Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

Pengcheng Qi; Yan Cui; Haijun Zhang; Shixiong Hu; Lunguang Yao; Larry Bailian Li

doi:10.1109/ACCESS.2020.3021034

IEEE Access (Jan 2020)

Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

Pengcheng Qi,
Yan Cui,
Haijun Zhang,
Shixiong Hu,
Lunguang Yao,
Larry Bailian Li

Affiliations

Pengcheng Qi: ORCiD; Collaborative Innovation Center of Water Security for Water Source Region of Mid-Line of South-to-North Diversion Project of Henan Province, Nanyang Normal University, Nanyang, China
Yan Cui: Collaborative Innovation Center of Water Security for Water Source Region of Mid-Line of South-to-North Diversion Project of Henan Province, Nanyang Normal University, Nanyang, China
Haijun Zhang: Collaborative Innovation Center of Water Security for Water Source Region of Mid-Line of South-to-North Diversion Project of Henan Province, Nanyang Normal University, Nanyang, China
Shixiong Hu: Department of History and Geography, East Stroudsburg University of Pennsylvania, East Stroudsburg, PA, USA
Lunguang Yao: Collaborative Innovation Center of Water Security for Water Source Region of Mid-Line of South-to-North Diversion Project of Henan Province, Nanyang Normal University, Nanyang, China
Larry Bailian Li: Department of Botany and Plant Sciences, Ecological Complexity and Modeling Laboratory, University of California–Riverside, Riverside, CA, USA

DOI: https://doi.org/10.1109/ACCESS.2020.3021034
Journal volume & issue: Vol. 8
pp. 162085 – 162098

Abstract

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For research and practice in fields such as the environment and meteorology, nighttime land surface temperature (LST) images at fine resolution provide important basic data. Since only a few satellite sensors can take fine-resolution thermal infrared images at night, such images are rather scarce. Downscaling of coarse-resolution LST images is a potential method for obtaining high-resolution LST images. However, downscaling methods have been mostly proposed for daytime LST images. This study aimed to evaluate the performance of methods that combine multiple explanatory variables and machine learning algorithms for downscaling coarse-resolution nighttime LST images in urban areas. Verification showed that the errors in the downscaling results were acceptable (mean absolute errors within 2 K). The resulting images could depict the spatial pattern of night LST in the study area in great detail. It was demonstrated that visible-near infrared images taken in the daytime could be used for downscaling of nighttime LST images, the rationality of which was deduced. It was also demonstrated that the performance of the spectral explanatory variables in nighttime LST downscaling was not lower than that of the mechanism explanatory variables. This method has high application value in many academic and practical efforts, such as land-atmosphere interface radiation budget studies, suitability assessments of human settlement, and urban environmental planning.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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