Modeling Land Surface Temperature Variability in a Semi-Arid Region of North India Using Spatial Regression Techniques

This study assesses Land Use and Land Cover (LULC) and Land Surface Temperature (LST) variability in the semi-arid Aligarh District, Uttar Pradesh, India, using two Landsat 9 images acquired on 30 May 2022 (summer) and 24 December 2022 (winter). This research employs a semi-automated hybrid classification approach integrating Maximum Likelihood Classification and Object-Based Image Analysis with Landsat imagery to map seasonal LULC for summer and winter 2022. Five classes, namely, cropland, built-up areas, barren land, plantations, and water bodies, were identified with overall accuracies of 94.4% (Kappa = 0.93) in summer and 91.2% (Kappa = 0.89) in winter. On the summer date LST ranged from 26.48 °C to 46.40 °C and on the winter date from 13.11 °C to 23.85 °C. Inter-predictor Pearson correlation analysis revealed severe multicollinearity among NDVI, SAVI, NDBI, and NDWI, with inter-predictor correlations ranging from |r| = 0.855 to 0.994, forming a tightly redundant spectral cluster. NDVI and SAVI were consequently excluded from the final OLS model. The reduced model using NDBI, BSI, and NDWI explains 54.3% of LST variability in summer and 57.8% in winter. NDBaI was excluded from the regression models because it is computed from the thermal band that is also used to retrieve LST, whereas BSI uses reflective bands only. NDBI is the dominant predictor in both seasons, followed by BSI as a significant secondary predictor, confirming built-up surface intensity and bare soil exposure as the primary thermal drivers. In contrast, GWR substantially improved model fit (Summer: R2 = 0.895, Winter: R2 = 0.918) and reduced the residual spatial autocorrelation (Moran’s I from 0.404 to 0.028 in summer and from 0.299 to 0.033 in winter), showing its effectiveness in capturing spatial heterogeneity. LST patterns differed markedly between the two acquisition dates in association with LULC differences, confirming the utility of spatially adaptive modeling for climate-responsive planning. The findings provide evidence for the scientific basis of sustainable urban management and climate adaptation strategies, contributing to the implementation of Sustainable Development Goal 13 (Climate Action).