Plants play a crucial role in mediating the interactions between the land and atmosphere. However, it is unclear how these interactions might shift with changing climate, such as drier air, increased temperatures or changing rainfall patterns.

Advances in remote sensing (e.g., LST, SIF), experimental manipulation studies (e.g., imposed drought), and the growing number of in situ measurements (e.g., eddy-covariance), can now be combined with machine learning, statistics and models, to understand how plants react to climate extremes. This can be used to improve plant-stress representation in models (e.g., CMIP6), and in-turn climate predictions.

Here, we encourage contributions that advance understanding of vegetation-stress and its climate feedbacks. This includes studies i) using new techniques for monitoring plant-stress, ii) evaluating, calibrating, and improving plant-stress representation in models, iii) using climate models to understand how plant-stress impacts boundary layer process and surface temperature, and iv) investigating plant-stress with changing climate through manipulation experiments.