Abstract
Land surface temperature is a crucial physical parameter in the examination of the natural ecological environment. The study utilized Landsat data to investigate land use indices and thermal environment change in Guangzhou, employing the radiative transfer equation, concentric circles, Pearson correlation coefficient, and other geospatial methods. Overall, as the distance from the city center increased, NDVI values tended to rise, while land surface temperature showed a gradual decreasing trend. Additionally, land surface temperature exhibited a negative correlation with NDVI and a positive correlation with NDBI. Barren had the highest LST, followed by impervious, while the water and the forest were cooler. The high-temperature area took on a V-shape, primarily situated in the west and southern areas, whereas the cooler temperature zone was mainly found in the northeast. The results can offer a scientific foundation for further exploration of the urban heat island formation mechanism, development of rational planning policies, and assessment of urbanization’s impact on local climate.
Data availability
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
References
Hasan, S. S., Zhen, L., Miah, M. G., Ahamed, T. & Samie, A. Impact of land use change on ecosystem services: A review. Environ. Dev. 34, 100527 (2020).
Jiang, W., Wu, T. & Fu, B. The value of ecosystem services in China: A systematic review for twenty years. Ecosyst. Serv. 52, 101365 (2021).
Zhou, Y., Li, X. & Liu, Y. Rural land system reforms in China: History, issues, measures and prospects. Land. Use Policy. 91, 104330 (2019).
Long, H. & Li, X. Analysis of land use pattern and its influencing factors in transect along the Yangtze River. Acta Geogr. Sin. 417–425 (2001).
Wu, S. Current situation and prospect of land use change simulation research in China. Agric. Technol. 43, 138–142 (2023).
Tian, H. & Liu, L. Spatiotemporal differentiation and attribution analysis of land surface temperature in China from 2001 to 2020. Acta Geogr. Sin. 77, 1713–1729 (2022).
Rawat, V., Saraf, A. K., Das, J., Sharma, K. & Shujat, Y. Anomalous land surface temperature and outgoing long-wave radiation observations prior to earthquakes in India and Romania. Nat. Hazards. 59, 33–46 (2011).
Liu, K. et al. Quantifying Spatial–Temporal Pattern of Urban Heat Island in Beijing: An Improved Assessment Using Land Surface Temperature (LST) Time Series Observations From LANDSAT, MODIS, and Chinese New Satellite GaoFen-1. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9, 2028–2042 (2016).
Jia, A., Ma, H., Liang, S. & Wang, D. Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method. Remote Sens. Environ. 263, 112566 (2021).
Zhang, F. et al. Cloud-free land surface temperature reconstructions based on MODIS measurements and numerical simulations for characterizing surface urban heat islands. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 15, 6882–6898 (2022).
Yu, W. et al. Attribution of Urban Diurnal Thermal Environmental Change: Importance of Global–Local Effects. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16, 8087–8101 (2023).
Sobrino, J. A., Oltra-Carrió, R., Sòria, G., Bianchi, R. & Paganini, M. Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sens. Environ. 117, 50–56 (2012).
Chen, H., Deng, Q., Zhou, Z., Ren, Z. & Shan, X. Influence of land cover change on spatio-temporal distribution of urban heat island —a case in Wuhan main urban area. Sustain. Cities Soc. 79, 103715 (2022).
Yu, X., Guo, X. & Wu, Z. Land Surface Temperature Retrieval from Landsat 8 TIRS-Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Remote Sens. 6, 9829–9852 (2014).
Hua, W. & Chen, H. New understanding of climate effects of land use/land cover change in the context of global warming. Sci. Bull. 58, 2832–2839 (2013).
Liu, J. et al. Biogeophysical mechanisms of land use change affecting climate change. J. Nat. 36, 356–363 (2014).
Liu, Z., Hu, W., Ma, L. & Huang, X. Spatio-temporal variations of vegetation cover and its influencing factors in highland lake basin. Front. Environ. Sci. 12, 1502208 (2024).
Danladi, T., Oruonye, E. D., Terhemen, A., Altayar, Y. & Zemba, A. Assessment of the impact of changes in land-use land-cover and land surface temperature on vegetal resources in Taraba State Central Zone Nigeria. Int. J. Environ. Sustain. Green. Technol. 1–19 (2025).
Zhang, Y., Silva, C. & Chen, M. Analyzing thermal environment contributions and driving factors of LST heterogeneity in different urban development zones. Remote Sens. 16, 2973 (2024).
Tian, L., Yang, J. & Jin, C. Dynamic Changes in Land Cover and Its Effect on Urban Heat Islands. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. PP, 1–11 (2024).
Yang, L., Shi, L., Li, J., Kong, H. & Shan, Z. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land. Open Geosci. 16 (2024).
Gemilang, D., Saputra, A. & Sasmi, A. Spatio-temporal relationship analysis of NDVI and LST in Palangkaraya City, Central Kalimantan Province (2018–2022). IOP Conf. Ser. Earth Environ. Sci. 1462, 012006 (2025).
Zhang, T., Xu, R. & Ye, J. Spatial heterogeneity of the relationship between NDVI and LST under urban land use patterns—a case study of Shanghai (2000–2024). Environ. Monit. Assess. 198, 171 (2026).
Sunitha, N. et al. Correlation Between LST, NDVI and NDBI with Reference to Bengaluru Urban, Karnataka. IJFMR 7, 48420 (2025).
