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Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

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  • Xinan Zhang

    (School of Geography, Nanjing Normal University, Nanjing 210044, China)

  • Fan Yang

    (School of Geography, Nanjing Normal University, Nanjing 210044, China)

  • Jun Zhang

    (School of Geography, Nanjing Normal University, Nanjing 210044, China)

  • Qiang Dai

    (School of Geography, Nanjing Normal University, Nanjing 210044, China)

Abstract

The identification of influencing factors (IFs) of land surface temperature (LST) is crucial for developing effective strategies to mitigate global warming and conducting other relevant studies. However, most previous studies ignored the potential impact of interactions between IFs, which might lead to biased conclusions. Generalized additivity models (GAMs) can provide more explanatory results compared to traditional machine learning models. Therefore, this study employs GAMs to investigate the impact of IFs and their interactions on LST, aiming to accurately detect significant factors that drive the changes in LST. The results of this case study conducted in Nanjing, China, showed that the GAMs incorporating the interactions between factors could improve the fitness of LST and enhance the explanatory power of the model. The autumn model exhibited the most significant improvement in performance, with an increase of 0.19 in adjusted- R 2 and a 17.9% increase in deviance explained. In the seasonal model without interaction, vegetation, impervious surface, water body, precipitation, sunshine hours, and relative humidity showed significant effects on LST. However, when considering the interaction, the previously observed significant influence of the water body in spring and impervious surface in summer on LST became insignificant. In addition, under the interaction of precipitation, relative humidity, and sunshine hours, as well as the cooling effect of NDVI, there was no statistically significant upward trend in the seasonal mean LST during 2000–2020. Our study suggests that taking into account the interactions between IFs can identify the driving factors that affect LST more accurately.

Suggested Citation

  • Xinan Zhang & Fan Yang & Jun Zhang & Qiang Dai, 2024. "Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing," Land, MDPI, vol. 13(4), pages 1-18, April.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:4:p:465-:d:1370669
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    References listed on IDEAS

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    1. Chen, Xiaoguang & Yang, Lu, 2019. "Temperature and industrial output: Firm-level evidence from China," Journal of Environmental Economics and Management, Elsevier, vol. 95(C), pages 257-274.
    2. Rao, Yingxue & Dai, Jingyi & Dai, Deyi & He, Qingsong, 2021. "Effect of urban growth pattern on land surface temperature in China: A multi-scale landscape analysis of 338 cities," Land Use Policy, Elsevier, vol. 103(C).
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    Cited by:

    1. Asma A. Al-Huqail & Zubairul Islam & Hanan F. Al-Harbi, 2025. "Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia," Land, MDPI, vol. 14(1), pages 1-24, January.
    2. Asma A. Al-Huqail & Zubairul Islam, 2025. "Ecological Stress Assessment on Vegetation in the Al-Baha Highlands, Saudi Arabia (1991–2023)," Sustainability, MDPI, vol. 17(7), pages 1-22, March.

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