Abstract
Transpiration (T) returns about half of continental precipitation back into the atmosphere. However, the global spatial and temporal dynamics of transpiration are highly uncertain, and current estimates rely on either indirect remote sensing or empirical model formulations. Here, we show that T can be estimated reliably at the global scale using observations of plant sun-induced fluorescence (SIF). To do so, we derive T using two different methods from globally-distributed eddy-covariance measurements and compare it with satellite SIF retrievals from GOME-2 and OCO-2. Whereas most research to date has focused on the link between SIF and gross primary production (GPP), we demonstrate that SIF is as highly correlated with T (mean correlation coefficient R of 0.76 across sites for 16-day periods with GOME-2 and 0.75 at the daily scale with OCO-2). SIF shows a greater predictive skill to estimate T than traditional optical vegetation indices and its dynamics are very similar to those of T. Through the use of an advanced radiative transfer model, we also demonstrate a clear empirical link between SIF and T. At 83 FLUXNET sites, remote sensing data and flux-derived GPP and T are used to estimate the relevant parameters of the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) radiative transfer model and to model SIF. While the relationship between SIF and photosynthesis (GPP) is mostly controlled by leaf biochemical properties and plant structure, the SIF–T relationship appears largely determined by air temperature and intrinsic water use efficiency. Our findings suggest that ongoing advances in satellite SIF retrievals will allow for a more direct estimation of transpiration over large scales.
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