Novel strategies are required to reduce uncertainties in the assessment of ecosystem transpiration (T). A major problem in modelling T is related to the complexity of constraining canopy stomatal resistance (r sc), accounting for the main biological controls on T besides non biological controls such as aerodynamic resistances or energy constraints. The novel Earth observation signal sun-induced chlorophyll fluorescence (SIF) is the most direct measure of plant photosynthesis and offers new pathways to advance estimates of T. The potential of using SIF to study ecosystem T either empirically or in combination with complex mechanistic models has already been demonstrated in recent studies. The diversity of environmental drivers determining diurnal and seasonal dynamics in T and SIF independently requires additional investigation to guide further developments towards robust SIF-informed T retrievals. This study consequently aims to identify relevant biotic and abiotic environmental drivers affecting the capability of SIF to inform estimates of ecosystem T. We used observational data from a temperate mixed forest during the leaf-on period and a Penman-Monteith (PM) based modelling framework, and observed varying sensitivities of SIF-informed approaches for diurnal and seasonal T dynamics (i.e. r 2 from 0.52 to 0.58 and rRMSD from 17 to 19%). We used the PM based modelling framework to investigate systematically the sensitivity of SIF to diurnal and seasonal variations in r sc when empirically and mechanistically embedded in the models. We used observations and the Soil-Vegetation-Atmosphere-Transfer model SCOPE to study the dependence of SIF and T on abiotic and biotic environmental drivers including net radiation, air temperature, relative humidity, wind speed, and leaf area index. We conclude on the potential of SIF to advance estimates of T and suggest preferring more sophisticated modelling frameworks constrained with SIF and other Earth observation data over the single use of SIF to assess reliably ecosystem T across scales.
- Abiotic and biotic change driver
- Eddy covariance
- Stomatal resistance