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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This content will become publicly available on September 1, 2026
Utility of near‐surface phenology in estimating productivity and evapotranspiration across diverse ecosystems
Abstract Agroecosystems, which include row crops, pasture, and grass and shrub grazing lands, are sensitive to changes in management, weather, and genetics. To better understand how these systems are responding to changes, we need to improve monitoring and modeling carbon and water dynamics. Vegetation Indices (VIs) are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET), but these empirical relationships are often location and crop specific. There is a need to evaluate if VIs can be effective and, more general, predictors of ecosystem processes through time and across different agroecosystems. Near‐surface photographic (red‐green‐blue) images from PhenoCam can be used to calculate the VI green chromatic coordinate (GCC) and offer a pathway to improve understanding of field‐scale relationships between VIs and GPP and ET. We synthesized observations spanning 76 site‐years across 15 agroecosystem sites with PhenoCam GCCand GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and GCCacross. We uncovered a high degree of variability in the strength and slopes of the GCC∼ GPP and ET relationships (R2 = 0.1 ‐ 0.9) within and across production systems. Overall, GCCis a better predictor of GPP than ET (R2 = 0.64 and 0.54, respectively), performing best in croplands (R2 = 0.91). Shrub‐dominated systems exhibit the lowest predictive power of GCCfor GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of GCCcould provide an alternative approach for predictions of ecosystem processes.
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- Award ID(s):
- 2425290
- PAR ID:
- 10654351
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of Environmental Quality
- Volume:
- 54
- Issue:
- 5
- ISSN:
- 0047-2425
- Page Range / eLocation ID:
- 1245 to 1257
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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