Leaf temperature is an elementary driver of plant physiology, ecology and ecosystem productivity. Individual leaf temperature may deviate strongly from air temperature, and may vary throughout the canopy. Measurements of leaf temperature, conducted at a high spatial and temporal resolution, can improve our understanding of leaf water loss, stomatal conductance, photosynthetic rates, phenology, and atmosphere-ecosystem exchanges. However, continuous high-resolution measurement of leaf temperature outside of a controlled environment is difficult and rarely done. Here, thermal infrared cameras are used to measure leaf temperatures. We describe two long-term field measurement sites: one in a temperature deciduous forest, and the other in a subalpine conifer forest. The considerations and constraints for deploying such cameras are discussed and the temperature errors are typically +/–1 °C or smaller (σ = 0.60 °C, 2σ = 1.20 °C). Lastly, we compare leaf temperature by species and height at hourly to multi-seasonal timescales and show that on average, leaf temperature is warmer than air temperature in a temperate forest. Leaf temperature can be uniform or heterogeneous across a scene, depending on canopy structure, leaf habit, and meteorology. With this data, we verify that leaf temperature follows classic expectations, yet exhibits noteworthy departures that require additional study and theoretical consideration.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.