Biophysical feedbacks on climate depend on plant responses to stress conditions. Yet current land surface models (LSMs) still treat plant stress rudimentarily, and typically assume the same sensitivity to soil moisture for all vegetation types. There is a need therefore to investigate the dynamics of vegetation stress at the global scale, both to further understand the effect of land feedbacks on climate, as well as to improve the representation of these processes in LSMs. Solar induced fluorescence (SIF) is a subtle glow of energy emitted by vegetation during photosynthesis. Recently, satellite observations of SIF have been shown to closely mimic the spatiotemporal variability of photosynthesis. Given the nexus between photosynthesis and transpiration through the opening and closing of stomata, a link between SIF observations and evaporation can be hypothesised. Here, we introduce a novel index of evaporative stress (i.e. the ratio of actual to potential evaporation) based on satellite SIF observations, and we compare it to the estimates of evaporative stress by various LSMs from the Earth2Observe database (i.e. JULES, HTESSEL, ORCHIDEE). Results of validations against in situ evaporative stress – calculated from the FLUXNET2015 eddy-covariance archive – indicate that our SIF-based stress index outperforms the estimates of the LSMs across the majority of sites, with the exception of regions with sparse vegetation in which bare soil evaporation dominates the flux of vapour from land to atmosphere. SIF derived stress greatly outperforms over densely forested regions, and shows a high skill to capture leaf-out periods. Overall, this novel SIF application provides improvements for large-scale estimates of transpiration and can be used to further understand vegetation–atmosphere feedbacks from different ecosystem types. Furthermore, the implications of this research are relevant to (a) the hydrology and climate modelling communities, given the opportunity to utilise our SIF-based evaporative stress to benchmark model representation of the land control over the atmospheric demand for water, and (b) the remote sensing community, that will see how an observation originally intended for the study of the carbon cycle is valorized through its application to study water cycle dynamics as well.