Bridging accuracy and efficiency: Advancing mean radiant temperature measurement in Urban Ecology

Extreme summertime heat is an increasing challenge for cities, highlighting the need for accurate, spatially meaningful methods to measure and map heat 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 with 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, offers promising potential for improving both measurement accuracy and efficiency. These findings are particularly relevant for planners working at the scale of parcels, where heat adaptation strategies are commonly applied, and especially insightful for semi-arid cities and those increasingly experiencing arid summer conditions due to climate change. This work advances practical methods for integrating human thermal comfort into landscape planning for climate-resilient urban design.