Modeling gross primary production and transpiration from sun-induced chlorophyll fluorescence using a mechanistic light-response approach

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 2 ≥ 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.