Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited, partially due to its coarse spatial resolution (i.e., 1 km). In this study, an Adaptive random forest regression (ARFR) method was developed f...

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Published inIEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 2103 - 2112
Main Authors Ebrahimy, Hamid, Aghighi, Hossein, Azadbakht, Mohsen, Amani, Meisam, Mahdavi, Sahel, Matkan, Ali Akbar
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive random forest
Analysis
Cloud computing
downscaling
Earth
Forest management
Google Earth Engine (GEE)
Land surface temperature
land surface temperature (LST)
Landsat
Landsat satellites
Meteorology
MODIS
Regression analysis
Remote sensing
Spatial discrimination
Spatial resolution
Spatiotemporal phenomena
Spring
Spring (season)
Summer
Surface temperature
Trend analysis
Trends
Winter
Iran
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Abstract MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited, partially due to its coarse spatial resolution (i.e., 1 km). In this study, an Adaptive random forest regression (ARFR) method was developed for LST downscaling at national scale. This study also provided a framework to shift from downscaling single-time image sets to extensive time-series of MOD11A1 LST images in an operational approach (i.e., a 19-years spatiotemporal LST trend analysis over Iran) using the Google Earth Engine (GEE) cloud computing platform. The performance of ARFR was assessed by comparing the results of the downscaled LSTs with the Landsat-8 LST data on different dates of six consecutive years (2014-2019) over ten different sub-areas in Iran. The results demonstrated the effectiveness of the proposed method with an average root mean square error and mean absolute error of 2.22 °C and 1.59 °C, respectively. The results of spatiotemporal LST trend analysis showed that 25.08%, 10.05%, 56.68%, 1.04%, and 32.84% of Iran experienced significant positive trends during a full year, spring, summer, fall, and winter, respectively. Significant negative trends were also observed over the 3.09%, 23.84%, 7.54%, 17.38%, and 18.77% of Iran during a full year, spring, summer, fall, and winter, respectively. In summary, the outcomes of this study not only exhibit the spatiotemporal trends of LST across Iran, but also reveal the substantial benefits of the ARFR method in downscaling LST using GEE.
AbstractList MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited, partially due to its coarse spatial resolution (i.e., 1 km). In this study, an Adaptive random forest regression (ARFR) method was developed for LST downscaling at national scale. This study also provided a framework to shift from downscaling single-time image sets to extensive time-series of MOD11A1 LST images in an operational approach (i.e., a 19-years spatiotemporal LST trend analysis over Iran) using the Google Earth Engine (GEE) cloud computing platform. The performance of ARFR was assessed by comparing the results of the downscaled LSTs with the Landsat-8 LST data on different dates of six consecutive years (2014-2019) over ten different sub-areas in Iran. The results demonstrated the effectiveness of the proposed method with an average root mean square error and mean absolute error of 2.22 °C and 1.59 °C, respectively. The results of spatiotemporal LST trend analysis showed that 25.08%, 10.05%, 56.68%, 1.04%, and 32.84% of Iran experienced significant positive trends during a full year, spring, summer, fall, and winter, respectively. Significant negative trends were also observed over the 3.09%, 23.84%, 7.54%, 17.38%, and 18.77% of Iran during a full year, spring, summer, fall, and winter, respectively. In summary, the outcomes of this study not only exhibit the spatiotemporal trends of LST across Iran, but also reveal the substantial benefits of the ARFR method in downscaling LST using GEE.
Author Aghighi, Hossein
Azadbakht, Mohsen
Matkan, Ali Akbar
Amani, Meisam
Mahdavi, Sahel
Ebrahimy, Hamid
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Snippet MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited,...
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SubjectTerms Adaptive random forest
Analysis
Cloud computing
downscaling
Earth
Forest management
Google Earth Engine (GEE)
Land surface temperature
land surface temperature (LST)
Landsat
Landsat satellites
Meteorology
MODIS
Regression analysis
Remote sensing
Spatial discrimination
Spatial resolution
Spatiotemporal phenomena
Spring
Spring (season)
Summer
Surface temperature
Trend analysis
Trends
Winter
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  providerName: IEEE
Title Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran
URI https://ieeexplore.ieee.org/document/9321543
https://www.proquest.com/docview/2486591987
https://doaj.org/article/84dd03758ed1449e85b2e98fd87da359
Volume 14
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