Ekele, J. I., Bello, I. E. & Jacob, R. J. Urbanization and environmental quality assessment in the Abuja Municipal Area Council Using Lst, Ndvi, Ndbi and Ndwi. IJRISS 9, 5860–5873 (2025).
RoohaniQadikolaei, M., RoohaniQadikolaei, F., Soltani, A., Misaghi, M. & Zali, N. Distance matters: Quantifying the influence of urban land use change and development proximity on land surface temperature in Sari, Iran. Ecol. Ind. 174, 113386 (2025).
Meng, L., Sun, Y. & Zhao, S. Comparing the spatial and temporal dynamics of urban expansion in Guangzhou and Shenzhen from 1975 to 2015: A case study of pioneer cities in China’s rapid urbanization. Land. Use Policy. 97, 104753 (2020).
Zhang, Y., Hu, Y. & Zhuang, D. A highly integrated, expansible, and comprehensive analytical framework for urban ecological land: A case study in Guangzhou, China. J. Clean. Prod. 268, 122360 (2020).
Yang, J. & Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data. 13, 3907–3925 (2021).
Liu, Z., Wei, W., Dong, Y. & Hu, W. Monitoring and Influencing Factors Analysis of Urban Vegetation Changes in the Plateau-Mountainous City. Forests 16, 1339 (2025).
Artis, D. A. & Carnahan, W. H. Survey of emissivity variability in thermography of urban areas. Remote Sens. Environ. 12, 313–329 (1982).
Griend, A. A. V. D. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int. J. Remote Sens. 14, 1119–1131 (1993).
Bausch, W. C. & Duke, H. R. Remote sensing of plant nitrogen status in corn. Trans. Am. Soc. Agric. Eng. 39, 1869–1875 (1996).
Liu, H. et al. Nonlinear relationship of vegetation greening with nature and human factors and its forecast – A case study of Southwest China. Ecol. Ind. 111, 106009 (2020).
Chen, S. & Wang, T. A comparative study of urban heat island defined by equidistance method and mean standard deviation method. J. Geoinf. Sci. 11, 145–150 (2009).
Mushore, T. D., Mutanga, O. & Odindi, J. Estimating urban LST using multiple remotely sensed spectral indices and elevation retrievals. Sustainable Cities Soc. 78, 103623 (2022).
Nse, O. U., Okolie, C. J. & Nse, V. O. Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria. Sci. Afr. 10, e00599 (2020).
Yuvaraj, R. M. Extents of Predictors for Land Surface Temperature Using Multiple Regression Model. Sci. World J. 1–10 (2020). (2020).
Gobatti, L., Bach, P. M., Maurer, M. & Leitao, J. P. Impact of soil moisture content on urban tree evaporative cooling and human thermal comfort. NPJ Urban Sustain 5 (2025).
Zuo, Y., Guo, Y., Song, C., Jin, S. & Qiao, T. Study on Soil Water and Heat Transport Characteristic Responses to Land Use Change in Sanjiang Plain. Sustainability 11 (2019).
Chen, J., Zhou, Z., Wu, J., Hou, S. & Liu, M. Field and laboratory measurement of albedo and heat transfer for pavement materials. Constr. Build. Mater. 202, 46–57 (2019).
Liu, X. et al. Wind environment assessment and planning of urban natural ventilation corridors using GIS: Shenzhen as a case study. Urban Clim. 42, 101091 (2022).
Liu, L., Li, Q., Niu, Z. & Huo, X. Comparative study on information extraction of urban wetlands and its thermal environment using the SDGSAT-1 data. Int. J. Digit. Earth 17 (2024).
Liu, L. et al. Cooling effects of wetland parks in hot and humid areas based on remote sensing images and local climate zone scheme. Build. Environ. 243, (2023).
Xiang, X. et al. Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China. J. Environ. Manag. 366, (2024).
Li, J. et al. Effect of optimal allocation of urban trees on the outdoor thermal environment in hot and humid areas: A case study of a university campus in Guangzhou, China. Energy Build. 300 (2023).
Gagliano, A., Detommaso, M., Nocera, F. & Berardi, U. The adoption of green roofs for the retrofitting of existing buildings in the Mediterranean climate. Int. J. Sustain. Build. Technol. Urban Dev. 7, 116–129 (2016).
Acknowledgements
We express our gratitude to anonymous reviewers and editors for their professional comments and suggestions.
Funding
This research was jointly supported by the Yunnan Provincial Basic Research (202301AT070084 and 202301AT070085), Western Yunnan University of Applied Sciences Talent Introduction Research Initiation Project (2023RCKY0001 and 2022RCKY0003), Breaking the Bottleneck of Teaching Resources: Infinite Simulation Scenario Generation and Practice for Autonomous Driving Based on AIGC and GIS (2026J1229), The Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities’ Association: Research on the evaluation method of land ecosystem vulnerability in central Yunnan (202401BA070001-016).
Author information
Authors and Affiliations
Contributions
Conceptualization and supervision, Z.L. and K.H.; methodology, Z.L. and K.H.; writing—original draft preparation, Z.L. and Z.K.; writing—review and editing, Z.L., K.H., Z.K., L.Y., Y.M., Y.Z.; validation, Z.L., Z.K. and K.H. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Liu, Z., He, K., Ke, Z. et al. Multi-buffer zones reveal the relationship between spatial pattern of land surface temperature and land use indices in Guangzhou, China. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44159-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-44159-3