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

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

Agricultural Water Management, 2020, vol. 236, issue C

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.

Keywords: Zea maysL.; spectral unmixing; above-ground green biomass; yield (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:236:y:2020:i:c:s0378377419317585

DOI: 10.1016/j.agwat.2020.106155

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