## 1 Introduction  
Plant transpiration dominates the global flux of terrestrial evaporation and is a central element of the hydrologic cycle over land [1; 2]. The transpiration flux depends on plant phenology and is mediated by stomatal conductance, by which plants tend to optimize to maximize carbon uptake while reducing water losses [3; 4; 5; 6]. As such, the biophysical and biochemical feedbacks on climate depend on vegetation phenology and plant physiological response to environmental conditions and nutrient cycling [7; 8; 9; 10]. These responses vary among plant species and make the modelling of transpiration particularly challenging [11; 12; 13]. More specifically, the physiological response to water stress remains a longstanding modelling challenge [14], which may ultimately be addressed by incorporating plant hydraulics into land surface models (LSMs) [15; 16; 17]. However, perhaps unsurprisingly, current LSMs still constrain transpiration based on empirical relationships between stomatal conductance and environmental variables such as soil moisture, temperature and vapor pressure deficit [18; 19; 20]. These formulations are supported by limited observational evidence at global scales, and their assumptions affect the modelled heat and drought response of ecosystem transpiration, which remains uncertain in current models [7; 21; 22]. The effect of water stress on transpiration is typically introduced in LSMs through a dimensionless empirical factor based on an assumed relationship between soil moisture and stomatal conductance, net photosynthesis, mesophyll conductance and/or carboxylation rate [21; 23; 24; 25]. Pure hydrologic and remote-sensing models--which have no explicit photosynthesis and coupled stomatal dependence--use similar empirical water-stress functions to constrain the potential transpiration estimates from Penman-Monteith or Priestley and Taylor approaches [26; 27; 28; 29; 30]. Two-source energy balance models utilize remotely sensed surface temperatures to constrain transpiration, but often still rely on Penman-Monteith, Priestley and Taylor, or aerodynamic formulations [31], or solve for latent heat as the residual term in the energy balance [32; 33]. Given (a) the importance of transpiration for global hydrology and climate, (b) the dependency of transpiration on phenology and physiological response to environmental stress, and (c) the difficulties representing this response in models, there is a need to unravel these processes at a global scale from an observational perspective. Due to the scarcity and limited coverage of in situ observations, satellite retrievals appear as an attractive means to monitor transpiration constraints, unravel the implications in terms of land feedbacks on climate, and improve the representation of transpiration in LSMs. However, current efforts to study transpiration dynamics based on satellite data are hampered by the inability to detect this flux directly from space [13; 34] and are thus limited to process-based modelling [35; 36] or machine-learning algorithms [37] run with observed environmental drivers of the flux. Moreover, even when these methods estimate the total terrestrial evaporation reliably, the transpiration component of the flux remains highly uncertain [35; 38]. Solar-induced chlorophyll fluorescence (SIF) is a byproduct of photosynthesis: a small fraction of light, initially absorbed by chlorophyll pigments, that is re-emitted as a subtle glow of energy in the spectral range of 650-800 nm [39; 40]. The intensity of the SIF signal is indicative of the functioning of the photosynthetic machinery [41]. As water lost through transpiration and carbon uptake through photosynthesis is synchronized by stomatal opening, and is sensitive to similar environmental drivers, negative anomalies in SIF are expected to integrate the effects of different environmental stressors on transpiration [42; 43; 44]. Spaceborne datasets of global SIF have become available in recent years [45; 46; 47; 48; 49; 50].