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

Yuki Hamada,Maria Cristina Negri,Cheng-Hsien Lin,Emily Heaton,Arvid Boe,Dokyoung Lee,Jules F Cacho,Colleen R Zumpf,Robert Mitchell

doi:10.3390/land10111221

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 R2 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.

Highlights

Global efforts on the adoption of a biomass-based economy are increasing, in the European Union and the United States (U.S.), to significantly displace our dependence on fossil-based energy and products in the coming decade [1]
A major sustainability challenge of large-scale, monocultural production systems for bioenergy crops is the associated indirect land-use change that may compete for land allocated for food, feed, and fiber production [6,8]
This paper presented the initial investigation on the predictive power of spectral vegetation indices derived from optical satellite imagery, green normalized difference vegetation index (GNDVI), for at-harvest switchgrass and other warm-season perennial dry biomass yields using a linear regression model

Summary

Introduction

Global efforts on the adoption of a biomass-based economy are increasing, in the European Union and the United States (U.S.), to significantly displace our dependence on fossil-based energy and products in the coming decade [1]. The U.S has the potential to produce at least 1 billion dry tons of biomass annually from a wide range of sources, including agricultural and forest residues, dedicated energy crops, algae, and municipal solid wastes by 2040 [5]. Along with algae and additional waste streams, are expected to become a major source of biomass for a thriving future bioeconomy. The large-scale production of dedicated bioenergy crops in agricultural landscapes needs to be done both cost-effectively and sustainably in order to meet the 1-billion-ton goal in the U.S [6,7].

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