾,时间序列重建大致包括插值、平滑2大步骤,插值又分为基于时间的、基于空间的、时空相结合的插值3大类型。然后,以模拟的归一化植被指数(normalized difference vegetation index,NDVI)时间序列、GIMMS NDVI真实时间序列为例,并随机制造不同比例的数据缺失,对比了线性插值、奇异谱分析、Whittaker和时间序列谐波分析等4种常用的不同类型的数据重建方法的时间序列重建效果。结果显示,4种方法的表现各有优劣,但Whittaker方法显示了整体更好的性能。由于随区域的不同,插值方法的表现可能有所差异,故不同数据重建方法还有待进一步推广验证。Abstract: Remote-sensing-based time series data of vegetation have been increasingly available with the accumulation of remote sensing data. These data are vital for ascertaining the changes in an ecosystem and analyzing relevant driving factors. However, some factors (e.g., cloud cover and instrument errors) restrict the observation quality of the vegetation products of remote sensing, creating data gaps in continuous and high-quality observation data. The data gaps can be filled based on the spatio-temporal dependence of the earth’s surface characteristics, which is called the spatio-temporal reconstruction of time series data. High-quality spatio-temporal reconstruction of time series data is an important prerequisite for the accurate extraction of changes in time series data. Taking the remote-sensing-based time series data of vegetation indices as examples, this study briefly reviewed the widely used reconstruction methods of time series data firstly. These methods generally include two steps: interpolation and smoothing. The interpolation can be divided into three major types, namely time-based, space-based, and spatio-temporal interpolation. Then, taking the simulated normalized vegetation index (NDVI) time series and actual GIMMS NDVI time series as examples, different proportions of data gaps in the two time series were created. Then, this study compared the effects of four types of data reconstruction methods (i.e., linear interpolation, singular spectrum analysis (SSA), Whittaker, and time series harmonic analysis (HANTS)) on the reconstruction results of the two time series. The results show that the four methods have their own advantages and disadvantages, and the Whittaker method showed relatively good performance overall. However, the performance of interpolation methods might vary within different regions, and thereby the data reconstruction methods need to be further verified.
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Key words:
- remote sensing of vegetation /
- time series /
- data reconstruction /
- interpolation /
- smoothing
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