A new approach to LST modeling and normalization under clear-sky conditions based on a local optimization strategy

The normalization of LST relative to environmental parameters is of great importance in various environmental applications. The purpose of this study was to develop a new approach for LST normalization relative to environmental variables. These included topographic variables (i.e. solar irradiance a...

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Bibliographic Details
Published in:International journal of digital earth 2022-12, Vol.15 (1), p.1833-1854
Main Authors: Kiavarz, Majid, Firozjaei, Mohammad Karimi, Alavipanah, Seyed Kazem, Hassan, Quazi K., Malbéteau, Yoann, Duan, Si-Bo
Format: Article
Language:English
Subjects:
Albedo
Anomalies
Anthropogenic factors
Coefficients
Environmental factors
environmental variables
Global optimization
Heat islands
Irradiance
land surface temperature
Lapse rate
Least squares method
Local optimization
local strategy
Modelling
Normalization
Optimization
partial least squares
random forest
Regression coefficients
Surface temperature
Urban heat islands
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Summary:The normalization of LST relative to environmental parameters is of great importance in various environmental applications. The purpose of this study was to develop a new approach for LST normalization relative to environmental variables. These included topographic variables (i.e. solar irradiance and near-surface temperature lapse rate (NSTLR)) as well as biophysical properties (i.e. vegetation, wetness, and albedo). The study was conducted in two phases, namely (1) using global and (2) local optimization strategies to calculate the regression coefficients of environmental variables in the partial least squares regression (PLSR) and build the non-linear linking model in the random forest regression (RFR). The RMSEs between actual LST and modeled LST based on the global and local optimization strategies using PLSR (RFR) were 2.202 (0.935) and 0.939 (0.835) °C, respectively. The results showed that RFR had higher efficiency than PLSR in normalizing LST. Moreover, the local optimization method outperformed the global optimization method in terms of normalization accuracy. The results of this study could be very useful in many environmental applications such as identifying thermal anomalies, and surface anthropogenic heat island modeling.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2022.2137254