ArticleScientific data2019
Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Decoupled carbon assimilation and growth responses to aridity in temperate deciduous oaks.Science advances · 2026Article
- Recurrent hot droughts cause persistent legacy effects in a temperate Scots Pine forest.Plant biology (Stuttgart, Germany) · 2026Article
- Discrepancy in Phenological Indicators from COPlants (Basel, Switzerland) · 2025Article
- Cooling outweighs warming across phenological transitions in the Northern Hemisphere.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Proximal remote sensing: an essential tool for bridging the gap between high-resolution ecosystem monitoring and global ecology.The New phytologist · 2025Review
- Genomics highlight an underestimation of phenology sensitivity to the urban heat island effect.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- LiPheStream - A 18-month high spatiotemporal resolution point cloud time series of Boreal trees from Finland.Scientific data · 2024Article
- Signatures of autumn deluges revealed during spring drought in a semi-arid grassland.Oecologia · 2024Article
- HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data.Scientific data · 2023Article
- Climate-driven shifts in leaf senescence are greater for boreal species than temperate species in the Acadian Forest region in contrast to leaf emergence shifts.Ecology and evolution · 2023Article
- Camera trapping in ecology: A new section for wildlife research.Ecology and evolution · 2023Article
- Understanding Organisms Using Ecological Observatory Networks.Integrative organismal biology (Oxford, England) · 2023Article
- Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications.Global change biology · 2022Review
- A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites.Scientific data · 2022Article
- Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.Scientific reports · 2022Article
- Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.Remote sensing · 2021Article
- Carbon response of tundra ecosystems to advancing greenup and snowmelt in Alaska.Nature communications · 2021Article
- Deep learning and citizen science enable automated plant trait predictions from photographs.Scientific reports · 2021Article
- Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery.Scientific reports · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 CO
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