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 in | IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 2103 - 2112 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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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. |
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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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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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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 |
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