Natural Resources, School of



Dawn M. Browning, USDA Agricultural Research ServiceFollow
Eric S. Russell, Washington State University Pullman
Guillermo E. Ponce-Campos, The University of Arizona
Nicole Kaplan, USDA Agricultural Research Service
Andrew D. Richardson, Northern Arizona University
Bijan Seyednasrollah, Northern Arizona University
Sheri Spiegal, USDA Agricultural Research Service
Nicanor Saliendra, USDA Agricultural Research Service
Joseph G. Alfieri, USDA ARS Beltsville Agricultural Research Center
John Baker, USDA Agricultural Research Service
Carl Bernacchi, USDA Agricultural Research Service
Brandon T. Bestelmeyer, USDA Agricultural Research Service
David Bosch, United States Department of Agriculture
Elizabeth H. Boughton, Archbold Biological Station
Raoul K. Boughton, Archbold Biological Station
Pat Clark, USDA Agricultural Research Service
Gerald Flerchinger, USDA Agricultural Research Service
Nuria Gomez-Casanovas, University of Illinois Urbana-Champaign
Sarah Goslee, USDA Agricultural Research Service
Nick M. Haddad, Michigan State University
David Hoover, USDA Agricultural Research Service
Abdullah Jaradat, USDA Agricultural Research Service
Marguerite Mauritz, The University of Texas at El Paso
Gregory W. McCarty, USDA ARS Beltsville Agricultural Research Center
Gretchen R. Miller, Texas A&M University
John Sadler, University of Missouri
Amartya Saha, Archbold Biological Station
Russell L. Scott, USDA ARS Carl Hayden Bee Research Center
Andrew Suyker, University of Nebraska - LincolnFollow
Craig Tweedie, Texas A&M University
Jeffrey D. Wood, University of Missouri
Xukai Zhang, Archbold Biological Station
Shawn D. Taylor, USDA Agricultural Research Service

Date of this Version



Ecological Indicators 131 (2021) 108147



This is an open access article under the CC BY license


Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous 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) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, 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. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics 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 grazing land sites where estimating SOS can be challenging. 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. Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.