Extreme summertime heat is an increasing challenge for cities, highlighting the need to measure and map temperature in ways that reflect human thermal experiences and inform land management decisions. Mean radiant temperature ( \({T}_{mrt}\) ) is a key metric for assessing urban heat at hyper-local scales, yet its measurement remains technically challenging. In this study, we apply the six-directional gold standard method for measuring \({T}_{mrt}\) alongside globe thermometer-based approaches across multiple levels of spatial aggregation and develop a novel machine learning model trained on field data. Data were collected in a semi-arid city in Colorado, USA, over two summers. Using measurements from residential parcels, we show that aggregated globe thermometer data—collected using a low-cost, accessible sensor—can capture thermal patterns across landscapes with reasonable accuracy. Our findings also indicate that machine learning, combining six-directional and globe thermometer data, has potential to improve both measurement accuracy and efficiency. This work advances practical methods using both low and high-cost micrometeorology instruments to address socio-ecological questions in cities.
