关键词: 遥感, 时间序列, 重构模型, 时间间隔

Abstract:

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.

Key words: remote sensing, time series, reconstruction model, time intervals