This research examines the relationship between urban physical characteristics and land surface temperature within discrete thermal

pixels captured by Landsat 8 imagery in Guadalajara, Mexico. The city’s extensive collection of high-quality geospatial data enables

urban thermal analysis with high granularity. We integrate multiple datasets: a LiDAR-derived urban tree inventory with individual

tree metrics; building footprints with roof material classifications; green space polygons; transportation infrastructure including roads

and sidewalks; and water body delineations. Our methodology focuses on the pixel level—for each 30-meter thermal pixel (900

square meters), we precisely quantify all urban features within its boundaries, creating a comprehensive dataset where each pixel

contains measurements of all elements present. This spatial integration enables multivariate regression modeling using machine

learning, where predictor variables represent the quantity of each urban element and the target variable is pixel temperature. Using

interpretable machine learning techniques, we quantify each element’s influence on thermal patterns, achieving R² values ranging

from 0.69 to 0.83 across different urban contexts. This pixel-level approach provides granular understanding of urban thermal

dynamics, explaining which factors influence land surface temperature and specifying what characteristics new developments should

incorporate to achieve desired LST conditions, contributing to evidence-based urban planning.