<p>Understanding how environmental and topographic factors shape Land Surface Temperature (LST) is essential for effective land use planning and climate adaptation. This study explores the temporal dynamics of LST in relation to land use and land cover (LULC), Normalized Difference Vegetation Index (NDVI), elevation, and slope in the Küçük Menderes Basin, western Türkiye. Landsat imagery was used to derive LULC, NDVI, and LST layers were combined with slope and elevation layers generated from the DEM, and multi-layer spatial signatures were clustered using the open-source R package <i>motif</i> through hierarchical clustering with the Ward agglomeration method. This approach identified recurring landscape configurations that jointly influence LST. Results show substantial LST increased in low-elevation, gently sloping, sparsely vegetated areas, particularly where impervious surfaces expanded, while steeper and densely vegetated areas remained thermally stable, reflecting the moderating influence of topography and vegetation. Cluster validity was confirmed by internal metrics (silhouette widths, Moran’s I) and external predictive benchmarking with Gradient Boosting and Random Forest under spatial cross-validation, demonstrating the robustness and practical relevance of the clustering results. Multivariable regression, partial-correlation, and variance-partitioning analyses further corroborated the independent contributions of spatial drivers. The study highlights the novelty of the pattern-based clustering approach, which uncovers how combined spatial patterns of land use, vegetation cover, and topographic attributes shape thermal behaviour. These findings provide clear and actionable insights for detecting thermally vulnerable zones and guiding climate-resilient land management.</p>