Using optimized three-band spectral indices to assess canopy N uptake in corn and wheat

Abstract

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.