The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between Tair and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between Tair and LST measurements of the UHI.
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This content will become publicly available on May 1, 2025
Spatial-temporal modeling of the relationship between surface temperature and air temperature in metropolitan urban systems
Research about urban local climate and urban heat island often relies on land surface temperature
(LST) data to characterize the distribution of temperature near the surface. Although using
remotely sensed data for such work has the advantage of continuous spatial coverage at regular
temporal intervals, it is recognized that surface temperature is not an ideal proxy for air temperature
(AT). This study's goal is to develop a spatiotemporal model revealing the relationship
between LST and AT within the complexities of the urban environment. A mobile weather
monitoring unit was used to collect spatially-explicit fine-scale AT data while Landsat 8 and 9
passed overhead collecting LST data. A spatiotemporal model of the relationship between LST and
AT in Philadelphia was constructed with this data utilizing basis functions to account for spatial
and temporal autocorrelation. The spatiotemporal model results show a strong relationship between
LST and AT and indicate that it is possible to predict fine scale AT (120 m) using remotely
sensed LST in an urban context (r-squared = 0.99, RMSE = 0.89 ◦C). The spatiotemporal model
outperforms models that do not account for spatial and temporal autocorrelation, highlighting the
importance of considering these dependencies in temperature modeling. City-wide AT predictions
were generated for Philadelphia demonstrating the ability of the model to improve understanding
of local urban climate.
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- Award ID(s):
- 1832407
- NSF-PAR ID:
- 10513598
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Urban Climate
- Volume:
- 55
- Issue:
- C
- ISSN:
- 2212-0955
- Page Range / eLocation ID:
- 101921
- Subject(s) / Keyword(s):
- Air temperature Surface temperature Spatiotemporal modeling Spatial autocorrelation Temporal autocorrelation Urban heat
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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