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Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction

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  • Peroni Venancio, Luan
  • Chartuni Mantovani, Everardo
  • do Amaral, Cibele Hummel
  • Usher Neale, Christopher Michael
  • Zution Gonçalves, Ivo
  • Filgueiras, Roberto
  • Coelho Eugenio, Fernando

Abstract

Crop biomass (Bio) is one of the most important parameters of a crop, and knowledge of it before harvest is essential to help farmers in their decision making. Both green and dry Bio can be estimated from vegetation spectral indices (VIs) because they have a close relationship with accumulated absorbed photosynthetically active radiation (APAR), which is proportional to total Bio. The aims of this study were to analyze the potential capacity of spectral vegetation indices in estimating corn green biomass based on their relationship with the photosynthetic vegetation sub-pixel fraction derived from spectral mixture analysis and to analyze the best interval of VI accumulation (days) for corn grain yield estimation. Field data of center pivots cultivated with corn during the irrigation seasons of 2015 and 2018 and Landsat 8 and Sentinel 2 images were used. The EVI produced the best results; Pearson's correlation coefficient, RMSE and Willmott’s index reached 0.99, 6.5%, and 0.948, respectively. Among the nine potential VIs analyzed, the EVI, SAVI and OSAVI were considered the first, second and third best performing for corn green Bio estimation, respectively, based on their comparison to the photosynthetic vegetation sub-pixel fraction (fPV), and the time intervals that extended until 120 days after sowing showed the best results for corn grain yield estimation.

Suggested Citation

  • Peroni Venancio, Luan & Chartuni Mantovani, Everardo & do Amaral, Cibele Hummel & Usher Neale, Christopher Michael & Zution Gonçalves, Ivo & Filgueiras, Roberto & Coelho Eugenio, Fernando, 2020. "Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction," Agricultural Water Management, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:agiwat:v:236:y:2020:i:c:s0378377419317585
    DOI: 10.1016/j.agwat.2020.106155
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    References listed on IDEAS

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    1. Ying Li & Hong Wang & Xiao Bing Li, 2015. "Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    2. Wei Wang & Xinfeng Yao & Junpeng Zhai & Minhe Ji, 2014. "A Tetrahedron-Based Endmember Selection Approach for Urban Impervious Surface Mapping," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-16, June.
    3. Sanaz Shafian & Nithya Rajan & Ronnie Schnell & Muthukumar Bagavathiannan & John Valasek & Yeyin Shi & Jeff Olsenholler, 2018. "Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
    4. Haiming Qin & Cheng Wang & Kaiguang Zhao & Xiaohuan Xi, 2018. "Estimation of the fraction of absorbed photosynthetically active radiation (fPAR) in maize canopies using LiDAR data and hyperspectral imagery," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    5. Venancio, Luan Peroni & Mantovani, Everardo Chartuni & do Amaral, Cibele Hummel & Usher Neale, Christopher Michael & Gonçalves, Ivo Zution & Filgueiras, Roberto & Campos, Isidro, 2019. "Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI)," Agricultural Water Management, Elsevier, vol. 225(C).
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    1. Gonçalves, I.Z. & Ruhoff, A. & Laipelt, L. & Bispo, R.C. & Hernandez, F.B.T. & Neale, C.M.U. & Teixeira, A.H.C. & Marin, F.R., 2022. "Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Chang Meng & Mei Hong & Yuncai Hu & Fei Li, 2024. "Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas," Sustainability, MDPI, vol. 16(10), pages 1-23, May.

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