Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Yuki Hamada, Colleen R. Zumpf, Jules F. Cacho, DoKyoung Lee, Cheng-Hsien Lin, Arvid Boe, Emily Heaton, Robert Mitchell and Maria Cristina Negri
Additional contact information
Yuki Hamada: Argonne National Laboratory, Environmental Science Division, 9700 South Cass Avenue, Lemont, IL 60439, USA
Colleen R. Zumpf: Argonne National Laboratory, Environmental Science Division, 9700 South Cass Avenue, Lemont, IL 60439, USA
Jules F. Cacho: Argonne National Laboratory, Environmental Science Division, 9700 South Cass Avenue, Lemont, IL 60439, USA
DoKyoung Lee: Crop Science Department, University of Illinois Urbana-Champaign, 1102 S Goodwin Avenue, Urbana, IL 61801, USA
Cheng-Hsien Lin: Crop Science Department, University of Illinois Urbana-Champaign, 1102 S Goodwin Avenue, Urbana, IL 61801, USA
Arvid Boe: Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Box-2140C University Station, Brookings, SD 57007, USA
Emily Heaton: Department of Agronomy, Iowa State University, 1223 Agronomy Hall, Ames, IA 50011, USA
Robert Mitchell: USDA-ARS Wheat, Sorghum, and Forage Research Unit, 251 Filley Hall, East Campus, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Maria Cristina Negri: Argonne National Laboratory, Environmental Science Division, 9700 South Cass Avenue, Lemont, IL 60439, USA

Land, 2021, vol. 10, issue 11, 1-22

Abstract: A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R 2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

Keywords: bioenergy; switchgrass yields; perennial grass; remote sensing; spectral vegetation indices; green normalized difference vegetation index; yield prediction; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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