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Abstract. Despite the significant contribution of biogenic volatileorganic compounds (BVOCs) to organic aerosol formation and ozone productionand loss, there are few long-term, year-round, ongoing measurements oftheir volume mixing ratios and quantification of their impacts onatmospheric reactivity. To address this gap, we present 1 year of hourlymeasurements of chemically resolved BVOCs between 15 September 2019 and15 September 2020, collected at a research tower in Central Virginiain a mixed forest representative of ecosystems in the Southeastern US.Mixing ratios of isoprene, isoprene oxidation products, monoterpenes, andsesquiterpenes are described and examined for their impact on the hydroxyradical (OH), ozone, and nitrate reactivity. Mixing ratios of isoprene rangefrom negligible in the winter to typical summertime 24 h averages of 4–6 ppb, while monoterpenes have more stable mixing ratios in the range of tenths of a part per billion up to ∼2 ppb year-round. Sesquiterpenes aretypically observed at mixing ratios of <10 ppt, but this representsa lower bound in their abundance. In the growing season, isoprene dominatesOH reactivity but is less important for ozone and nitrate reactivity.Monoterpenes are the most important BVOCs for ozone and nitrate reactivitythroughout the year and for OH reactivity outside of the growing season. Tobetter understand the impact of this compound class on OH, ozone, andnitrate reactivity, the role of individual monoterpenes is examined. Despitethe dominant contribution of α-pinene to total monoterpene mass, theaverage reaction rate of the monoterpene mixture with atmospheric oxidantsis between 25 % and 30 % faster than α-pinene due to thecontribution of more reactive but less abundant compounds. A majority ofreactivity comes from α-pinene and limonene (the most significantlow-mixing-ratio, high-reactivity isomer), highlighting the importance ofboth mixing ratio and structure in assessing atmospheric impacts ofemissions. 

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.