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Article

High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat

1
Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), Universidad Politécnica de Madrid, Senda del Rey 18, 28040 Madrid, Spain
2
International Maize and Wheat Improvement Center—CIMMYT, Texcoco 56237, Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: Yafit Cohen and Xia Yao
Remote Sens. 2021, 13(7), 1373; https://doi.org/10.3390/rs13071373
Received: 5 March 2021 / Revised: 26 March 2021 / Accepted: 31 March 2021 / Published: 2 April 2021
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in crop parameter prediction and application to nitrogen (N) fertilization remain open. The objective of this study was to optimize of remote sensing spectral information for its application to grain yield (GY), biomass, grain N concentration (GNC), and N output assessment, and decision making on spring wheat fertilization. Spring wheat (Triticum turgidum L.) field experiments testing two tillage treatments, two irrigation levels and six N treatments were conducted in Northwest Mexico over four consecutive years. Hyperspectral images were acquired through 27 airborne flight campaigns. At harvest, GY, biomass, GNC and N output were determined. Spectral exploratory analysis was used to identify the best wavelength combinations, the most suitable vegetation indices (VIs) and the best growth stages to assess the agronomic variables. The relationship between the spectral information and the agronomic measurements was evaluated by the coefficient of determination (R2) and the root mean square error (RMSE). The ability of the indices to guide fertilizer recommendation was assessed through an error analysis based on the N sufficiency index. GY was better assessed from the end of flowering to the early milk stage by VIs based on the combination of bands from near infrared radiation/visible and from near infrared radiation/red-edge regions (R2 > 0.6; RMSE < 700 kg ha−1). N output was efficiently assessed by a combination of bands from near infrared radiation/red-edge at booting (R2 > 0.7; RMSE < 9 kg N ha−1). The GNC was better estimated by VIs combining bands in near infrared radiation/red-edge at early milk, but with great variability among the years studied. Some VIs were promising for guiding fertilizer recommendation for increasing GNC, but there was not a single index providing reliable recommendations every year. This study highlights the potential of remote sensing imagery to assess GY and N output in spring wheat, but the identification of GNC responsive sites needs to be improved. View Full-Text
Keywords: precision farming; protein content; reflectance; spectroscopy; vegetation indices precision farming; protein content; reflectance; spectroscopy; vegetation indices
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MDPI and ACS Style

Raya-Sereno, M.D.; Ortiz-Monasterio, J.I.; Alonso-Ayuso, M.; Rodrigues, F.A., Jr.; Rodríguez, A.A.; González-Perez, L.; Quemada, M. High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat. Remote Sens. 2021, 13, 1373. https://doi.org/10.3390/rs13071373

AMA Style

Raya-Sereno MD, Ortiz-Monasterio JI, Alonso-Ayuso M, Rodrigues FA Jr., Rodríguez AA, González-Perez L, Quemada M. High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat. Remote Sensing. 2021; 13(7):1373. https://doi.org/10.3390/rs13071373

Chicago/Turabian Style

Raya-Sereno, María D., J. Ivan Ortiz-Monasterio, María Alonso-Ayuso, Francelino A. Rodrigues Jr., Arlet A. Rodríguez, Lorena González-Perez, and Miguel Quemada. 2021. "High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat" Remote Sensing 13, no. 7: 1373. https://doi.org/10.3390/rs13071373

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