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Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.
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SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024
As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.
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- Award ID(s):
- 2425290
- PAR ID:
- 10654328
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization.more » « less
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null (Ed.)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.more » « less
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{"Abstract":["This classified_bed data product represents the radar bed classification shown in <a href="https://doi.org/10.1098/rsta.2014.0297">Young et al., 2016</a>. Values of 0 represent specularity content below 20%; values of 3.3 represent specularity content above 20% and energy 1 microsecond below the bed 15 dB lower than the bed echo, and values of 6.7 represent specularity content above 20% and energy 1 microsecond below the bed 15 dB within than the bed echo. Grids for specularity content and post bed echo are also available. Data is available as COARDS-compliant netCDF-4/HDF5 grids (.grd) and GeoTiffs (.tiff), both in EPSG 3031 (Antarctic Polar Stereographic) projection.\n<p>\n<p>\nData were gridded using <a href="https://docs.generic-mapping-tools.org/6.1/gmt.html"> GMT6.1</a> and the <a href="https://github.com/sakov/nn-c">nnbathy</a> natural neighbor interpolator. Cell size was 1 km, gaussian filter distance was 5 km, and mask radius was 2 km.\n<p>\nBrowse images, with Bedmap3 (Pritchard et al., 2025) surface elevation contours and MEASURES phase derived surface velocities (Mouginot et al. 2019) are available for each dataset.\n\n<p>\n<p>\nAn interpretation of the values in the classified_bed product is that low values are rough bed, intermediate values are isotropic wet bed, and high values are anisotropic wet bed.\n\nVersion 1 includes data from the 2016 paper, including AGASEA over Thwaites Glacier (Holt et al., 2006), ATRS over West Antarctica (Peters et al., 2005), GIMBLE over Marie Byrd Land (Young et al, 2013) and parts of ICECAP over Wilkes Subglacial Basin, Dome C, Highland B and Totten Glacier. (Young et al, 2011, Young et al., 2016). We expect updates to the coverage as part of work funded by the Arête Glaciers Initiative.\n\n<p>\n<b>References</b>\n<br>\nHolt, J. W., Blankenship, D. D., Morse, D. L., Young, D. A., Peters, M. E., Kempf, S. D., Richter, T. G., Vaughan, D. G., and Corr, H., New boundary conditions for the West Antarctic ice sheet: subglacial topography of the Thwaites and Smith Glacier catchments, 2006, Geophysical Research Letters, 33 (L09502), pp., https://doi.org/10.1029/2005GL025561\n<br>\nMouginot, J., Rignot, E., and Scheuchl, B., Continent-wide, interferometric SAR phase, mapping of Antarctic ice velocity, 2019, Geophysical Research Letters, 46(16), pp.9710-9718, https://doi.org/10.1029/2019GL083826\n<br>\nPeters, M. E., Blankenship, D. D., and Morse, D. L., Analysis techniques for coherent airborne radar sounding: Application to West Antarctic ice streams, 2005 ,Journal of Geophysical Research, 110(B06303), pp.,https://doi.org/10.1029/2004JB003222\n<br>\nPritchard, H. D., and others.,Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica,2025,Scientific Data,12(1), pp.414,https://doi.org/10.1038/s41597-025-04672-y\n<br>\nYoung, D. A., D. D. Blankenship, J. S. Greenbaum, E. Quartini, G. L. Muldoon, F. Habbal, L. E. Lindzey, C. A. Greene, E. M. Powell, G. C. Ng, T. G. Richter, G. Echeverry, and S. Kempf, 2024, Geophysical Investigations of Marie Byrd Land Lithospheric Evolution (GIMBLE) Airborne VHF Radar Transects: 2012/2013 and 2014/2015, https://doi.org/10.18738/T8/BMXUHX, Texas Data Repository\n<br>\nYoung, D. A., Wright, A. P., Roberts, J. L., Warner, R. C., Young, N. W., Greenbaum, J. S., Schroeder, D. M., Holt, J. W., Sugden, D. E., Blankenship, D. D., van Ommen, T. D., and Siegert, M. J.,A dynamic early East Antarctic Ice Sheet suggested by ice covered fjord landscapes, 2011, Nature, 474, pp.72-75, https://doi.org/10.1038/nature10114\n<br>\nYoung, D. A., Schroeder, D. M., Blankenship, D. D., Kempf, S. D., and Quartini, E.,The distribution of basal water between Antarctic subglacial lakes from radar sounding,2016,Philosophical Transactions of the Royal Society A, 374 (20140297), pp.1-21, https://doi.org/10.1098/rsta.2014.0297\n\n<p>\n<b>Change Log</b>\n<br>\nChanges from V1: changes to gridding parameters to more closely match the figures from Young 2016; updated metadata gridding description"]}more » « less
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Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.more » « less
