A Method to Weaken Cloud Interference in Solar-Induced Chlorophyll Fluorescence (SIF) Reconstruction by Using Satellite VOD Observations
Solar-induced chlorophyll fluorescence (SIF) satellite observations enable large-scale crop monitoring and yield assessment. Some optical vegetation indexes have been commonly used as predictors to reconstruct SIF. However, satellite optical vegetation indexes observations are highly susceptible to...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11023846/ |
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| Summary: | Solar-induced chlorophyll fluorescence (SIF) satellite observations enable large-scale crop monitoring and yield assessment. Some optical vegetation indexes have been commonly used as predictors to reconstruct SIF. However, satellite optical vegetation indexes observations are highly susceptible to clouds, leading to degradations of EVI-based SIF reconstruction in cloud-covered situations. Unlike optical vegetation indexes, vegetation optical depth (VOD) can penetrate clouds and is highly sensitive to the changes in vegetation internal water. This study aims to investigate the potentials of VOD in reducing cloud-induced SIF reconstruction performance loss. First, a VOD-based model is established based on a dataset containing Global Ozone Monitoring Experiment-2 SIF, daily MODIS normalized bidirectional reflectance, land surface temperature, photosynthetically active radiation, and VOD data in 2015–2017. Second, comparisons between the VOD-based model and the non-VOD model are performed, and results suggest that as cloudage rises from 10% to 90%, the VOD-based SIF model reduces cloud-induced performance loss by 62% over the non-VOD model, proving that the introducing of VOD is effective in reducing cloud-induced SIF reconstruction performance loss, particularly under heavy cloudage. Finally, comparisons between the VOD-based model and the EVI-based model are performed, and results show that, in general, the VOD-based model mitigates the cloud-induced degradations in SIF reconstruction by 40% over the EVI-based model. But, under the cloudage less than 53.7%, the EVI-based model is recommended for easy access to higher-resolution optical vegetation indexes observations, and under the cloudage exceeding 53.7%, the VOD-based model is strongly recommended for its advantages in reducing cloud-induced degradation in SIF reconstruction. |
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| ISSN: | 1939-1404 2151-1535 |