the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Tracking vegetation phenology across diverse biomes using Version 3.0 of the PhenoCam Dataset
Abstract. Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 minutes, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, GCC), and how it varies in space and time. These phenological cameras (i.e., “PhenoCams”) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (cameraNDVI), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both GCC and cameraNDVI showed similar, but somewhat unique, patterns in canopy greenness and VIS vs. NIR reflectance, across various ecosystems, indicating their consistent ability to record phenological variability. However, we did find that at most sites, GCC time series had less variability and fewer outliers, representing a smoother signal of canopy greenness and phenology. Overall, PhenoCam greenness as measured by both GCC and cameraNDVI provides expanded opportunities for studying phenology and tracking ecological changes, with potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at https://doi.org/10.3334/ORNLDAAC/2364 (Ballou et al., 2025), and the summarized phenology data are available at https://doi.org/10.3334/ORNLDAAC/2389 (Zimmerman et al., 2025).
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Status: open (until 20 May 2025)
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RC1: 'Comment on essd-2025-120', Anonymous Referee #1, 13 May 2025
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This manuscript by Young et al. presents Version 3.0 of the PhenoCam dataset, a comprehensive, multi-year, and multi-biome collection of near-surface remote sensing data for vegetation phenology monitoring. The authors provide a robust description of dataset structure, methodology, and applications, along with valuable comparisons between GCC, cameraNDVI (derived using PhenoCams), and broadbandNDVI (using typical net radiation and PAR sensors). This paper builds on and extends the PhenoCam 2.0 dataset, which is already a tremendous resource for scientists across a range of scientific communities. A new aspect of PhenoCam 3.0 is that it enables the utility of cameraNDVI data and a simplified data structure. Importantly, this work addresses not only a clear need for high-frequency, ground-based phenology observations that complement satellite datasets and support model validation but provides a well-documented community resource.
One thing that was a bit unclear to me was regarding the addition of Data Record 7 (simplified products). It may help to clarify in the main text whether these simplified files include uncertainty estimates or metadata, and if not, whether that may impact scientific use. Otherwise I only have minor comments. Overall, the paper is well-written, methodologically sound, and makes a significant contribution that is likely to be widely used. It is well-suited for publication in Earth System Science Data with minor revisions.
Around line 100 – perhaps mention the issue of clouds as well. Satellites can’t see through them, but ground based data can fill these gaps.
Line 145- The description of the simplified GCC products should briefly clarify that while uncertainty is omitted, users may refer to Data Record 5 for uncertainty quantification if needed.
Line 149 – no need to capitalize phenology here.
Line 194: Do end-users have access to the ‘exposure time’ data? Or how is this extracted? Is it a pre-set exposure, or does it automatically adjust based on irradiance conditions? Potentially some more information would be useful. ***I now see from Box 2 it’s provided in the metadata. Do all sites have all of this metadata associated with them?
Around line 220: Perhaps mention the FOV is going to be a bit different between broadbandNDVI and cameraNDVI. How so?
Figure 1: Great figure. Might be nice to also have a smaller dot in the center of each larger transparent circle so we can hone in on exactly where these data come from. Probably not needed but an idea.
Figure 3: The camera NDVI values are quite low. Especially in SH, where they are all negative. Should this be discussed? Either way, these results are impressive, and makes you wonder, do we need NDVI at all? The GCC data seem to be more dynamic and ‘sharper’. **ah yes, just as you mention around line 425.
Figure 5: Very nice, the SNR analysis is great here and gives ideas for future researchers.
Line 485 – yes, always visually inspect the data!
Note that there is a growing number of near-surface remote sensing platforms (see recent Tansley Review by Pierrat et al., 2025 https://nph.onlinelibrary.wiley.com/doi/epdf/10.1111/nph.20405 ) and it might be nice in the discussion to discuss briefly how PhenoCam can serve to compliment these data sources, or how it can be used as an example for how future networks might consider standardizing and producing user-friendly data products.
This is a great contribution, thank you for all your efforts to keep PhenoCam alive, it has had enormous benefits to the scientific community.
Citation: https://doi.org/10.5194/essd-2025-120-RC1
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