Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities

2021 ◽  
Vol 75 ◽  
pp. 103374
Author(s):  
Asfa Siddiqui ◽  
Gautami Kushwaha ◽  
Bhaskar Nikam ◽  
S.K. Srivastav ◽  
Ankita Shelar ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Annual Changes ◽  
Indian Cities

scholarly journals A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data

2020 ◽  
Vol 12 (12) ◽  
pp. 2052 ◽  
Author(s):  
José Antonio Sobrino ◽  
Itziar Irakulis
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Urban Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Heat Island ◽  
Urban Agglomerations ◽  
The World ◽  
Urban Heat ◽  
Surface Urban Heat Island

Retrieval of land surface temperature (LST) from satellite data allows to estimate the surface urban heat island (SUHI) as the difference between the LST obtained in the urban area and the LST of its surroundings. However, this definition depends on the selection of the urban and surroundings references, which translates into greater difficulty in comparing SUHI values in different urban agglomerations across the world. In order to avoid this problem, a methodology is proposed that allows reliable quantification of the SUHI. The urban reference is obtained from the European Space Agency Climate Change Initiative Land Cover and three surroundings references are considered; that is, the urban adjacent (Su), the future adjacent (Sf), and the peri-urban (Sp), which are obtained from mathematical expressions that depend exclusively on the urban area. In addition, two formulations of SUHI are considered: SUHIMAX and SUHIMEAN, which evaluate the maximum and average SUHI of the urban area for each of the three surrounding references. As the urban population growth phenomenon is a world-scale problem, this methodology has been applied to 71 urban agglomerations around the world using LST data obtained from the sea and land surface temperature radiometer (SLSTR) on board Sentinel-3A. The results show average values of SUHIMEAN of (1.8 ± 0.9) °C, (2.6 ± 1.3) °C, and (3.1 ± 1.7) °C for Su, Sf, and Sp, respectively, and an average difference between SUHIMAX and SUHIMEAN of (3.1 ± 1.1) °C. To complete the study, two additional indices have been considered: the Urban Thermal Field Variation Index (UFTVI) and the Discomfort Index (DI), which proved to be essential for understanding the SUHI phenomenon and its consequences on the quality of life of the inhabitants.

scholarly journals IDENTIFYING THE LOCAL SURFACE URBAN HEAT ISLAND THROUGH THE MORPHOLOGY OF THE LAND SURFACE TEMPERATURE

2016 ◽  
Vol III-2 ◽  
pp. 69-75
Author(s):  
Jiong Wang ◽  
Qingming Zhan ◽  
Yinghui Xiao
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Satellite Image ◽  
Local Scale ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

Current characterization of the Land Surface Temperature (LST) at city scale insufficiently supports efficient mitigations and adaptations of the Surface Urban Heat Island (SUHI) at local scale. This research intends to delineate the LST variation at local scale where mitigations and adaptations are more feasible. At the local scale, the research helps to identify the local SUHI (LSUHI) at different levels. The concept complies with the planning and design conventions that urban problems are treated with respect to hierarchies or priorities. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. The continuous and smooth latent LST is first recovered from the raw images. The Multi-Scale Shape Index (MSSI) is then applied to the latent LST to extract morphological indicators. The local scale variation of the LST is quantified by the indicators such that the LSUHI can be identified morphologically. The results are promising. It can potentially be extended to investigate the temporal dynamics of the LST and LSUHI. This research serves to the application of remote sensing, pattern analysis, urban microclimate study, and urban planning at least at 2 levels: (1) it extends the understanding of the SUHI to the local scale, and (2) the characterization at local scale facilitates problem identification and support mitigations and adaptations more efficiently.

scholarly journals IDENTIFYING THE LOCAL SURFACE URBAN HEAT ISLAND THROUGH THE MORPHOLOGY OF THE LAND SURFACE TEMPERATURE

2016 ◽  
Vol III-2 ◽  
pp. 69-75 ◽  
Author(s):  
Jiong Wang ◽  
Qingming Zhan ◽  
Yinghui Xiao
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Satellite Image ◽  
Local Scale ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

Current characterization of the Land Surface Temperature (LST) at city scale insufficiently supports efficient mitigations and adaptations of the Surface Urban Heat Island (SUHI) at local scale. This research intends to delineate the LST variation at local scale where mitigations and adaptations are more feasible. At the local scale, the research helps to identify the local SUHI (LSUHI) at different levels. The concept complies with the planning and design conventions that urban problems are treated with respect to hierarchies or priorities. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. The continuous and smooth latent LST is first recovered from the raw images. The Multi-Scale Shape Index (MSSI) is then applied to the latent LST to extract morphological indicators. The local scale variation of the LST is quantified by the indicators such that the LSUHI can be identified morphologically. The results are promising. It can potentially be extended to investigate the temporal dynamics of the LST and LSUHI. This research serves to the application of remote sensing, pattern analysis, urban microclimate study, and urban planning at least at 2 levels: (1) it extends the understanding of the SUHI to the local scale, and (2) the characterization at local scale facilitates problem identification and support mitigations and adaptations more efficiently.

scholarly journals Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou

2020 ◽  
Vol 12 (13) ◽  
pp. 2134 ◽  
Author(s):  
Rui Wang ◽  
Weijun Gao ◽  
Wangchongyu Peng
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Time Dimension ◽  
Urban Heat ◽  
Surface Urban Heat Island

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.

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