Spatiotemporal analysis of surface temperature dynamics  in the Supii River basin using regression methods

Serhii Marhes; Volodymyr Filipovych; Mykola Lubskyi

doi:10.24028/gj.v47i5.333289

Authors

Serhii Marhes Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Kyiv, Ukraine, Ukraine
Volodymyr Filipovych Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Kyiv, Ukraine, Ukraine
Mykola Lubskyi Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Kyiv, Ukraine, Ukraine

DOI:

https://doi.org/10.24028/gj.v47i5.333289

Keywords:

linear regression, time series, land surface temperature, annual increment, global warming

Abstract

The research presents an approach for detecting spatial and temporal changes, specifically the annual increase in land surface temperature. The study area encompasses the Supii River basin, which spans the Chernihiv, Kyiv, and Cherkasy oblasts and flows into the Dnipro. This region is characterized by intensive agriculture, poor aquifer recharge, low-quality groundwater, and prolonged droughts.

Temperature data for July and August were obtained from publicly available Landsat mission archives for the period 1984—2024. It is recommended to recalculate Landsat thermal images based on emissivity, since their Level 2 product may contain pixels with missing information. To ensure the highest accuracy, pixels affected by clouds and their shadows were masked; the next stepinvolved time series analysis of the filtered images.

The time series analysis aimed to identify key patterns in the evolution of temperature dynamics, functionally dependent on various influencing factors. Accurate spatial alignment of the imagery enabled consistent undistorted calculation of the ground’s physical characteristics over the entire study area and for each year of observation. A simple linear regression was applied to each pixel in each raster image. To visualize the spatial distribution of long-term temperature dynamics, the regression gradient (or slope coefficient) was used, representing the average annual increase in temperature.

The results are presented as spatial indices of annual surface temperature growth within the Supii River basin, highlighting settlements with dominant positive trends, and the distribution by land use cover. This provides insight into where and to what extent climate conditions may become critical in the coming years, assuming the temperature continues to rise. As a mitigation measure, it is proposed to vegetate the urban and rural areas (particularly in larger local communities) to help reduce the impact of global warming.

References

Altman, N., & Krzywinski, M. (2015). Simple linear regression. Nature Methods, 12(11), 999—1000. https://doi.org/10.1038/nmeth.3627.

Bala, R., Prasad, R., Yadav, V.P., & Sharma, J. (2018). A comparative study of land surface temperature with different indices on heterogeneous land cover using Landsat 8 data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(5), 389—394. https://doi.org/10.5194/isprs-archives-xlii-5-389-2018.

Bowman, A.W., & Azzalini, A. (1997). Applied smoothing techniques for data analysis: The kernel approach with S-Plus illustrations. Oxford University Press, 191 p.

Brockwell, P.J., & Davis, R.A. (Eds.). (2002). Introduction to time series and forecasting. Springer. https://doi.org/10.1007/b97391.

Chander, G., Markham, B.L. & Helder, D.L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893—903. https://doi.org/10. 1016/j.rse.2009.01.007.

Chatfield, C. (2000). Time-series forecasting. Chap-man & Hall/CRC. https://doi.org/10.1201/ 9781420036206.

Cohn, P.M. (1994). Elements of linear algebra. Chapman & Hall/CRC. ISBN 9780412552809.

Derii, M.M. (2007). Report on the geological survey of the subsurface: Geological structure and minerals of the confluence of the Trubizh and Supii rivers (Book 1). State Geological Service of Ukraine.

Draper, N.R., & Smith, H. (1998). Applied regression analysis (3rd ed.). John Wiley & Sons.

Filipovych, V.Ye., & Shevchuk, R.M. (2018). Satellite technology for determining the heat load on the city in summer and ways to overcome it through green planning. Conference:Sustainable cities: Implementing green planning, design and construction ideas in Ukraine. https://doi.org/10.13140/RG.2.2.17113.08807.

Hemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021). A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth. Remote Sensing, 13, 1—33. https://doi.org/10.3390/rs13152869.

Ismaila, A.-R.B., Muhammed, I., & Adamu, B. (2023). A spatial regression approach to modeling urban land surface temperature. MethodsX, 10, 1025. https://doi.org/10.1016/j.mex.2023.1025.

Kendall, M.G. (1975). Rank correlation methods (4th ed.). Charles Griffin.

Li, X., Wang, Y., & Wu, C. (2018). «Regression-then-Fusion» or «Fusion-then-Regression»? A theoretical analysis for generating high spatiotemporal resolution land surface temperatures. Remote Sensing, 10(9), 1382. https://doi.org/10.3390/rs10091382.

Lischenko, L., Shevchuk, R., & Filipovych, V. (2022). The technique for satellite monitoring of peatlands in order to determinate their fire hazard and combustion risks. Ukrainian Journal of Remote Sensing, 9(1), 23—32. https://doi.org/10.36023/ujrs.2022.9.1.210 (in Ukrainian).

Lubskyi, M., Khyzhniak, A., Piestova, I., & Orlenko, T. (2025). Land surface emissivity estimation technique based on NDVI/ASTER GED data correlation. 18th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, 2025 (pp. 1—5). https://doi.org/10.3997/2214-4609.2025510040.

Lubskyi, M., Orlenko, T., Piestova, I., Andreiev, A., & Lysenko, A. (2023). Evaluation of indicators for desertification risk assessment of Oleshky sands desertification based on Landsat data time series. Ukrainian Journal of Remote Sensing, 10(1), 17—28. https://doi.org/10.36023/ujrs.2023.10.1.229 (in Ukrainian).

Lyalko, V.I., Filipovich, V.E., Lischenko, L.P., Pazynych, N.V., Teremenko, A.N., & Krylova, A.B. (2016). Remote sensing monitoring of historical centre of Kyiv for reducing risks from disasters at world heritage properties. Journal of the Japanese Geotechnical Society Special Publication, 2(78), 2671—2675. https://doi.org/10.3208/jgssp.TC301-04.

Management plan of the Dnieper river basin: Part 1 (2025—2030). (2021). Ministry of Environmental Protection and Natural Resources of Ukraine, 422 p. Retrieved from https://davr.gov.ua/fls18/DNIPRO4.pdf.

Mann, H.B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245—259. https://doi.org/10.2307/1907187.

Marhes, S.V. (2023). Research of the Supiya River Valley as a Link of the Emerald Network of Europe Using GIS Technologies and Remote Sensing Methods. In S.O. Dovgy (Ed.), Information and Communication Technologies for Victory and Recovery (pp. 193—195). Kyiv: Yuston Publishing House (in Ukrainian).

Stankevich, S.A., Filipovich, V.E., Lubsky, N.S., Krylova, A.B., Kritsuk, S.G., Brovkina, O.V., Gornyy, V.I., & Tronin, A.A. (2015). Intercalibration of methods for land surface thermodynamic temperature retrieval inside urban areas by thermal-infrared satellite imaging. Ukrainian Journal of Remote Sensing, (7), 12—21 (in Russian).

Valor, E., & Caselles, V. (1996). Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sensing of Environment, 57(3), 167—184. https://doi.org/10.1016/0034-4257(96)00039-9.

Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS-Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829—9852. https://doi.org/10.3390/rs6109829.

Zhang, Y., & Li, Z. (2020). Modelling future land surface temperature: A comparative analysis between parametric and non-parametric methods. Sustainability, 12(18), 8195. https://doi.org/10.3390/su12188195.