The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity
Sun-induced chlorophyll fluorescence (SIF) is an important indicator of vegetation photosynthesis. While remote sensing enables large-scale monitoring of SIF, existing products face the challenge of trade-offs between temporal and spatial resolutions, limiting their applications. To select the optim...
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MDPI AG
2025-07-01
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| author | Chenyu Hu Pinhua Xie Zhaokun Hu Ang Li Haoxuan Feng |
| author_facet | Chenyu Hu Pinhua Xie Zhaokun Hu Ang Li Haoxuan Feng |
| author_sort | Chenyu Hu |
| collection | DOAJ |
| description | Sun-induced chlorophyll fluorescence (SIF) is an important indicator of vegetation photosynthesis. While remote sensing enables large-scale monitoring of SIF, existing products face the challenge of trade-offs between temporal and spatial resolutions, limiting their applications. To select the optimal model for SIF data downscaling, we used a consistent dataset combined with vegetation physiological and meteorological parameters to evaluate four different regression methods in this study. The XGBoost model demonstrated the best performance during cross-validation (R<sup>2</sup> = 0.84, RMSE = 0.137 mW/m<sup>2</sup>/nm/sr) and was, therefore, selected to downscale GOME-2 SIF data. The resulting high-resolution SIF product (HRSIF) has a temporal resolution of 8 days and a spatial resolution of 0.05° × 0.05°. The downscaled product shows high fidelity to the original coarse SIF data when aggregated (correlation = 0.76). The reliability of the product was ensured through cross-validation with ground-based and satellite observations. Moreover, the finer spatial resolution of HRSIF better matches the footprint of eddy covariance flux towers, leading to a significant improvement in the correlation with tower-based gross primary productivity (GPP). Specifically, in the mixed forest vegetation type with the best performance, the R<sup>2</sup> increased from 0.66 to 0.85, representing an increase of 28%. This higher-precision product will support more effective ecosystem monitoring and research. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-797e3a8a2df6469481083f83401bd0c72025-08-20T03:02:58ZengMDPI AGRemote Sensing2072-42922025-07-011715264210.3390/rs17152642The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem ProductivityChenyu Hu0Pinhua Xie1Zhaokun Hu2Ang Li3Haoxuan Feng4School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, ChinaSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, ChinaKey Laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaKey Laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, ChinaSun-induced chlorophyll fluorescence (SIF) is an important indicator of vegetation photosynthesis. While remote sensing enables large-scale monitoring of SIF, existing products face the challenge of trade-offs between temporal and spatial resolutions, limiting their applications. To select the optimal model for SIF data downscaling, we used a consistent dataset combined with vegetation physiological and meteorological parameters to evaluate four different regression methods in this study. The XGBoost model demonstrated the best performance during cross-validation (R<sup>2</sup> = 0.84, RMSE = 0.137 mW/m<sup>2</sup>/nm/sr) and was, therefore, selected to downscale GOME-2 SIF data. The resulting high-resolution SIF product (HRSIF) has a temporal resolution of 8 days and a spatial resolution of 0.05° × 0.05°. The downscaled product shows high fidelity to the original coarse SIF data when aggregated (correlation = 0.76). The reliability of the product was ensured through cross-validation with ground-based and satellite observations. Moreover, the finer spatial resolution of HRSIF better matches the footprint of eddy covariance flux towers, leading to a significant improvement in the correlation with tower-based gross primary productivity (GPP). Specifically, in the mixed forest vegetation type with the best performance, the R<sup>2</sup> increased from 0.66 to 0.85, representing an increase of 28%. This higher-precision product will support more effective ecosystem monitoring and research.https://www.mdpi.com/2072-4292/17/15/2642Sun-induced chlorophyll fluorescencemachine learningXGBoost modeldownscaling |
| spellingShingle | Chenyu Hu Pinhua Xie Zhaokun Hu Ang Li Haoxuan Feng The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity Remote Sensing Sun-induced chlorophyll fluorescence machine learning XGBoost model downscaling |
| title | The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity |
| title_full | The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity |
| title_fullStr | The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity |
| title_full_unstemmed | The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity |
| title_short | The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity |
| title_sort | downscaled gome 2 sif based on machine learning enhances the correlation with ecosystem productivity |
| topic | Sun-induced chlorophyll fluorescence machine learning XGBoost model downscaling |
| url | https://www.mdpi.com/2072-4292/17/15/2642 |
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