Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau
Accurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) microwav...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10714024/ |
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| author | Yonghong Liu Fuzhong Weng Fei Tang Rui Li Yongming Xu Yang Han Jun Yang Qingyang Liu |
| author_facet | Yonghong Liu Fuzhong Weng Fei Tang Rui Li Yongming Xu Yang Han Jun Yang Qingyang Liu |
| author_sort | Yonghong Liu |
| collection | DOAJ |
| description | Accurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) microwave radiation imager in 2022, two global MLSE daily product datasets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), were obtained by using one-dimensional variational method and microwave radiative transfer method, respectively. Based on the global spatiotemporal consistency assessment, a high-quality daily MLSE training dataset for the Tibetan Plateau was selected from the two datasets. Then, ten land surface parameters from routine observation were chosen as input features to the RF model to simulate the MLSE under all-sky conditions in the Tibetan Plateau. The results show that both FY-3D1 and FY-3D2 MLSE datasets are comparable to the international mainstream MLSE products in quality, while the clear sky FY-3D1 is likely to be better than the clear/cloudy FY-3D2 MLSE. Land surface roughness, vegetation optical thickness, normalized vegetation index, and land cover type are identified as the most important factors affecting MLSE in the Tibetan Plateau. The RF model can effectively simulate the MLSE in the frequency range of 10.65–89.0 GHz under all-sky conditions. The coefficients of determination (<italic>R</italic><sup>2</sup>) for horizontal polarization and vertical polarization range from 0.86 (10.65 GHz) to 0.91 (18.7 GHz) and from 0.60 (10.65 GHz) to 0.74 (89.0 GHz), respectively. The root mean square errors for horizontal polarization and vertical polarization range from 0.017 (23.8 GHz) to 0.023 (10.65 GHz) and from 0.016 (10.65 GHz) to 0.019 (89.0 GHz), respectively. These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE. |
| format | Article |
| id | doaj-art-ede15087ce35454093f83bd20055c50d |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ede15087ce35454093f83bd20055c50d2025-08-20T02:12:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117190781909410.1109/JSTARS.2024.347835010714024Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan PlateauYonghong Liu0https://orcid.org/0000-0003-4550-7924Fuzhong Weng1Fei Tang2https://orcid.org/0000-0001-6422-3244Rui Li3https://orcid.org/0000-0002-4461-6507Yongming Xu4https://orcid.org/0000-0003-4032-8759Yang Han5https://orcid.org/0000-0003-2049-7678Jun Yang6https://orcid.org/0000-0001-9158-4328Qingyang Liu7https://orcid.org/0009-0005-0612-704XCenter for Earth System Modeling and Prediction, China Meteorological Administration, Beijing, ChinaCenter for Earth System Modeling and Prediction, China Meteorological Administration, Beijing, ChinaKey Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing, ChinaSchool of Earth and Space Sciences, State Key Laboratory of Fire Science, MEM Key Laboratory of Forest Fire Monitoring and Warning, University of Science and Technology of China, Hefei, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, ChinaCenter for Earth System Modeling and Prediction, China Meteorological Administration, Beijing, ChinaCenter for Earth System Modeling and Prediction, China Meteorological Administration, Beijing, ChinaSchool of Earth and Space Sciences, State Key Laboratory of Fire Science, MEM Key Laboratory of Forest Fire Monitoring and Warning, University of Science and Technology of China, Hefei, ChinaAccurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) microwave radiation imager in 2022, two global MLSE daily product datasets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), were obtained by using one-dimensional variational method and microwave radiative transfer method, respectively. Based on the global spatiotemporal consistency assessment, a high-quality daily MLSE training dataset for the Tibetan Plateau was selected from the two datasets. Then, ten land surface parameters from routine observation were chosen as input features to the RF model to simulate the MLSE under all-sky conditions in the Tibetan Plateau. The results show that both FY-3D1 and FY-3D2 MLSE datasets are comparable to the international mainstream MLSE products in quality, while the clear sky FY-3D1 is likely to be better than the clear/cloudy FY-3D2 MLSE. Land surface roughness, vegetation optical thickness, normalized vegetation index, and land cover type are identified as the most important factors affecting MLSE in the Tibetan Plateau. The RF model can effectively simulate the MLSE in the frequency range of 10.65–89.0 GHz under all-sky conditions. The coefficients of determination (<italic>R</italic><sup>2</sup>) for horizontal polarization and vertical polarization range from 0.86 (10.65 GHz) to 0.91 (18.7 GHz) and from 0.60 (10.65 GHz) to 0.74 (89.0 GHz), respectively. The root mean square errors for horizontal polarization and vertical polarization range from 0.017 (23.8 GHz) to 0.023 (10.65 GHz) and from 0.016 (10.65 GHz) to 0.019 (89.0 GHz), respectively. These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE.https://ieeexplore.ieee.org/document/10714024/All-skyfeature selectionFengYun-3D (FY-3D)microwave land surface emissivity (MLSE)random forest (RF) |
| spellingShingle | Yonghong Liu Fuzhong Weng Fei Tang Rui Li Yongming Xu Yang Han Jun Yang Qingyang Liu Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing All-sky feature selection FengYun-3D (FY-3D) microwave land surface emissivity (MLSE) random forest (RF) |
| title | Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau |
| title_full | Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau |
| title_fullStr | Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau |
| title_full_unstemmed | Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau |
| title_short | Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau |
| title_sort | simulations of microwave land surface emissivity using fengyun 3d microwave radiation imager data a case in the tibetan plateau |
| topic | All-sky feature selection FengYun-3D (FY-3D) microwave land surface emissivity (MLSE) random forest (RF) |
| url | https://ieeexplore.ieee.org/document/10714024/ |
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