Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA

Yuki Hamada (), Colleen R. Zumpf, John J. Quinn and Maria Cristina Negri
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Yuki Hamada: Environmental Science Division, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA
Colleen R. Zumpf: Environmental Science Division, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA
John J. Quinn: Environmental Science Division, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA
Maria Cristina Negri: Environmental Science Division, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA

Energies, 2023, vol. 16, issue 21, 1-15

Abstract: We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R 2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R 2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.

Keywords: remote sensing; Sentinel-2; vegetation index; switchgrass; spectral response; renewable resource; clean energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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