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Purpose <p>Remote sensing is a valuable tool for creating site-specific maps to reduce the environmental impact of excessive nitrogen (N) fertilization and for predicting crop yield and quality in harvest planning and food security. The aim of this study was the application of remote sensing to improve N management and harvest assessment in bread wheat (<i>Triticum aestivum</i> L.).</p> Methods <p>A wheat field experiment with four N levels and two water regimes was conducted in Central Spain over 2 years. Ground-truth measurements of biomass, plant N concentration, and nitrogen nutrition index (NNI) were collected at three growth stages, with grain yield and N concentration recorded at harvest. Close to the dates of the ground measurements, hyperspectral imagery was acquired covering the visible and near-infrared regions (400–850&#xa0;nm) and part of the short-wave infrared (950–1750&#xa0;nm) from an aircraft flying 300&#xa0;m above the experiment. Sentinel-1 and Sentinel-2 imagery of the site was downloaded and processed. Vegetation indices extracted from the airborne imagery were tested to assess NNI and combined with satellite data by ensemble models (multiple linear regression, artificial neural network, random forest) to predict wheat traits at harvest.</p> Results <p>The canopy chlorophyll content index (CCCI) was the best proxy for crop N status, assessing NNI with root mean square error of 0.21. The NNI maps from aerial imagery reflected the spatial distribution of wheat N requirements and enabled identification of N-responsive and nonresponsive sites for yield at stem elongation and for grain N concentration at flowering. Visible and near-infrared regions provided reliable yield estimates, and bands from the red-edge and the short-wave infrared regions improved prediction of N-related crop traits. Models using hyperspectral imagery and Sentinel-2 data performed comparably.</p> Conclussion <p>The findings highlight the effectiveness of hyperspectral and multispectral imagery for crop monitoring, N-fertilizer management, and harvest planning.</p>

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Hyperspectral and multispectral imagery for optimizing nitrogen management and harvest assessment in bread wheat

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Miguel Quemada,
  • J. L. Pancorbo,
  • N. Arencibia-Pérez,
  • M. D. Raya-Sereno,
  • J. L. Gabriel,
  • A. Hornero

摘要

Purpose

Remote sensing is a valuable tool for creating site-specific maps to reduce the environmental impact of excessive nitrogen (N) fertilization and for predicting crop yield and quality in harvest planning and food security. The aim of this study was the application of remote sensing to improve N management and harvest assessment in bread wheat (Triticum aestivum L.).

Methods

A wheat field experiment with four N levels and two water regimes was conducted in Central Spain over 2 years. Ground-truth measurements of biomass, plant N concentration, and nitrogen nutrition index (NNI) were collected at three growth stages, with grain yield and N concentration recorded at harvest. Close to the dates of the ground measurements, hyperspectral imagery was acquired covering the visible and near-infrared regions (400–850 nm) and part of the short-wave infrared (950–1750 nm) from an aircraft flying 300 m above the experiment. Sentinel-1 and Sentinel-2 imagery of the site was downloaded and processed. Vegetation indices extracted from the airborne imagery were tested to assess NNI and combined with satellite data by ensemble models (multiple linear regression, artificial neural network, random forest) to predict wheat traits at harvest.

Results

The canopy chlorophyll content index (CCCI) was the best proxy for crop N status, assessing NNI with root mean square error of 0.21. The NNI maps from aerial imagery reflected the spatial distribution of wheat N requirements and enabled identification of N-responsive and nonresponsive sites for yield at stem elongation and for grain N concentration at flowering. Visible and near-infrared regions provided reliable yield estimates, and bands from the red-edge and the short-wave infrared regions improved prediction of N-related crop traits. Models using hyperspectral imagery and Sentinel-2 data performed comparably.

Conclussion

The findings highlight the effectiveness of hyperspectral and multispectral imagery for crop monitoring, N-fertilizer management, and harvest planning.