Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data

• Published in: Remote sensing of environment 2020-06, Vol.242, p.111791, Article 111791
• Main Authors: Venter, Zander S., Brousse, Oscar, Esau, Igor, Meier, Fred
• Format: Article
• Language: English
• Subjects: Air temperature; Biodiversity; Buffer zones; Climate change; Climate models; Crowdsourcing; Detection; Disease control; Energy consumption; Environmental impact; Extreme heat; Extreme high temperatures; Health risks; Heat waves; High resolution; Infectious diseases; Land surface temperature; Landsat; Landsat satellites; Lidar; Local climates; Mapping; Meteorological data; Model accuracy; Remote control; Remote sensing; Satellite; Satellites; Sentinel; Spatial variations; Surface temperature; Urban air; Urban areas; Urban climates; Urban heat island; Urban planning; Urbanization; Vector-borne diseases; Weather; Weather stations; Wind speed; Wind velocities
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2020.111791

The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and urban biodiversity. Remote sensing of urban climate has been focused on land surface temperature (LST) due to a scarcity of data on Tair which is usually interpolated at 1 km resolution. We assessed the efficacy of mapping hyperlocal Tair (spatial resolutions of 10–30 m) over Oslo, Norway, by integrating Sentinel, Landsat and LiDAR data with crowd-sourced Tair measurements from 1310 private weather stations during 2018. Using Random Forest regression modelling, we found that annual mean, daily maximum and minimum Tair can be mapped with an average RMSE of 0.52 °C (R2 = 0.5), 1.85 °C (R2 = 0.05) and 1.46 °C (R2 = 0.33), respectively. Mapping accuracy decreased sharply with