Sun-induced chlorophyll fluorescence (SIF) is a promising optical remote sensing signal which is directly linked to photosynthesis, allowing for the monitoring of gross primary production (GPP). Although empirical relationships between these variables have demonstrated the potential of SIF for site-specific GPP estimations, a better physiological understanding of the link between SIF and GPP would pave the way for a more robust model of photosynthesis. The mechanistic light response (MLR) model is a novel approach which determines GPP from SIF by using only a small set of equations and parameters with physiological significance. This study combines the MLR model with the unified stomatal optimality (USO) model to estimate both GPP and transpiration (Tr) at the ecosystem scale. Top-of-canopy SIF measurements were collected over a winter crop with a field spectrometer installed next to an eddy covariance station. MLR-USO model parameters were determined from gas exchange and active chlorophyll fluorescence measurements at the leaf level and interpolated on a half-hourly basis using solar irradiance and canopy temperature. GPP and Tr estimated by the MLR-USO model and eddy covariance measurements were highly correlated at half-hourly and daily timescales (R ≥ 0.91, rRMSE ≤ 13.7%) under a wide range of environmental conditions, including soil water stress. These results highlight the potential of the MLR-USO model as an important step towards an improvement of our understanding of the coupling between the water and carbon cycles at the ecosystem scale and beyond.

中文翻译：

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使用机械光响应方法对太阳诱导的叶绿素荧光的总初级生产和蒸腾作用进行建模

太阳诱导的叶绿素荧光（SIF）是一种很有前景的光学遥感信号，它与光合作用直接相关，可用于监测总初级生产（GPP）。尽管这些变量之间的经验关系已经证明了 SIF 对于特定地点 GPP 估计的潜力，但对 SIF 和 GPP 之间联系的更好的生理理解将为更稳健的光合作用模型铺平道路。机械光响应（MLR）模型是一种新颖的方法，它仅使用一小组具有生理意义的方程和参数来从 SIF 确定 GPP。本研究将 MLR 模型与统一气孔最优性 (USO) 模型相结合，以估计生态系统规模的 GPP 和蒸腾量 (Tr)。使用安装在涡流协方差站旁边的现场光谱仪收集冬季作物的冠层顶部 SIF 测量值。 MLR-USO 模型参数是根据叶子水平的气体交换和活性叶绿素荧光测量确定的，并使用太阳辐照度和冠层温度每半小时进行插值。在包括土壤水分胁迫在内的各种环境条件下，MLR-USO 模型估计的 GPP 和 Tr 以及涡度协方差测量在每半小时和每日时间尺度上高度相关（R ≥ 0.91，rRMSE ≤ 13.7%）。这些结果凸显了 MLR-USO 模型的潜力，它是提高我们对生态系统规模及其他范围内水和碳循环之间耦合的理解的重要一步。