Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data

Abstract

Real-time, nondestructive monitoring of crop nitrogen (N) status is important for precise N management in winter wheat production. Nadir viewing passive multispectral sensors have limited utility for measuring the N status of winter wheat in middle and bottom layers, and multi-angular remote sensors may instead improve detection of whole canopy physiological and biochemical parameters. Our objective was to improve the predictive accuracy and angular stability of leaf nitrogen concentration (LNC) measurement by constructing a novel Angular Insensitivity Vegetation Index (AIVI). We quantified the relationship between LNC and ground-based multi-angular hyperspectral reflectance in winter wheat (Triticum aestivum L.) across different growth stages, plant types, N rates, planting density, ecological sites and years. The optimum vegetation indices (VIs) obtained from 17 traditional indices reported in the literature were tested for their stability in estimating LNC at 13 view zenith angles (VZAs) in the solar principal plane (SPP). Overall the back-scatter direction gave improved index performance, relative to the nadir and forward-scattering direction. Red-edge VIs (e.g., mND705, GND [750,550], NDRE, RI-1dB) were highly correlated with LNC. However, the relationships strongly depended on experimental conditions, and these VIs tended to saturate at the highest LNC (4.5%). To further overcome the influence of different experimental conditions and VZAs on VIs, we developed a novel index, Angular Insensitivity Vegetation Index (AIVI), based on red-edge, blue and green bands. Our new model showed the highest association with LNC (R2=0.73–0.87) compared to traditional VIs. Investigating AIVI predictive accuracy in measuring LNC across view zenith angles (VZAs) revealed that performance was the highest at −20° and was relatively homogenous between −10° and −40°. This provided a united, predictive model across this wide-angle range, which enhances the possibility of N monitoring by using portable monitors. Testing of the models with independent data gave R2 of 0.84 at −20°, and 0.83 across the range of −10° to −40°, respectively. These results suggest that the novel AIVI is more effective for monitoring LNC than previously reported VIs for predicting accuracy, monitoring model stability and view angle independency. More generally, our model indicates the importance of accounting for angular effects when analyzing VIs under different experimental conditions.

Plant phenotyping relevance

冬小麦の葉窒素濃度という植物形質を、多角 hyperspectral センシングと新規植生指数で推定する手法を開発・独立データで検証しており、フェノタイピング手法が中心である。

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