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High-resolution airborne hyperspectral imagery for assessing yield, biomass, grain N concentration, and N output in spring wheat

Creator: Raya-Sereno, M.D.
Creator: Ortiz-Monasterio, I.
Creator: Alonso-Ayuso, M.
Creator: Rodrigues, F.
Creator: Rodríguez, A.A.
Creator: Gonzalez-Perez, L.
Creator: Quemada, M.
Year: 2021
URI: https://hdl.handle.net/10883/21490
Language: English
Publisher: MDPI
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Basel (Switzerland)
Issue: 7
Volume: 13
DOI: 10.3390/rs13071373
Keywords: Precision Farming
Description: 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.
Agrovoc: PRECISION AGRICULTURE
Agrovoc: PROTEIN CONTENT
Agrovoc: REFLECTANCE
Agrovoc: SPECTROSCOPY
Agrovoc: VEGETATION INDEX
Related Datasets: https://www.mdpi.com/2072-4292/13/7/1373#supplementary
ISSN: 2072-4292
Journal: Remote Sensing
Article number: 1373


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    Buffer collection to upload SIP Latam information products.
  • Sustainable Intensification
    Sustainable intensification agriculture including topics on cropping systems, agronomy, soil, mechanization, precision agriculture, etc.

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