Characterizing the spatio-temporal pattern of Land Surface Temperature through time series clustering: Based on the latent pattern and morphology

Huimin Liu, Qingming Zhan, Chen Yang, Jiong Wang

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)
71 Downloads (Pure)


Land Surface Temperature (LST) is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products. The Latent pattern of LST (LLST) generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP) and the Multi-Scale Shape Index (MSSI) which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k -means and the Back-Propagation neural networks (BP-Net). The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k -means with Euclidean Distance (ED), shape-based k -means with the constrained Dynamic Time Warping ( c DTW) distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA) centroid computation method ( k - c DBA) and k -shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k - c DBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST patterns. The homogeneous geographical clusters correspond to the zoning custom of urban planning and design, and thus, may efficiently bridge the urban and environmental systems in terms of research scope and scale. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.
Original languageEnglish
Article number654
Pages (from-to)1-23
Number of pages23
JournalRemote sensing
Issue number4
Publication statusPublished - 23 Apr 2018


  • LST
  • spatio-temporal
  • pattern recognition
  • time series clustering
  • latent pattern
  • morphology


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