4.7 Article

NIRvP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales

期刊

REMOTE SENSING OF ENVIRONMENT
卷 268, 期 -, 页码 -

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ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112763

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Sun-induced chlorophyll fluorescence; SIF; Photosynthesis; Near-infrared reflectance of vegetation; NIRv; Gross primary productivity; GPP; Remote sensing

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Our study highlights the strong relationship between canopy structure and far-red SIF, indicating that the contribution of leaf physiology to SIF variability is small compared to structure and radiation components. The near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) emerges as a robust proxy for far-red SIF across different scales, showing potential for reliable vegetation monitoring at the global level.
Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIRvP captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIRvP showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIRvP data for vegetation monitoring and also indicate the potential of using SIF and NIRvP in combination to extract physiological information from SIF.

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