Caracterización de espectros estándar de reflectancia hiperespectral para parcelas de caña de azúcar de alta y baja productividad mediante análisis de datos funcionales

Aldemar Reyes-Trujillo; Johann Alexis  Ospina-Galíndez; Jhony Armando Benavides-Bolaños

doi:10.22490/21456453.10349

Vol. 17 Núm. 2 (2026)

Publicado 26-09-2026

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Characterization of standard hyperspectral reflectance spectra for high- and low-productivity sugarcane plots using functional data analysis

DOI: https://doi.org/10.22490/21456453.10349

Aldemar Reyes-Trujillo Universidad del Valle

Johann Alexis Ospina-Galíndez Universidad Autónoma de Occidente

Jhony Armando Benavides Bolaños Universidad del Valle

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Contextualization: Sugarcane is a key crop in tropical agriculture, providing sugar, ethanol, and bioenergy. Valle del Cauca Department, Colombia, is among the world’s most productive sugarcane-growing regions, where sustainable intensification requires accurate, nondestructive methods to monitor crop development and predict yield.

Knowledge gap: Traditional vegetation indices calculated from broad multispectral bands, such as the Normalized Difference Vegetation Index, saturate in dense canopies and have limited sensitivity to subtle physiological and structural variations, restricting early yield prediction. Although hyperspectral analysis provides greater detail, most studies rely on discrete indices. Functional statistical approaches remain underused in sugarcane research, leaving a gap in the early classification of productivity.

Objective: This study evaluated whether functional data analysis of hyperspectral reflectance can improve the early classification of sugarcane productivity. The specific objectives were to distinguish productivity levels, compare canopy- and leaf-scale measurements, and analyze the influence of growth stage on spectral variability.

Methodology: Hyperspectral reflectance data were collected from six plots in Valle del Cauca at two growth stages, 2.5 and 5.5 months, at both the canopy and leaf scales. The raw spectra were converted into continuous functional curves using B-spline smoothing. Functional means and permutation-based t-tests were then used to identify significant differences between productivity classes.

Results and conclusions: Canopy spectra clearly distinguished high- and low-productivity plots, particularly in the near-infrared region, where greater biomass and leaf area in high-productivity plots resulted in higher reflectance. Leaf spectra showed weaker discrimination, especially at later growth stages. Functional data analysis preserved spectral characteristics often overlooked by traditional indices, enabling a more detailed interpretation of crop variability. Canopy-scale functional hyperspectral analysis provides a robust, nondestructive tool for early yield assessment and supports precision agriculture decision-making related to fertilization, irrigation, and crop management.

Palabras clave: agricultura de precisión, espectrometría, reflectancia, rendimiento de cultivos

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Reyes-Trujillo, A., Ospina-Galíndez , J. A. ., & Benavides Bolaños, J. A. (2026). Characterization of standard hyperspectral reflectance spectra for high- and low-productivity sugarcane plots using functional data analysis. Revista De Investigación Agraria Y Ambiental, 17(2), 331-355. https://doi.org/10.22490/21456453.10349

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