Temporal variation patterns of Land Surface Temperature (LST) under different time scales are crucial in understanding the response of urban thermal environment to different forcings. However, there is no integrated toolset to extract such patterns from satellite remotely sensed time series LST (TSLST) data. This paper presents a workflow to extract the multi-timescale temporal patterns and dynamics from nonlinear and non-stationary TSLST data by taking Wuhan, China as case study. The 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products spanning the 2003–2017 period are used to generate a TSLST dataset with continuous and smooth surfaces on the monthly basis through the non-parametric Multi-Task Gaussian Process Modeling (MTGP). The study area is segmented into multiple time series clusters by k-means to bridge with urban planning in terms of research and implementation scale. Then, temporal patterns including annual, interannual components, and overall trends are reconstructed based on the components with characteristic time scales decomposed by the adaptive Ensemble Empirical Mode Decomposition (EEMD) method. The generated patterns of the 17 time series clusters are interpreted from the perspective of earth revolution, meteorological cycles and urbanization. Specifically, the annual components which are mainly generated by earth revolution reveal consistent rhythmic patterns among the time series. The interannual components preserve similar shapes although they differ in amplitudes. The overall shape is basically consistent with that of air temperature of Central China, which may be mainly induced by the El Niño-Southern Oscillation (ENSO) phenomenon. The overall trends which exert considerable differences are grouped into three types by shape. Such differences may be potentially caused by the inconsistent levels of localized urbanization, afforestation or circular economy development. This study facilitates the understanding of TSLST patterns and human-environment interactions. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.
- Temporal variation
- Temporal dynamic