A study published in the journal PLOS Climate on October 2, 2024, investigates the effectiveness of land surface temperatures (LSTs) as indicators of surface air temperatures (SATs) in subtropical regions that experience seasonal wetness.
Researchers from the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science utilized satellite remote sensing data to assess how LST can reflect human heat exposure in Miami-Dade County, Florida. The findings are significant for urban heat adaptation strategies and raise questions regarding the extent to which LST captures outdoor heat exposure in this and similar regions.
The lead author of the study, Nkosi Muse, a Ph.D. candidate at the Rosenstiel School, noted that LST data, collected via satellite imaging, have traditionally been employed to estimate surface air temperatures—the temperatures experienced by people outdoors. He emphasized the importance of LSTs for understanding urban heat risks and developing adaptive strategies, especially as cities become increasingly hotter due to climate change.
The researchers highlighted that the reliability of LST as a proxy for SAT can differ based on geographical and climatic variations. While this connection has been widely studied in temperate areas, subtropical regions with significant summer rainfall have not been similarly explored.
The study specifically analyzed LST data from Landsat 8, collected from 2013 to 2022, comparing it with air temperature readings from local weather stations to determine when LST serves as an effective proxy for SAT. The results indicated seasonal fluctuations in the LST-SAT relationship, pointing to the complexities of using LST data in subtropical regions.
The research indicated that LST data effectively illustrated the heat distribution across Miami-Dade, notably demonstrating the surface urban heat island (SUHI) effect, where urban areas experience higher temperatures than rural surroundings. This effect was most evident in spring, with an average SUHI intensity of 4.09°C, unexpectedly exceeding the summer average of 3.43°C.
Interestingly, LST readings peaked in May and June, differing from the usual pattern in the northern hemisphere, where July and August are typically hottest. In contrast, SAT in Miami-Dade reached its highest in August, and the LST-SAT correlation varied widely by season. During winter, LST closely matched SAT, but this alignment faded in the wetter fall months, and no significant relationship was observed during the summer.
While LST is valuable for indicating heat patterns in urban settings, the study suggests its limitations as a gauge of actual thermal exposure, particularly in subtropical areas like Miami-Dade. The findings indicate that during the wet season, LST may not accurately reflect the heat exposure experienced by residents, especially given that data collection times (11 AM ET/12 PM EST) may miss peaks in daily heat, particularly in humid conditions.
Amy Clement, a professor of atmospheric sciences at the Rosenstiel School and co-author of the study, stressed the importance of not relying solely on LST for urban heat adaptation strategies in climates that differ from temperate zones. As cities globally face mounting challenges from heatwaves and rising temperatures, the study’s results underscore the necessity for precise measurements to accurately evaluate heat risks and formulate responses.
The implications of this research are significant for urban planners and policymakers focused on developing heat adaptation strategies in subtropical and tropical areas. As Miami-Dade County progresses with new heat policies and launches its inaugural “Heat Season Plan,” the study’s findings can inform future planning efforts.
The study warns that using LST exclusively could misrepresent heat risks, particularly during wet periods when air temperatures may notably exceed surface temperatures. This could hinder effective measures to protect vulnerable populations from extreme heat.
Moreover, the research lays the foundation for future studies examining localized processes—like vegetation, water bodies, or urban materials—that influence surface energy balances and LST readings. Understanding these dynamics could enhance the accuracy of LST as a method for measuring heat exposure in diverse urban settings.