Trees participate in mitigating the urban heat island phenomenon thanks to their transpiration and shading. This cooling potential is highly dependent on leaf area. Nevertheless, leaf traits potentially vary across different land management practices in urban settings, thereby challenging the models used to estimate thermal budgets. The present study aims to investigate the variability of leaf area traits of linden (Tilia spp.) urban trees, and their effect on simulated tree transpiration. Reconstruction of the leaf area was undertaken at the tree scale at three different urban land management sites from three cities: London and Birmingham (UK) and Chantilly (France). The reconstruction combined allometric measurements at shoot and leaf scales, and a tree-scale 3D digitization with laboratory analysis using field data collected by citizen scientists. The management practices had a significant impact on leaf area, and on tree allometric relationships, which were propagated through the reconstruction process. Relative differences between the management practices ranged between 12% and 48% according to the city where the variable was considered (e.g., leaf area index, total leaf area, or tree transpiration). Trees in managed sites (i.e., individualized leaf crowns, frequent leaf litter removal, and standard thinning/pruning operations) develop a higher leaf area, thus promoting cooling potential. This study shows that the variability of leaf traits, and their responses to different land management, can be studied by comprehensive data collection through citizen science and lab-based modelling. It also highlights the importance of appropriate, well-designed urban planning, where landscaping using urban trees can play an even better role in climate proofing cities.

In their recent study, Sanchez et al. compared various cloud detection methods applied to Sentinel-2, specifically on images acquired over the Amazonian region, known for its frequent cloud cover. Comparison of cloud screening methods for optical satellite images is a complex task, which must take several parameters into account, such as the definition of a cloud, which can differ according to the methods, the different coding of the cloud and shadow masks, the possible dilation of masks, and also the way the method must be used to perform in nominal conditions. We found that the otherwise serious and useful comparison of cloud masks by Sanchez et al. is not fair to the real performances of MAJA cloud detection, for two reasons: (i) two thirds of the images used in the comparison were acquired before the launch of Sentinel-2B satellite, when the revisit of the Sentinel-2 mission was 20 days instead of five days for the nominal conditions of the mission, and (ii) there is an error in the understanding of how MAJA cloud masks are coded which also probably artificially degraded the results of MAJA as compared to the other methods.