Preprints
https://doi.org/10.5194/amt-2021-301
https://doi.org/10.5194/amt-2021-301

  27 Oct 2021

27 Oct 2021

Review status: this preprint is currently under review for the journal AMT.

A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

Shihan Chen1,2, Yuanjian Yang2, Fei Deng1, Yanhao Zhang2, Duanyang Liu3,4, Chao Liu2, and Zhiqiu Gao2 Shihan Chen et al.
  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
  • 2Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 3Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210008, China
  • 4Nanjing Joint Institute For Atmospheric Sciences, Nanjing 210008, China

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Shihan Chen et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-301', Anonymous Referee #3, 03 Nov 2021
  • RC2: 'Comment on amt-2021-301', Anonymous Referee #2, 17 Nov 2021

Shihan Chen et al.

Shihan Chen et al.

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Short summary
This paper propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. The spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.