Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework
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...
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2021
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oai:doaj.org-article:71f69c22561c40999b13bf6e8f2759a72021-12-01T04:59:42ZMonitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework1470-160X10.1016/j.ecolind.2021.108147https://doaj.org/article/71f69c22561c40999b13bf6e8f2759a72021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21008128https://doaj.org/toc/1470-160XEffective 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.Dawn M. BrowningEric S. RussellGuillermo E. Ponce-CamposNicole KaplanAndrew D. RichardsonBijan SeyednasrollahSheri SpiegalNicanor SaliendraJoseph G. AlfieriJohn BakerCarl BernacchiBrandon T. BestelmeyerDavid BoschElizabeth H. BoughtonRaoul K. BoughtonPat ClarkGerald FlerchingerNuria Gomez-CasanovasSarah GosleeNick M. HaddadDavid HooverAbdullah JaradatMarguerite MauritzGregory W. McCartyGretchen R. MillerJohn SadlerAmartya SahaRussell L. ScottAndrew SuykerCraig TweedieJeffrey D. WoodXukai ZhangShawn D. TaylorElsevierarticleAgricultural managementEddy covarianceGPPGrowing season lengthIndicatorsLong-Term Agroecosystem Research (LTAR) networkEcologyQH540-549.5ENEcological Indicators, Vol 131, Iss , Pp 108147- (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Agricultural management Eddy covariance GPP Growing season length Indicators Long-Term Agroecosystem Research (LTAR) network Ecology QH540-549.5 |
| spellingShingle |
Agricultural management Eddy covariance GPP Growing season length Indicators Long-Term Agroecosystem Research (LTAR) network Ecology QH540-549.5 Dawn M. Browning Eric S. Russell Guillermo E. Ponce-Campos Nicole Kaplan Andrew D. Richardson Bijan Seyednasrollah Sheri Spiegal Nicanor Saliendra Joseph G. Alfieri John Baker Carl Bernacchi Brandon T. Bestelmeyer David Bosch Elizabeth H. Boughton Raoul K. Boughton Pat Clark Gerald Flerchinger Nuria Gomez-Casanovas Sarah Goslee Nick M. Haddad David Hoover Abdullah Jaradat Marguerite Mauritz Gregory W. McCarty Gretchen R. Miller John Sadler Amartya Saha Russell L. Scott Andrew Suyker Craig Tweedie Jeffrey D. Wood Xukai Zhang Shawn D. Taylor Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework |
| description |
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. |
| format |
article |
| author |
Dawn M. Browning Eric S. Russell Guillermo E. Ponce-Campos Nicole Kaplan Andrew D. Richardson Bijan Seyednasrollah Sheri Spiegal Nicanor Saliendra Joseph G. Alfieri John Baker Carl Bernacchi Brandon T. Bestelmeyer David Bosch Elizabeth H. Boughton Raoul K. Boughton Pat Clark Gerald Flerchinger Nuria Gomez-Casanovas Sarah Goslee Nick M. Haddad David Hoover Abdullah Jaradat Marguerite Mauritz Gregory W. McCarty Gretchen R. Miller John Sadler Amartya Saha Russell L. Scott Andrew Suyker Craig Tweedie Jeffrey D. Wood Xukai Zhang Shawn D. Taylor |
| author_facet |
Dawn M. Browning Eric S. Russell Guillermo E. Ponce-Campos Nicole Kaplan Andrew D. Richardson Bijan Seyednasrollah Sheri Spiegal Nicanor Saliendra Joseph G. Alfieri John Baker Carl Bernacchi Brandon T. Bestelmeyer David Bosch Elizabeth H. Boughton Raoul K. Boughton Pat Clark Gerald Flerchinger Nuria Gomez-Casanovas Sarah Goslee Nick M. Haddad David Hoover Abdullah Jaradat Marguerite Mauritz Gregory W. McCarty Gretchen R. Miller John Sadler Amartya Saha Russell L. Scott Andrew Suyker Craig Tweedie Jeffrey D. Wood Xukai Zhang Shawn D. Taylor |
| author_sort |
Dawn M. Browning |
| title |
Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework |
| title_short |
Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework |
| title_full |
Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework |
| title_fullStr |
Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework |
| title_full_unstemmed |
Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework |
| title_sort |
monitoring agroecosystem productivity and phenology at a national scale: a metric assessment framework |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/71f69c22561c40999b13bf6e8f2759a7 |
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