Articles | Volume 15, issue 3
https://doi.org/10.5194/amt-15-735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2022
Research article |
| 09 Feb 2022
| 09 Feb 2022
A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations
Shihan Chen
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Yuanjian Yang
CORRESPONDING AUTHOR
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Fei Deng
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Yanhao Zhang
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Duanyang Liu
Key Laboratory of Transportation Meteorology, China Meteorological
Administration, Nanjing 210008, China
China Meteorological Administration, Nanjing Joint Institute For Atmospheric Sciences, Nanjing 210008, China
Chao Liu
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Zhiqiu Gao
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
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Latest update: 13 Dec 2024
Short summary
This paper proposes 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.
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by...
