Effective measurement of seasonal variations in the timing and amount of production are critical to managing spatially heterogeneous agroecosystems in a changing climate. Although many technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in 34 cropland, grazingland, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. We analyzed production dynamics estimated from three independent ground and remote platforms for 51 site-years of co-located time series.
We found that when similar methods were applied across the four metrics, pairwise sensor comparisons revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazingland sites where estimating SOS can be challenging and inter-annual variability in production is high. Growing season length estimates derived from 16-day Satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj < 0.0001) and EC GPP (79.6 days, padj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam GCC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC m-2 yr-1. We identified production systems where management and climate variability mattered most and offered ideas we feel can advance our capacity to better integrate management decision-making into long- and short-term forecasting for drought and climate scenarios at multiple spatial scales.