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

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...

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Autores principales:	Yuki Hamada, Colleen R. Zumpf, Jules F. Cacho, DoKyoung Lee, Cheng-Hsien Lin, Arvid Boe, Emily Heaton, Robert Mitchell, Maria Cristina Negri
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bioenergy switchgrass yields perennial grass remote sensing spectral vegetation indices green normalized difference vegetation index Agriculture S
Acceso en línea:	https://doaj.org/article/def433421c0c4af29401e2a1ff99dc43
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spelling	oai:doaj.org-article:def433421c0c4af29401e2a1ff99dc432021-11-25T18:09:46ZRemote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy10.3390/land101112212073-445Xhttps://doaj.org/article/def433421c0c4af29401e2a1ff99dc432021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1221https://doaj.org/toc/2073-445XA 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 <i>R</i><sup>2</sup> 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.Yuki HamadaColleen R. ZumpfJules F. CachoDoKyoung LeeCheng-Hsien LinArvid BoeEmily HeatonRobert MitchellMaria Cristina NegriMDPI AGarticlebioenergyswitchgrass yieldsperennial grassremote sensingspectral vegetation indicesgreen normalized difference vegetation indexAgricultureSENLand, Vol 10, Iss 1221, p 1221 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bioenergy switchgrass yields perennial grass remote sensing spectral vegetation indices green normalized difference vegetation index Agriculture S
spellingShingle	bioenergy switchgrass yields perennial grass remote sensing spectral vegetation indices green normalized difference vegetation index Agriculture S Yuki Hamada Colleen R. Zumpf Jules F. Cacho DoKyoung Lee Cheng-Hsien Lin Arvid Boe Emily Heaton Robert Mitchell Maria Cristina Negri Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
description	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 <i>R</i><sup>2</sup> 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.
format	article
author	Yuki Hamada Colleen R. Zumpf Jules F. Cacho DoKyoung Lee Cheng-Hsien Lin Arvid Boe Emily Heaton Robert Mitchell Maria Cristina Negri
author_facet	Yuki Hamada Colleen R. Zumpf Jules F. Cacho DoKyoung Lee Cheng-Hsien Lin Arvid Boe Emily Heaton Robert Mitchell Maria Cristina Negri
author_sort	Yuki Hamada
title	Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
title_short	Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
title_full	Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
title_fullStr	Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
title_full_unstemmed	Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
title_sort	remote sensing-based estimation of advanced perennial grass biomass yields for bioenergy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/def433421c0c4af29401e2a1ff99dc43
work_keys_str_mv	AT yukihamada remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT colleenrzumpf remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT julesfcacho remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT dokyounglee remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT chenghsienlin remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT arvidboe remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT emilyheaton remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT robertmitchell remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy AT mariacristinanegri remotesensingbasedestimationofadvancedperennialgrassbiomassyieldsforbioenergy
_version_	1718411588648566784

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

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