Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

Abstract

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 (cDTW) distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA) centroid computation method (k-cDBA) and k-shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k-cDBA 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.

The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA)

“…The goal of CDF matching is to scale a source data set such that its empirical CDF becomes similar to the empirical CDF of the reference data set. CDF matching is applied on a per-pixel basis and has been successfully used for similar tasks that require the correction of higher-order differences between data sets (Liu et al, 2009(Liu et al, , 2011a(Liu et al, , 2012Dorigo et al, 2017).…”

“…To overcome this issue, Liu et al (2011a) proposed a long-term harmonized multi-sensor VOD data set by merging VOD products derived from the Special Sensor Microwave/Imager (SSM/I), the Microwave Imager on board the Tropical Rainfall Measuring Mission (TMI), and the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) through the Land Parameter Retrieval Model (LPRM; Owe et al, 2008). Their methodology was inherited from the methodology used to produce the first long-term satellite-based climate data record of soil moisture within the Climate Change Initiative of the European Space Agency (ESA CCI Soil moisture; Dorigo et al, 2017Liu et al, 2011cLiu et al, , 2012Gruber et al, 2019). In their methodology, all available observations were harmonized with respect to C-band (6.9 GHz) VOD observations from AMSR-E, which was assumed to provide the highest-quality observations (Liu et al, 2012).…”

“…Their methodology was inherited from the methodology used to produce the first long-term satellite-based climate data record of soil moisture within the Climate Change Initiative of the European Space Agency (ESA CCI Soil moisture; Dorigo et al, 2017Liu et al, 2011cLiu et al, , 2012Gruber et al, 2019). In their methodology, all available observations were harmonized with respect to C-band (6.9 GHz) VOD observations from AMSR-E, which was assumed to provide the highest-quality observations (Liu et al, 2012). Only in periods where AMSR-E C-band observations were not available, were other products used instead.…”

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

“…Previous studies have mainly focused on studying differences in ground temperatures as a function of land-use land cover and its evolution at the city level [16,16,20,[33][34][35], temporal trends in urban SUHI in urban areas [18,36,37], the refreshing impact of parks on their surroundings [15,38,39], the evolution of SUHI as a function of day and night [9,34,40,41], the impact of vegetation on LST at the urban scale [12,36,37,42], comparison of surface temperatures and air temperature [8][9][10]20,43,44], the impact of surface temperatures on health [30,41] and transversely at surface temperatures at moderate resolutions (MODIS 1 km) [16,20,[34][35][36]42,45] but, to our knowledge, there are no similar studies such as ours that analyze the thermal monitoring of site redevelopment at such a detailed spatial and temporal resolution.…”

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

“…LST derived from thermal infrared remote sensing has drawn attention from geographers and environmentalist, over the last two decades [12]. Understanding LST and SUHI dynamics may improve our awareness of regional environmental change and support sustainable development [13,14]. For this reason, it is important to analyze the spatial patterns of LST and SUHIs and identify their influencing factors.A recent review conducted by Zhou et al[15] provides a comprehensive summary of the main satellites, methods, key findings, and challenges regarding the SUHI research.…”