Retrieval of All-Sky Land Surface Temperature from MERSI-II/FY-3D Data.
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| Title: | Retrieval of All-Sky Land Surface Temperature from MERSI-II/FY-3D Data. |
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| Authors: | Zhang, Han-Hao1 (AUTHOR), Jiang, Geng-Ming1,2 (AUTHOR) jianggm@fudan.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1954. 22p. |
| Subjects: | Land surface temperature, Boosting algorithms, Algorithms, Artificial satellites, Energy budget (Geophysics) |
| Abstract: | Highlights: What are the main findings? An improved split-window algorithm is developed using numerical radiative transfer simulation experiments and is successfully applied to retrieving accurate clear-sky land surface temperature (LST) from MERSI-II/FY-3D data. A hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and surface energy balance theory is proposed to estimate cloudy-sky LSTs from MERSI-II/FY-3D data. The XGBoost model is used to estimate hypothetical clear-sky LSTs, while the surface energy balance theory is employed to correct cloud radiation effect, enabling accurate LST retrieval under cloudy-sky conditions. What are the implications of the main findings? The results of clear-sky LST retrieval indicate that MERSI-II/FY-3D data are reliable and can be used to produce clear-sky LSTs at a level comparable to well-established satellite products. This matters because it strengthens confidence in using FY-3D as an independent or complementary data source, which is valuable for continuity when MODIS data are unavailable or for cross-validation in long-term climate records. The combination of machine learning (XGBoost) with surface energy balance theory demonstrates a successful fusion of data-driven and physics-based approaches. This is important because purely statistical models often lack physical interpretability, while purely physical models struggle under complex conditions like clouds. The hybrid method effectively reconstructs LST under cloudy-sky conditions with good accuracy. This shows that combining the two can improve not only retrieval accuracy but also spatial coverage. Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R2) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R2 of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? An improved split-window algorithm is developed using numerical radiative transfer simulation experiments and is successfully applied to retrieving accurate clear-sky land surface temperature (LST) from MERSI-II/FY-3D data. A hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and surface energy balance theory is proposed to estimate cloudy-sky LSTs from MERSI-II/FY-3D data. The XGBoost model is used to estimate hypothetical clear-sky LSTs, while the surface energy balance theory is employed to correct cloud radiation effect, enabling accurate LST retrieval under cloudy-sky conditions. What are the implications of the main findings? The results of clear-sky LST retrieval indicate that MERSI-II/FY-3D data are reliable and can be used to produce clear-sky LSTs at a level comparable to well-established satellite products. This matters because it strengthens confidence in using FY-3D as an independent or complementary data source, which is valuable for continuity when MODIS data are unavailable or for cross-validation in long-term climate records. The combination of machine learning (XGBoost) with surface energy balance theory demonstrates a successful fusion of data-driven and physics-based approaches. This is important because purely statistical models often lack physical interpretability, while purely physical models struggle under complex conditions like clouds. The hybrid method effectively reconstructs LST under cloudy-sky conditions with good accuracy. This shows that combining the two can improve not only retrieval accuracy but also spatial coverage. Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R2) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R2 of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121954 |
