Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO₂ and H₂O flux data, and to study climate change impacts on the terrestrial biosphere.

Summary: Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO₂ flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.

In recent decades, the use of satellite sensors, near‐surface cameras, and other remote methods for monitoring vegetation phenology at landscape and higher scales has become increasingly common. These technologies provide a means to determine the timing of phenophases and growing season length at different spatial resolutions; coverage that is not attainable by human observers. However, in situ ground observations are required to validate remotely derived phenometrics. Despite increased knowledge and expertise there still remains the persistent challenge of reconciling ground observations at the individual plant level with remotely sensed (RS) phenometrics at landscape or larger scales. We compared the timing of in situ phenophase estimates (spring and autumn) with a range of corresponding remote sensing (moderate resolution imaging spectroradiometer [MODIS], visible infrared imaging radiometer suite [VIIRS], PhenoCam) phenometrics across five terrestrial sites in the United States' NEON (Harvard Forest [MA] [HARV], Onaqui [UT] [ONAQ], Abby Road [WA] [ABBY], Disney Wilderness Preserve [FL] [DSNY], and Ordway‐Swisher Biological Station [FL] [OSBS]) focusing on the 3‐year period from 2017 to 2019. Our main objective was to explore potential reasons for the observed discrepancies between in situ and RS phenometrics and to determine which technologies were better able to capture ground observations. Statistically significant relationships were strongest (p < 0.001) for spring phenophases, while the only RS phenometrics significantly correlated with in situ estimates of autumn phenophases were leaf fall (p < 0.01) and leaves (p < 0.000). In particular, root mean square error (RMSE) (mean bias error [MBE]) for MODIS‐Enhanced Vegetation Index‐2 band (EVI2), VIIRS‐EVI2, and PhenoCam‐green chromatic coordinate (GCC) derived early spring transition dates indicated overall differences of 21.7 days (−4.6 days), 28.4 days (−1.2 days), and 24.1 days (11.9 days) from in situ estimates of early leaf‐out dates. In autumn, RMSE/MBE was smallest (10.9 days/−2.2 days) between phenesse estimates (95th percentile date) of the latest date of in situ leaf fall and VIIRS derived end of senescence, compared to the equivalent phenometric derived from MODIS (13.5 days/7.7 days) and PhenoCam (GCC greenness‐falling) (13.8 days/−5.1 days). Overall, discrepancies between in situ and RS phenology related to scale, species availability, and the short duration of the time series (3 years). However, as the NEON project progresses these challenges are expected to be reduced as more data become available.