@article{eff3e5d312ec47279733dda988673447,
title = "NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales",
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.",
keywords = "GPP, Gross primary productivity, NIRv, Near-infrared reflectance of vegetation, Photosynthesis, Remote sensing, SIF, Sun-induced chlorophyll fluorescence",
author = "Benjamin Dechant and Youngryel Ryu and Grayson Badgley and Philipp K{\"o}hler and Uwe Rascher and Mirco Migliavacca and Yongguang Zhang and Giulia Tagliabue and Kaiyu Guan and Micol Rossini and Yves Goulas and Yelu Zeng and Christian Frankenberg and Berry, \{Joseph A.\}",
note = "This study was funded by National Research Foundation of Korea ( NRF-2019R1A2C2084626 ). We thank a large number of colleagues for contributing to the site datasets as well as the airborne and satellite SIF products. For the site-level SIF and NIR V measurements, we would like to acknowledge the support from Kaige Yang, Jongmin Kim, Genghong Wu, Guofang Miao, Hyungsuk Kim, Zhaohui Li, Qian Zhang, Fabrice Daumard, and David Martini. We also thank many colleagues for providing the site-level GPP data, in particular, Minseok Kang, Andy Suyker, Guofang Miao, Ji Li, Hezhou Wang, Tarek El-Madany, and Olivier Marloie. We are also grateful for the contributions of Chongya Jiang to the BESS modelling platform and thank Yulin Yan, Seungjoon Lee, Bolun Li, and Juwon Kong for downloading and processing MODIS data. We thank Bastian Siegmann for providing useful information and additional data related to the HyPlant dataset. Furthermore, we thank Martin Jung and Ulrich Weber for sharing the FLUXCOM products and Yao Zhang and Pierre Gentine for making the CSIF dataset publicly available. We also thank three anonymous reviewers for providing comments that helped improve the manuscript. This work is a contribution to the LEMONTREE (Land Ecosystem Models based On New Theory, obseRvations and ExperimEnts) project, funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program (YR).",
year = "2022",
month = jan,
doi = "10.1016/j.rse.2021.112763",
language = "English (US)",
volume = "268",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",
}