Details

Autor(en) / Beteiligte

• Scolio, Madeline
• Kremer, Peleg
• Zhang, Yimin
• Shakya, Kabindra M.

Titel

Spatial-temporal modeling of the relationship between surface temperature and air temperature in metropolitan urban systems

Ist Teil von

• Urban climate, 2024-05, Vol.55, Article 101921

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. •LST data is often used as a proxy for AT data in studies of urban climate.•Access to AT data is a barrier to modeling efforts.•Mobile monitoring can be used to collect large amounts of AT data.•LST data can be used to predict spatially fine-scale AT in urban environments.•Spatial and temporal autocorrelation must be considered in AT modeling efforts.