TY - JOUR
T1 - Monitoring agroecosystem productivity and phenology at a national scale
T2 - A metric assessment framework
AU - Browning, Dawn M.
AU - Russell, Eric S.
AU - Ponce-Campos, Guillermo E.
AU - Kaplan, Nicole
AU - Richardson, Andrew D.
AU - Seyednasrollah, Bijan
AU - Spiegal, Sheri
AU - Saliendra, Nicanor
AU - Alfieri, Joseph G.
AU - Baker, John
AU - Bernacchi, Carl
AU - Bestelmeyer, Brandon T.
AU - Bosch, David
AU - Boughton, Elizabeth H.
AU - Boughton, Raoul K.
AU - Clark, Pat
AU - Flerchinger, Gerald
AU - Gomez-Casanovas, Nuria
AU - Goslee, Sarah
AU - Haddad, Nick M.
AU - Hoover, David
AU - Jaradat, Abdullah
AU - Mauritz, Marguerite
AU - McCarty, Gregory W.
AU - Miller, Gretchen R.
AU - Sadler, John
AU - Saha, Amartya
AU - Scott, Russell L.
AU - Suyker, Andrew
AU - Tweedie, Craig
AU - Wood, Jeffrey D.
AU - Zhang, Xukai
AU - Taylor, Shawn D.
N1 - Publisher Copyright:
© 2021
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Agricultural management
KW - Eddy covariance
KW - GPP
KW - Growing season length
KW - Indicators
KW - Landsat
KW - Long-Term Agroecosystem Research (LTAR) network
KW - PhenoCam
KW - Sustainable agriculture
UR - http://www.scopus.com/inward/record.url?scp=85113699639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113699639&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2021.108147
DO - 10.1016/j.ecolind.2021.108147
M3 - Article
AN - SCOPUS:85113699639
SN - 1470-160X
VL - 131
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 108147
ER -