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A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

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  • Yue Jiang

    (School of Environment Science and Geography, Shanghai Normal University, Shanghai 200234, China)

  • Wenpeng Lin

    (School of Environment Science and Geography, Shanghai Normal University, Shanghai 200234, China)

Abstract

In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.

Suggested Citation

  • Yue Jiang & Wenpeng Lin, 2021. "A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China," IJERPH, MDPI, vol. 18(11), pages 1-18, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5659-:d:562024
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    References listed on IDEAS

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    1. Chen Yang & Qingming Zhan & Sihang Gao & Huimin Liu, 2019. "How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China," IJERPH, MDPI, vol. 16(20), pages 1-21, October.
    2. Wei Chen & Jianjun Zhang & Xuelian Shi & Shidong Liu, 2020. "Impacts of Building Features on the Cooling Effect of Vegetation in Community-Based MicroClimate: Recognition, Measurement and Simulation from a Case Study of Beijing," IJERPH, MDPI, vol. 17(23), pages 1-22, November.
    3. Barrak Alahmad & Linda Powers Tomasso & Ali Al-Hemoud & Peter James & Petros Koutrakis, 2020. "Spatial Distribution of Land Surface Temperatures in Kuwait: Urban Heat and Cool Islands," IJERPH, MDPI, vol. 17(9), pages 1-12, April.
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    6. Xiamei Yao & Yuanyuan Chen & Qingyi Zhang & Zhongqiong Mou & Xiaojie Yao & Chun Ou, 2022. "Assessment of the Urban Expansion and Its Impact on the Eco-Environment—A Case Study of Hefei Municipal Area," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
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