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Modeling of land surface temperature–multiscale curvatures relationship using XGBoost algorithm (Case study: Southwestern Iran)

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Abstract

Land surface temperature (LST) is a fundamental variable in determining surface energy and water balances due to its control over the exchange of mass and energy between the soil surface and the atmosphere. This research aimed to investigate the relationship between spatial–temporal variability of LST and multiscale curvatures using the Extreme Gradient Boosting (XGBoost) algorithm in southwestern Iran. For this purpose, six curvatures were derived using the Wood method in three neighborhood scales (5 × 5, 15 × 15, and 25 × 25) from the 30-m Shuttle Radar Topography Mission digital elevation model. The Gaussian method was used for the landform classification. Emissivity was estimated based on the thresholding method of Normalized Difference Vegetation Index, and the average seasonal LST was calculated based on the Landsat 8 images taken during the 2013–2021 period using the inversion of Planck's function. The results depicted that neighborhood is a regulating factor in the amount of the detailed display of land surface features that in case of increasing its size, the rate of topographic surface bending decreases. In landform maps, four units that include the hill, closed depression, convex saddle, and concave saddle were identified, which have the lowest to the highest area within the studied region, respectively. The results of the Kruskal–Wallis test indicated that only the difference of each of the average LSTs of spring, summer, and winter between landform units classified based on 25 × 25 curvatures is significant (p-value < 0.05). The best performance of XGBoost models in explaining the variability in the average seasonal LST was obtained when the predictor variables were 25 × 25 curvatures (R2 values between 0.20 for spring LST and 0.11 for winter LST). XGBoost models predicted the values of average LST of the spring and summer more accurately than those of the autumn and winter. The difference between the R2 values of the XGBoost models based on the curvatures of 5 × 5 and 25 × 25 for the average LST of spring to winter is 0.14, 0.16, 0.15, and 0.11, respectively. The overall results of this research indicated the critical importance of neighborhood scale in capturing the variability of the environmental process studied in the landscape, while in many studies, the multiscale geomorphometric modeling has not been considered.

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Source: Florinsky (2016)

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Dataset and code availability

All datasets and codes used in this research are available on the https://khanifar.github.io/LSTMVs.html.

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Acknowledgements

This research is based on the datasets of Landsat 8, SRTM DEM, and meteorology. Remote sensing and machine learning analyses were performed in Google Earth Engine and Google Colaboratory. We thank all those who participated in the preparation and provision of these datasets and services.

Funding

This work was supported by Shahid Chamran University of Ahvaz [SCU.AS98.364].

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Authors and Affiliations

  1. Soil Science Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    J. Khanifar & A. Khademalrasoul

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  1. J. Khanifar
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  2. A. Khademalrasoul
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Correspondence to A. Khademalrasoul.

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The author declares no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Editorial responsibility: Agnieszka Galuszka.

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Khanifar, J., Khademalrasoul, A. Modeling of land surface temperature–multiscale curvatures relationship using XGBoost algorithm (Case study: Southwestern Iran). Int. J. Environ. Sci. Technol. 19, 11763–11774 (2022). https://doi.org/10.1007/s13762-022-04409-z

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