Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems
Abstract
Abstract Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF 760 ) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 2311034
- Resource Type:
- Published Article
- Journal Name:
- Scientific Data
- Additional Journal Information:
- Journal Name: Scientific Data Journal Volume: 11 Journal Issue: 1; Journal ID: ISSN 2052-4463
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Wu, Genghong, Guan, Kaiyu, Kimm, Hyungsuk, Miao, Guofang, Yang, Xi, and Jiang, Chongya. Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems. United Kingdom: N. p., 2024.
Web. doi:10.1038/s41597-024-03004-w.
Wu, Genghong, Guan, Kaiyu, Kimm, Hyungsuk, Miao, Guofang, Yang, Xi, & Jiang, Chongya. Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems. United Kingdom. https://doi.org/10.1038/s41597-024-03004-w
Wu, Genghong, Guan, Kaiyu, Kimm, Hyungsuk, Miao, Guofang, Yang, Xi, and Jiang, Chongya. Thu .
"Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems". United Kingdom. https://doi.org/10.1038/s41597-024-03004-w.
@article{osti_2311034,
title = {Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems},
author = {Wu, Genghong and Guan, Kaiyu and Kimm, Hyungsuk and Miao, Guofang and Yang, Xi and Jiang, Chongya},
abstractNote = {Abstract Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF 760 ) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.},
doi = {10.1038/s41597-024-03004-w},
journal = {Scientific Data},
number = 1,
volume = 11,
place = {United Kingdom},
year = {Thu Feb 22 00:00:00 EST 2024},
month = {Thu Feb 22 00:00:00 EST 2024}
}
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