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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #417945
Research Project: Knowledge Systems and Tools to Increase the Resilience and Sustainability of Western Rangeland Agriculture

Location: Range Management Research

Title: Interannual variability governs performance of near-surface vegetation greenness to predict productivity and evapotranspiration across diverse agroecosystems

Author
item Denham, Sander
item Browning, Dawn
item Dalzell, Brent
item Huggins, David
item Flerchinger, Gerald
item Clark, Patrick
item Goslee, Sarah
item GRIFFS, TIMOTHY - University Of Minnesota
item Hoover, David
item LITVAK, MARCY - University Of New Mexico
item MARITZ, MARGUERITE - University Of Texas - El Paso
item Phillips, Claire
item Scott, Russell
item Schreiner-Mcgraw, Adam
item BRACHO, ROSVEL - University Of Florida
item SILVEIRA, MARIA - University Of Florida
item Whippo, Craig

Submitted to: American Geophysical Union Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2024
Publication Date: 12/13/2025
Citation: Denham, S.O., Browning, D.M., Dalzell, B.J., Huggins, D.R., Flerchinger, G.N., Clark, P., Goslee, S.C., Griffs, T., Hoover, D.L., Litvak, M., Maritz, M., Phillips, C.L., Scott, R.L., Schreiner-Mcgraw, A.P., Bracho, R., Silveira, M., Whippo, C.W. 2025. Interannual variability governs performance of near-surface vegetation greenness to predict productivity and evapotranspiration across diverse agroecosystems. American Geophysical Union Meeting Abstract. Abstract.

Interpretive Summary:

Technical Abstract: Climate change is increasingly impacting agroecosystems. Improved methods of both monitoring and modeling carbon and water dynamics are needed to better understand how these systems are responding to these changes in climate. Modeling efforts take different forms (e.g., empirical, mechanistic, machine learning) and within these models, remotely-sensed vegetation indices (VI) such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), in conjunction with other hydrometeorological drivers, are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET). Although VIs and meteorological drivers are effective predictors of ecosystem production, how the relationship between VI and GPP or ET varies through space and time has not yet been thoroughly evaluated. Co-located digital cameras (PhenoCams) and eddy covariance (EC) flux towers enabled us to identify nuances of the coupling between a PhenoCam-derived VI and fluxes at sites in the Long-Term Agroecosystem Research (LTAR) network. We synthesized long-term records (130 site years, 34 sites) of near-surface green chromatic coordinate (GCC) derived from digital images and GPP/ET using eddy covariance at LTAR sites to quantify the interannual variability (IAV) in the strength of the relationship of GPP/ET as a function of GCC across diverse agroecosystems of the conterminous United States. We uncover a high degree of variability between years and across production system types in the strength of GCC to inform daily GPP/ET (R2 ranging from <0.1 to >0.9), with shrub-dominated rangelands exhibiting the greatest IAV. We propose that incorporating more dynamic representations of VI that capture subtle changes in vegetation which may not be possible at coarser scales in ecosystem models, particularly in certain production types, will aid in our ability to more accurately predict the magnitude of ecosystem processes.