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Estimation of direct-seeded guayule cover, crop coefficient, and yield using UAS-based multispectral and RGB data

Author

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  • Elshikha, Diaa Eldin M.
  • Hunsaker, Douglas J.
  • Waller, Peter M.
  • Thorp, Kelly R.
  • Dierig, David
  • Wang, Guangyao
  • Cruz, Von Mark V.
  • Katterman, Matthew E.
  • Bronson, Kevin F.
  • Wall, Gerard W.
  • Thompson, Alison L.

Abstract

Guayule (Parthenium argentatum, A. Gray), a perennial desert shrub, produces high-quality natural rubber and is targeted as a domestic natural rubber source in the U.S. While commercialization efforts for guayule are on-going, crop management requires plant growth monitoring, irrigation requirement assessment, and final yield estimation. Such assistance for guayule management could be provided with remote sensing (RS) data. In this study, field and RS data, collected via drones, from a 2-year guayule irrigation experiment conducted at Maricopa, Arizona were evaluated. In-season field measurements included fractional canopy cover (fc), basal (Kcb) and single (Kc) crop coefficients, and final yields of dry biomass (DB), rubber (RY), and resin (ReY). The objectives of this paper were to compare vegetations indices from MS data (NDVI) and RGB data (triangular greenness index, TGI); and derive linear prediction models for estimating fc, Kcb, Kc, and yield as functions of the MS and RGB indices. The NDVI and TGI showed similar seasonal trends and were correlated at a coefficient of determination (r2) of 0.52 and a root mean square error (RMSE) of 0.11. The prediction of measured fc as a linear function of NDVI (r2 = 0.90) was better than by TGI (r2 = 0.50). In contrast to TGI, the measured fc was highly correlated with estimated fc based on RGB image evaluation (r2 = 0.96). Linear models of Kcb and Kc, developed over the two years of guayule growth, had similar r2 values vs NDVI (r2 = 0.46 and 0.41, respectively) and vs TGI (r2 = 0.48 and 0.40, respectively). Final DB, RY, and ReY were predicted by both NDVI (r2 = 0.75, 0.53, and 0.70, respectively) and TGI (r2 = 0.72, 0.48, and 0.65, respectively). The RS-based models enable estimation of irrigation requirements and yields in guayule production fields in the U.S.

Suggested Citation

  • Elshikha, Diaa Eldin M. & Hunsaker, Douglas J. & Waller, Peter M. & Thorp, Kelly R. & Dierig, David & Wang, Guangyao & Cruz, Von Mark V. & Katterman, Matthew E. & Bronson, Kevin F. & Wall, Gerard W. &, 2022. "Estimation of direct-seeded guayule cover, crop coefficient, and yield using UAS-based multispectral and RGB data," Agricultural Water Management, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:agiwat:v:265:y:2022:i:c:s0378377422000877
    DOI: 10.1016/j.agwat.2022.107540
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    References listed on IDEAS

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    1. Bausch, Walter C., 1995. "Remote sensing of crop coefficients for improving the irrigation scheduling of corn," Agricultural Water Management, Elsevier, vol. 27(1), pages 55-68, April.
    2. José Marín & Salima Yousfi & Pedro V. Mauri & Lorena Parra & Jaime Lloret & Alberto Masaguer, 2020. "RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C 3 and C 4 Turfgrass under Different Water Conditions," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    3. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
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    5. Hunsaker, D.J. & Elshikha, D.M., 2017. "Surface irrigation management for guayule rubber production in the US desert Southwest," Agricultural Water Management, Elsevier, vol. 185(C), pages 43-57.
    6. Er-Raki, S. & Chehbouni, A. & Guemouria, N. & Duchemin, B. & Ezzahar, J. & Hadria, R., 2007. "Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region," Agricultural Water Management, Elsevier, vol. 87(1), pages 41-54, January.
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