不同时间间隔下的遥感时间序列重构模型比较分析

遥感时间序列被广泛用于地表信息探测。然而受传感器和外部条件的影响,时间序列存在不同程度的噪声。时间序列重构模型能够实现时间序列去噪,但不同重构模型应用于不同时间间隔时间序列的精度不同。本文以辽宁省朝阳市为研究区,分别利用1、4、8、16和30 d间隔归一化植被指数（Normalized Difference Vegetable Index, NDVI）时间序列,进行模拟数据实验和物候监测实验,从波形还原能力和物候期提取精度2方面,评价了SG滤波、DL拟合、HANTS 3种模型在不同时间间隔下的重构效果。结果表明,SG滤波模型适用于较大时间间隔的时间序列数据,DL拟合模型适用于较小时间间隔的时间序列数据,HANTS模型对较小间隔的时间序列重构精度较低。在此基础上,从模型自身的角度分析了3者在不同时间间隔下表现的原因,并为面向不同时间间隔时间序列数据的重构模型选择提供了参考。

Comparison and Analysis of Remotely Sensed Time Series of Reconstruction Models at Various Intervals

Remotely sensed time series are being widely used in land surface information detection. However, influenced by the sensors and external conditions, different levels of noises exist in the remotely sensed time series. Although reconstruction models can reduce the noises in times series effectively, different reconstruction models provide different levels of accuracy when they are used at various intervals. This study took the city of Chaoyang in Liaoning Province as a case. We utilized the time series of Normalized Difference Vegetation Index (NDVI) at intervals of 1-day, 4-day, 8-day, 16-day and 30-day, respectively, to carry out experiments of simulation and phenology observation. We also assessed the reconstruction results of the SG filter model, the DL fitting model and the HANTS model based on their capabilities of keeping waveform of time series and their accuracy of phenological date extraction. In addition, we also analyzed sensitivity of these three models to various intervals. The results showed that the SG filter model performed better at larger intervals, the DL fitting model gave better reconstruction accuracy at smaller intervals and the Hants model gave better accuracy when it is used at larger intervals. Moreover, the reasons of the different performance of the three reconstruction models were analyzed from the theories of these models. On this basis, we gave the suggestions on the choice of reconstruction models of time series at different intervals.