Evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence

Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales....

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Vydáno v:Environmental research letters Ročník 17; číslo 11; s. 115009 - 115026
Hlavní autoři: Cheng, Rui, Magney, Troy S, Orcutt, Erica L, Pierrat, Zoe, Köhler, Philipp, Bowling, David R, Bret-Harte, M Syndonia, Euskirchen, Eugénie S, Jung, Martin, Kobayashi, Hideki, Rocha, Adrian V, Sonnentag, Oliver, Stutz, Jochen, Walther, Sophia, Zona, Donatella, Frankenberg, Christian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.11.2022
Témata:
Arctic land cover
Arctic zone
Biosphere
Carbon cycle
Chlorophyll
Correlation coefficient
Correlation coefficients
Fluorescence
FluxCom GPP
Land cover
Latitude
Machine learning
Monitoring instruments
Photosynthesis
Pixels
Plant species
Primary production
snow
solar-induced chlorophyll fluorescence (SIF)
surface water
Terrestrial ecosystems
topography
Arctic region
ISSN:1748-9326, 1748-9326
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Popis
Shrnutí:Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales. Recent studies have constructed empirical relationships between SIF and eddy covariance-derived GPP as a first step to predicting global GPP. However, high latitudes pose two specific challenges: (a) Unique plant species and land cover types in the Arctic–Boreal region are not included in the generalized SIF-GPP relationship from lower latitudes, and (b) the complex terrain and sub-pixel land cover further complicate the interpretation of the SIF-GPP relationship. In this study, we focused on the Arctic-Boreal vulnerability experiment (ABoVE) domain and evaluated the empirical relationships between SIF for high latitudes from the TROPOspheric Monitoring Instrument (TROPOMI) and a state-of-the-art machine learning GPP product (FluxCom). For the first time, we report the regression slope, linear correlation coefficient, and the goodness of the fit of SIF-GPP relationships for Arctic-Boreal land cover types with extensive spatial coverage. We found several potential issues specific to the Arctic-Boreal region that should be considered: (a) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; (b) changing biomass distribution and SIF-GPP relationship along elevational gradients, and (c) limited perspective and misrepresentation of heterogeneous land cover across spatial resolutions. Taken together, our results will help improve the estimation of GPP using SIF in terrestrial biosphere models and cope with model-data uncertainties in the Arctic-Boreal region.
Bibliografie:ERL-114365.R2
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/ac9dae