Articles | Volume 15, issue 3
https://doi.org/10.5194/amt-15-735-2022
https://doi.org/10.5194/amt-15-735-2022
Research article
 | 
09 Feb 2022
Research article |  | 09 Feb 2022

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao

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Akdemir, S. and Tagarakis, A.: Investigation of Spatial Variability of Air Temperature, Humidity and Velocity in Cold Stores by Using Management Zone Analysis, Tarim Bilim. Derg., 20, 175–186, https://doi.org/10.1501/Tarimbil_0000001277, 2014. 
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Alonso, L. and Renard, F.: Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns, Urban Science, 3, 101, https://doi.org/10.3390/urbansci3040101, 2019. 
Alonso, L. and Renard, F.: A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models, Remote Sens., 12, 2434, https://doi.org/10.3390/rs12152434, 2020. 
An, N., Dou, J., González-Cruz, J. E., Bornstein, R. D., Miao, S., and Li, L.: An Observational Case Study of Synergies between an Intense Heat Wave and the Urban Heat Island in Beijing, J. Appl. Meteorol. Clim., 59, 605–620, https://doi.org/10.1175/jamc-d-19-0125.1, 2020. 
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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.
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