Evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence
Evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence
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DOI：
10.1088/1748-9326/ac9dae
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发表时间：
2022-10
影响因子：
6.7
通讯作者：
Rui Cheng;T. Magney;Erica L Orcutt;Z. Pierrat;P. Köhler;D. Bowling;M. Bret-Harte;E. Euskirchen;M. Jung;Hideki Kobayashi;A. Rocha;O. Sonnentag;J. Stutz;Sophia Walther;D. Zona;C. Frankenberg
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作者：
Rui Cheng;T. Magney;Erica L Orcutt;Z. Pierrat;P. Köhler;D. Bowling;M. Bret-Harte;E. Euskirchen;M. Jung;Hideki Kobayashi;A. Rocha;O. Sonnentag;J. Stutz;Sophia Walther;D. Zona;C. Frankenberg
北极-北方地区陆地生态系统的光合作用是全球碳循环的重要组成部分。太阳诱导的叶绿素荧光(SIF)是一种具有生理意义的光合作用指标，已被用来跟踪区域尺度上的总初级生产力(GPP)。最近的研究已经建立了SIF和涡旋协方差GPP之间的经验关系，作为预测全球GPP的第一步。然而，高纬度带来了两个具体的挑战：(A)北极-北方地区独特的植物物种和土地覆盖类型不包括在较低纬度的广义SIF-GPP关系中，以及(B)复杂的地形和亚像素的土地覆盖进一步使SIF-GPP关系的解释复杂化。在这项研究中，我们专注于北极-北方脆弱性实验(上面)领域，并评估了来自对流层监测仪(Tropomi)的高纬度SIF与最先进的机器学习GPP产品(FlosCom)之间的经验关系。我们首次报道了具有广泛空间覆盖的北极-北方土地覆盖类型的回归斜率、线性相关系数和SIF-GPP关系的拟合优度。我们发现了北极-北方地区特有的几个值得考虑的潜在问题：(A)由于亚像素尺度的雪和水的存在，导致流量系数GPP不切实际地高；(B)沿海拔梯度的生物量分布和SIF-GPP关系的变化；以及(C)对不同空间分辨率的异质土地覆盖的有限视角和错误描述。综上所述，我们的结果将有助于改进在陆地生物圈模型中使用SIF估计GPP的方法，并处理北极-北方地区模型数据的不确定性。
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.