Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method
All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)–passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution...
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2025-01-01
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| author | Yaohai Dong Xiaodong Zhang Xiuqing Hu Jian Shang Feng Zhao |
| author_facet | Yaohai Dong Xiaodong Zhang Xiuqing Hu Jian Shang Feng Zhao |
| author_sort | Yaohai Dong |
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| description | All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)–passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time. Therefore, a gap still remains in terms of all-sky LST data with a higher temporal resolution or at other times (e.g., dawn–dusk time). Under this background, this study merged the observations of the Medium Resolution Spectrum Imager (MERSI-LL) on board the dusk–dawn-orbit Fengyun (FY)-3E satellite and Global Land Data Assimilation System (GLDAS) data to estimate dawn–dusk 1 km all-sky LST using a random forest-based method (RFRTM). The results showed that the model had good robustness, with an STD of 0.62–0.86 K of the RFRTM LST, compared with the original MERSI-LL LST. Validation against in situ LST showed that the estimated LST had an accuracy of 1.34–3.71 K under all-sky conditions. In addition, compared with the dawn–dusk LST merged from MERSI-LL and the Special Sensor Microwave Imager/Sounder (SSMI/S), the RFRTM LST showed better performance in accuracy and image quality. This study’s findings are beneficial for filling the gap in all-sky LST at high spatiotemporal resolutions for associated applications. |
| format | Article |
| id | doaj-art-a526131e78a8400b9690852fde3cf52b |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-a526131e78a8400b9690852fde3cf52b2025-01-24T13:49:10ZengMDPI AGSensors1424-82202025-01-0125250810.3390/s25020508Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) MethodYaohai Dong0Xiaodong Zhang1Xiuqing Hu2Jian Shang3Feng Zhao4Committee of Science and Technology, Shanghai Academy of Space Technology, Shanghai 201109, ChinaShanghai Spaceflight Institute of TT&C and Telecommunication, Shanghai 201109, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites (KLRCV), National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaFengYun Meteorological Satellite Innovation Center, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaShanghai Spaceflight Institute of TT&C and Telecommunication, Shanghai 201109, ChinaAll-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)–passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time. Therefore, a gap still remains in terms of all-sky LST data with a higher temporal resolution or at other times (e.g., dawn–dusk time). Under this background, this study merged the observations of the Medium Resolution Spectrum Imager (MERSI-LL) on board the dusk–dawn-orbit Fengyun (FY)-3E satellite and Global Land Data Assimilation System (GLDAS) data to estimate dawn–dusk 1 km all-sky LST using a random forest-based method (RFRTM). The results showed that the model had good robustness, with an STD of 0.62–0.86 K of the RFRTM LST, compared with the original MERSI-LL LST. Validation against in situ LST showed that the estimated LST had an accuracy of 1.34–3.71 K under all-sky conditions. In addition, compared with the dawn–dusk LST merged from MERSI-LL and the Special Sensor Microwave Imager/Sounder (SSMI/S), the RFRTM LST showed better performance in accuracy and image quality. This study’s findings are beneficial for filling the gap in all-sky LST at high spatiotemporal resolutions for associated applications.https://www.mdpi.com/1424-8220/25/2/508land surface temperatureall-sky LSTdawn–dusk timethermal infrared remote sensingreanalysis datamerging |
| spellingShingle | Yaohai Dong Xiaodong Zhang Xiuqing Hu Jian Shang Feng Zhao Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method Sensors land surface temperature all-sky LST dawn–dusk time thermal infrared remote sensing reanalysis data merging |
| title | Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method |
| title_full | Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method |
| title_fullStr | Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method |
| title_full_unstemmed | Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method |
| title_short | Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method |
| title_sort | estimation of 1 km dawn dusk all sky land surface temperature using a random forest based reanalysis and thermal infrared remote sensing data merging rfrtm method |
| topic | land surface temperature all-sky LST dawn–dusk time thermal infrared remote sensing reanalysis data merging |
| url | https://www.mdpi.com/1424-8220/25/2/508 |
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