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 GCC and GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and GCC across. We uncovered a high degree of variability in the strength and slopes of the GCC similar to GPP and ET relationships (R2 = 0.1 - 0.9) within and across production systems. Overall, GCC is 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 GCC for GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of GCC could provide an alternative approach for predictions of ecosystem processes.