Potential of hyperspectral remote sensing to estimate the yield of a Crambe abyssinica Hochst crop

Octavio H. Viana; Erivelto Mercante; Mauricio Guy de Andrade; Henrique dos Santos Felipetto; Carlos  E. Vizzotto Cattani; Filipe F. Bombarda; Marcio Antonio Vilas Boas

doi:10.1117/1.JRS.12.016023

14 February 2018 Potential of hyperspectral remote sensing to estimate the yield of a Crambe abyssinica Hochst crop

Octavio H. Viana, Erivelto Mercante, Mauricio Guy de Andrade, Henrique dos Santos Felipetto, Carlos E. Vizzotto Cattani, Filipe F. Bombarda, Marcio Antonio Vilas Boas

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016023 (February 2018). https://doi.org/10.1117/1.JRS.12.016023

Abstract

The objective of research is to evaluate the relationship between vegetation indices obtained in different phenological phases by hyperspectral sensors and crambe grain yield. The study was conducted in the winter crop season in 2015 in an experimental agricultural area in the city of Cascavel, Paraná. Spectral information about 12 random points were acquired during all phenological phases of the crambe crop. The data collected were separated into different spectral bands. Then, the vegetation indices normalized difference vegetation index and normalized difference moisture index (NDMI) were generated. Remote sensing data were correlated with grain yield, and linear regression models were elaborated and evaluated. At 66 days after sowing (DAS), in the beginning of the flowering phase, the NDMI presented a negative correlation with grain yield (Radj2: 0.49; RMSE: 134.80 kg ha−1). The red-light region (648 to 672 nm) at 66 DAS presented a positive linear correlation with grain yield (Radj2: 0.36; RMSE: 151.38 kg ha−1). The flowers in the canopy of the plant during the flowering phase contributed to the increase of the reflectance in the red-light and midinfrared regions. Excessive precipitation and winds during flowering, granulation, and maturation led to grain damage and yield variability, reducing the explanatory capacity of the production with the models.

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Octavio H. Viana, Erivelto Mercante, Mauricio Guy de Andrade, Henrique dos Santos Felipetto, Carlos E. Vizzotto Cattani, Filipe F. Bombarda, and Marcio Antonio Vilas Boas "Potential of hyperspectral remote sensing to estimate the yield of a Crambe abyssinica Hochst crop," Journal of Applied Remote Sensing 12(1), 016023 (14 February 2018). https://doi.org/10.1117/1.JRS.12.016023

Received: 22 October 2017; Accepted: 19 January 2018; Published: 14 February 2018

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