Argonne's initial study shows great promise for using optical remote sensing to forecast perennial bioenergy grass yields
Scientists from Argonne's Environmental Science Division and colleagues from the University of Illinois Urbana-Champaign, South Dakota State University, Iowa State University, and the U.S. Department of Agriculture's Agricultural Research Service have just published an article entitled “Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy” in the open-access journal Land: Special Issue on Remote Sensing Analysis of Agricultural Landscapes, from the study supported by the U.S. Department of Energy's Bioenergy Technologies Office.
Growing high-yield bioenergy crops on marginal agricultural areas while growing commodity crops in productive areas could help us realize a sustainable bioeconomy. To determine the cost competitiveness and environmental sustainability of such production systems, researchers must be able to reliably estimate biomass yield. The marginal areas — which are often small and spread across the landscape — make a traditional cut-bale-weigh approach to estimate yields costly and time‑consuming. In this article, the team presents their initial investigation of optical remote sensing to predict perennial bioenergy grass yields using a linear regression model with data from five Midwest field sites. Although additional testing is needed, the study showed great promise for an optical remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.