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Plant traits are often measured in the field or laboratory to characterize stress responses. However, direct measurements are not always cost effective for broader sampling efforts, whereas indirect approaches such as reflectance spectroscopy could offer efficient and scalable alternatives. Here, we used field spectroscopy to assess whether (1) existing vegetation indices could predict leaf trait responses to heat stress, or if (2) partial least squares regression (PLSR) spectral models could quantify these trait responses. On several warm, sunny days, we measured leaf trait responses indicative of photosynthetic mechanisms, plant water status, and morphology, including electron transport rate (ETR), photochemical quenching (qP), leaf water potential (Ψleaf), and specific leaf area (SLA) in 51 urban trees from nine species. Concurrent measures of hyperspectral leaf reflectance from the same individuals were used to calculate vegetation indices for correlation with trait responses. We found that vegetation indices predicted only SLA robustly (R2 = 0.55), while PLSR predicted all leaf trait responses of interest with modest success (R2 = 0.36 to 0.58). Using spectral band subsets corresponding to commercially available drone-mounted hyperspectral cameras, as well as those selected for use in common multispectral satellite missions, we were able to estimate ETR, qP, and SLA with reasonable accuracy, highlighting the potential for large-scale prediction of these parameters. Overall, reflectance spectroscopy and PLSR can identify wavelengths and wavelength ranges that are important for remote sensing-based modeling of important functional trait responses of trees to heat stress over broad ranges. 

Abstract Tree cover is generally associated with cooler air temperatures in urban environments but the roles of canopy configuration, spatial context, and time of day are not well understood. The ability to examine spatiotemporal relationships between trees and urban climate has been hindered by lack of appropriate air temperature data and, perhaps, by overreliance on a single ‘tree canopy’ class, obscuring the mechanisms by which canopy cools. Here, we use >70 000 air temperature measurements collected by car throughout Washington, DC, USA in predawn (pd), afternoon (aft), and evening (eve) campaigns on a hot summer day. We subdivided tree canopy into ‘soft’ (over unpaved surfaces) and ‘hard’ (over paved surfaces) canopy classes and further partitioned soft canopy into distributed (narrow edges) and clumped patches (edges with interior cores). At each level of subdivision, we predicted air temperature anomalies using generalized additive models for each time of day. We found that the all-inclusive ‘tree canopy’ class cooled linearly at every time (pd = 0.5 °C ± 0.3 °C, aft = 1.8 °C ± 0.6 °C, eve = 1.7 °C ± 0.4 °C), but could be explained in the afternoon by aggregate effects of predominant hard and soft canopy cooling at low and high canopy cover, respectively. Soft canopy cooled nonlinearly in the afternoon with minimal effect until ∼40% cover but strongly (and linearly) across all cover fractions in the evening (pd = 0.7 °C ± 1.1 °C, aft = 2.0 °C ± 0.7 °C, eve = 2.9 °C ± 0.6 °C). Patches cooled at all times of day despite uneven allocation throughout the city, whereas more distributed canopy cooled in predawn and evening due to increased shading. This later finding is important for urban heat island mitigation planning since it is easier to find planting spaces for distributed trees rather than forest patches.