Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA
Abstract
:1. Introduction
1.1. Use of Remote Sensing for Estimating Perennial Bioenergy Grass Biomass Yields
1.2. Relevance of the NDRE for Estimating Perennial Bioenergy Grass Biomass Yields
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Analysis
3. Results
3.1. Seasonal Trajectory of the NDRE
3.2. Optimal Date of NDRE for Estimating Perennial Bioenergy Grass Biomass Yields
3.3. Accuracy of Estimated Field-Level Perennial Bioenergy Grass Biomass Yields Using a Linear NDRE Regression Model
3.4. Statistical Significance of the NDRE for Estimating the Perennial Bioenergy Grass Biomass Yields Using a Linear Regression Model
3.5. Bias and Consistency in Estimating the Field-Level Perennial Bioenergy Grass Biomass Yields Using a Linear NDRE Regression Model
4. Discussion
4.1. Impact of the Harvest Date on Determining a Common Optimal Date for Multiple Sites
4.2. Possible Transition of the NDRE Indication from the Early to the Late Growing Season
4.3. Challenge in Estimating Biomass Yields for Sites with Hetergeneous Perennial Bioenergy Grass Production
4.4. Limitations Associated with Inconsistent Yield Ranges across the Study Sites
5. Conclusions
- Inconsistent with the existing studies [24,25,32], the optimal date for estimating field-level perennial bioenergy grass biomass yields for the central Virginia sites using the pooled data was in late summer (11 August). We hypothesized that the index–biomass relationships specific to a perennial bioenergy grass type are the strongest in early summer to mid-summer [24,32] and become more indicative of the overall biomass than the biomass specific to a certain grass type as crops mature and senesce later in summer. This could be an important consideration for scaling the perennial bioenergy grass biomass yield estimation for a large number of sites. Examining relationships between the recorded yields and index values by perennial bioenergy grass types from early summer to right before harvest would allow the testing of this hypothesis.
- We should expect an earlier harvest for sites with a short growing season or for growing perennial grasses with earlier peak biomass yields than other sites. The optimal date may overlap with, or occur after, harvest for those sites. The sites with early harvest may require a separate model because of their potentially distinct yield–index relationships. This would be a critical limitation for scaling the current method for estimating perennial bioenergy grass biomass yields over large areas.
- Regardless of the approach (i.e., all-site, cross-site, or site-specific estimations), estimating the biomass yields for a site with heterogeneous perennial bioenergy grasses is more difficult than for sites with a monoculture or uniform mixed grass for the fields within a site. Stratifying the fields by grass types would allow type-specific index–yield relationships to be compared for fields with comparable crop types. This comparison could determine the optimal date for yield estimation by grass type. It could also identify grasses that may hinder the remotely sensed biomass yield estimation using a single model due to their unique spectral-response characteristics.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Site A | Site B | Site C | Site D | |
---|---|---|---|---|
Elevation (m) | 305 | 116 | 168 | 244 |
Total field count | 13 | 18 | 15 | 24 |
Field count available for modeling | 13 | 16 | 14 | 20 |
Total area 1 (ha) | 243.4 | 134.3 | 63.8 | 100.2 |
Average field size (ha) [standard deviation] | 18.7 [20.6] | 8.4 [7.9] | 4.3 [3.4] | 5.0 [2.6] |
Average field-level perennial bioenergy grass yield 2 (Mg/ha) [standard deviation] | 7.9 [2.4] | 5.7 [1.8] | 2.7 [0.6] | 7.2 [2.0] |
Perennial bioenergy grass variety | Switchgrass ‘Alamo’ | Switchgrass ‘Kanlow’, Miscanthus, mixed grass (big bluestem, Indiangrass, switchgrass) | Mixed grass (big bluestem, Indiangrass, switchgrass) | Switchgrass ‘Blackwell’ |
Harvest activities for the 2019 season | ||||
Mowing period | 9/18–10/6 | 8/13–8/29 | 9/3–9/5 | 7/30–8/4 |
Raking period | 9/19–10/7 | 8/25–8/31 | 9/4–9/6 | 7/30–8/12 |
Baling begins | 9/19–10/7 | 8/27–8/31 | 9/4–9/6 | 8/4–8/21 |
Dates of Sentinel-2 image availability for the 2019 season | ||||
April | — | 11, 16 | 4 | 4 |
May | 24 | 19, 21, 24 | 14, 19, 24 | 4, 19, 24 |
June | 3, 23, 28 | 3, 15 | 3, 23 | 3, 13, 28 |
July | 3, 13, 28 | 10, 13, 20, 30 | 3, 8, 18, 28 | 3, 13, 28 |
August | 12 | 9, 19, 29 | 22 | 12, 22 |
September | 11, 16, 21 | 8, 21 | 1, 6, 11, 16, 21 | 1, 11, 16, 21, 26 |
October | 1, 11, 21 | 1, 18, 28 | 1, 11 | 1, 11 |
Total images considered | 14 | 19 | 18 | 19 |
Site | R2 | RMSE (Mg/ha) | MAE (Mg/ha) |
---|---|---|---|
All | 0.76 | 1.5 | 1.2 |
A | 0.69 | 1.8 | 1.4 |
B | 0.44 | 1.9 | 1.5 |
C | 0.33 | 1.6 | 1.4 |
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Hamada, Y.; Zumpf, C.R.; Quinn, J.J.; Negri, M.C. Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA. Energies 2023, 16, 7397. https://doi.org/10.3390/en16217397
Hamada Y, Zumpf CR, Quinn JJ, Negri MC. Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA. Energies. 2023; 16(21):7397. https://doi.org/10.3390/en16217397
Chicago/Turabian StyleHamada, Yuki, Colleen R. Zumpf, John J. Quinn, and Maria Cristina Negri. 2023. "Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA" Energies 16, no. 21: 7397. https://doi.org/10.3390/en16217397
APA StyleHamada, Y., Zumpf, C. R., Quinn, J. J., & Negri, M. C. (2023). Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA. Energies, 16(21), 7397. https://doi.org/10.3390/en16217397