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

Cited 81 time in Web of Science Cited 89 time in Scopus
Authors

Dechant, Benjamin; Ryu, Youngryel; Badgley, Grayson; Kohler, Philipp; Rascher, Uwe; Migliavacca, Mirco; Zhang, Yongguang; Tagliabue, Giulia; Guan, Kaiyu; Rossini, Micol; Goulas, Yves; Zeng, Yelu; Frankenberg, Christian; Berry, Joseph A.

Issue Date
2022-01-01
Publisher
Elsevier BV
Citation
Remote Sensing of Environment, Vol.268
Abstract
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.
ISSN
0034-4257
URI
https://hdl.handle.net/10371/199149
DOI
https://doi.org/10.1016/j.rse.2021.112763
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  • College of Agriculture and Life Sciences
  • Department of Landscape Architecture and Rural System Engineering
Research Area Crop, Forest Carbon, Sensing Network, Water Cycles

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