Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Dawn M Browning,Nicole Kaplan,Elizabeth H Boughton,Xukai Zhang,John Sadler,Andrew D Richardson,Nick M Haddad,Eric S Russell,Sheri Spiegal,Amartya Saha,Raoul K Boughton,Shawn D Taylor,Joseph G Alfieri,Marguerite Mauritz,Gregory W Mccarty,Brandon T Bestelmeyer,Russell L Scott,Bijan Seyednasrollah,J D Wood ,Abdullah A Jaradat ,G R Miller ,Carl J Bernacchi ,Nicanor Z Saliendra ,David L Hoover ,Nuria Gomez‐Casanovas ,G N Flerchinger ,David D Bosch ,John M Baker ,P K Clark ,Sarah C Goslee ,Guillermo E Ponce‐Campos ,Andrew E Suyker ,C E Tweedie

doi:10.1016/j.ecolind.2021.108147

Abstract

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.

Highlights

An accurate understanding of agroecosystem dynamics is critical for the design of management and policy strategies in a changing climate
We focused on measuring phenology - defined as the timing of recurring events such as germination and flowering, green-up, or senescence - as it is a key multiscale attribute in agroecosystems that is sensitive to management and climate change
Our specific research questions were: Among sensors, what is the correlation between phenology metrics [start of season (SOS), end of season (EOS), season length, and related daily and annual estimates of timing and amount of production]? Do differences in temporal and spatial scale of sensor metrics account for variability in SOS and EOS? Does the degree of site heterogeneity correlate with variation in pairwise comparisons of metrics?

Summary

Introduction

An accurate understanding of agroecosystem dynamics is critical for the design of management and policy strategies in a changing climate. Climate change can alter growing seasons, water availability, and pro duction potential (Tracy et al, 2018). These changes may vary across agroecosystems spanning a wide range of climates, operation scales, production commodities, management practices, and stakeholder per ceptions. Monitoring agroecosystems from pasture or field-level to landscape and regional scales is necessary to inform management and policy decisions. Day-to-day management decisions at the field level, may be best served by groundbased sensors that validate and verify satellite-derived metrics and provide real-time, fine-scale estimates of crop or forage status (Browning et al, 2015, Fritz et al, 2019). We have little understanding of the varying relationships among sensor platforms in agroecosystems at a national scale, which vary strongly in the amount and timing of pro duction, intensity of management, and degree of spatial heterogeneity

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Conclusion