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Abstract: Remotely and accurately quantifying the canopy nitrogen status in crops is essential for regional studies of N budgets and N balances. In this study, we optimised three-band spectral algorithms to estimate the N status of winter wheat. This study extends previous work to optimise the band combinations further and identifies the optimised central bands and suitable bandwidths of the three-band nitrogen planar domain index (NPDI) for estimating the aerial N uptake, N concentration and aboveground biomass. Analysis of the influence of bandwidth change on the accuracy of estimating the canopy N status and aboveground biomass indicated that the suitable bandwidths for optimised central bands were 37nm at 846nm, 13nm at 738nm and 57nm at 560nm for assessing the aerial N uptake and were 37nm at 958nm, 21nm at 696nm and 73nm at 578nm for the assessment of the aerial N concentration and were 49nm at 806nm, 17nm at 738nm and 57nm at 560nm for the estimation of aboveground biomass. The optimised three-band NPDI could consistently and stably estimate the aerial N uptake and aboveground biomass of winter wheat in the vegetative stage and the aerial N concentration in the reproductive stage compared to the fixed band combinations. With suitable bandwidths, the broadband NPDI demonstrated excellent performance in estimating the aerial N concentration, N uptake and biomass. We conclude that the band-optimised algorithm represents a promising tool to measure the improved performance of the NPDI in estimating the aerial N uptake and biomass in the vegetative stage and the aerial N concentration in the reproductive stage, which will be useful for designing improved nitrogen diagnosis systems and for enhancing the applications of ground- and satellite-based sensors. [Copyright &y& Elsevier]

Highlights: [•] Hyperspectral indices obtained from algorithms designed to optimize a combination of wavebands were explored. [•] Multi-temporal winter wheat datasets of China and Germany were used to assess aerial N uptake. [•] Optimized hyperspectral indices were generally more robust than published indices. [•] Optimized waveband combinations provided a promising algorithm to improve the prediction of aerial N uptake. [•] Implementation for future aerial N uptake monitoring applications by hyperspectral sensors is possible. [Copyright &y& Elsevier]

Abstract: Red light based broadband vegetation indices are widely applied to derive aerial nitrogen (N) status parameters. With the advance of growth stages, however, crop canopy structure and aerial biomass will vary greatly, which negatively influences the relationships between spectral indices and the crop canopy N status. The current research aimed to assess the performance of red edge based vegetation indices, derived from simulated broadband WorldView-2 data, to remotely sense aerial N concentration and uptake in winter wheat (Triticum aestivum L.). Six experiments with different N rates for five German cultivars and four Chinese cultivars of winter wheat were conducted in southeast Germany and in the North China Plain from 2007 to 2010. The results showed that aerial biomass strongly affected the relationships between broadband vegetation indices and aerial N concentration before the heading stage. Normalising by using the planar domain index approach significantly improved the prediction power of red edge dependent broadband vegetation indices in estimating aerial N status. The two-dimensional broadband canopy chlorophyll content index (CCCI) and a newly proposed nitrogen planar domain index (NPDI) involving the WorldView-2 satellite red edge region were found to be more stable and better predictors than traditional red light based broadband vegetation indices in estimating aerial N concentration after the heading stage and in assessing aerial N uptake before the heading stage. The findings from this study may be useful for managing the application of N fertiliser for winter wheat in Zadoks growth stages 30–55 and in indirectly monitoring aerial N content in Zadoks growth stages 59–75 at landscape scales. [Copyright &y& Elsevier]

• Identify optimized three-band spectral indices for estimating the canopy N uptake of corn and wheat. • The optimized NPDI estimated canopy N uptake of both corn and wheat alone and in combination. • Our algorithm may allow the design of improved N diagnosis systems. Nitrogen (N) fertilization management plays an important role in optimizing crop growth and yield. Concerns over environmental risk require a quick, accurate, non-destructive determination of the N status of crops. Hyperspectral remote sensing allows a timely monitoring of the in-season crop N status. Although many spectral indices for assessing the N status of crops have been proposed, it is still necessary to further optimize the central bands, since they often vary with plant cultivars and species. To improve this, we identified optimized three-band spectral indices for estimating the canopy N uptake of corn and wheat. Experiments were conducted from 2009 to 2011 by evaluating and testing optimized three-band spectral indices for estimating the N status of wheat cultivars grown in Germany and China and corn cultivars grown in China. The indices generally enabled more robust predictions compared to published indices. The central bands suitable for assessing the canopy N uptake were 768, 740, and 548 nm for corn; 876, 736 and 550 nm for wheat; and 846, 732 and 536 nm for corn and wheat combined. Both wheat and corn assessed individually as well as in combination where sharing similar wavebands reflected by the species-specific and interspecies-specific optimized three-band spectral indices, e.g. the wavelengths 740, 736 and 732 nm were identified as optimum for corn, wheat and their combination, respectively. The validation results suggest that predictions using the optimized three-band N planar domain index (NPDI) delivered the highest coefficient of determination (R2 = 0.86) and the lowest root mean square error (RMSE, 20.1 kg N ha–1) and relative error (RE, 18.7 %). The optimized NPDI consistently estimated canopy N uptake of both corn and wheat alone and in combination. Therefore, the optimized three-band algorithm is an attractive tool for optimizing and identifying central bands. Our algorithm may allow the design of improved N diagnosis systems and enhance the application of ground- and satellite-based sensing. [ABSTRACT FROM AUTHOR]