Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2
Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan o...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/2/424 |
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| author | Zuomin Xu Ruijing Sun Shuang Wu Jiali Shao Jie Chen |
| author_facet | Zuomin Xu Ruijing Sun Shuang Wu Jiali Shao Jie Chen |
| author_sort | Zuomin Xu |
| collection | DOAJ |
| description | Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets. |
| first_indexed | 2024-03-08T10:35:50Z |
| format | Article |
| id | doaj.art-d96012ba6cf74a638ef067c9f2014591 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T10:35:50Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d96012ba6cf74a638ef067c9f20145912024-01-26T18:20:17ZengMDPI AGRemote Sensing2072-42922024-01-0116242410.3390/rs16020424Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2Zuomin Xu0Ruijing Sun1Shuang Wu2Jiali Shao3Jie Chen4Heilongjiang Eco-Meteorology Center, Heilongjiang Meteorological Bureau, Harbin 150030, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, ChinaHeilongjiang Eco-Meteorology Center, Heilongjiang Meteorological Bureau, Harbin 150030, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, ChinaMicrowave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets.https://www.mdpi.com/2072-4292/16/2/424FY-3D/MWRIGCOM-W1/AMSR2brightness temperaturecalibration |
| spellingShingle | Zuomin Xu Ruijing Sun Shuang Wu Jiali Shao Jie Chen Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2 Remote Sensing FY-3D/MWRI GCOM-W1/AMSR2 brightness temperature calibration |
| title | Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2 |
| title_full | Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2 |
| title_fullStr | Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2 |
| title_full_unstemmed | Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2 |
| title_short | Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2 |
| title_sort | inter calibration of passive microwave satellite brightness temperature observations between fy 3d mwri and gcom w1 amsr2 |
| topic | FY-3D/MWRI GCOM-W1/AMSR2 brightness temperature calibration |
| url | https://www.mdpi.com/2072-4292/16/2/424 |
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