Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops.  This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice.  Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014.  Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice.  LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period.  Group and individual plant characters (e.g., tiller number (TN) and plant height (H)) were investigated simultaneously.  Two N indicators of plant N accumulation (NA) and N nutrition index (NNI) were measured as well.  A calibration equation (LAI=1.7787LAI₂₀₀₀–0.8816, R²=0.870**) was developed for LAI-2000.  The linear regression analysis showed a significant relationship between NA and actual LAI (R²=0.863**).  For the NNI, the relative LAI (R2=0.808**) was a relatively unbiased variable in the regression than the LAI (R²=0.33**).  The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice (NA=29.778LAI–5.9397; NNI=0.7705RLAI+0.2764).  Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H (LAI=–0.3375(TH×H×0.01)²+3.665(TH×H×0.01)–1.8249, R²=0.875**).  With these models, the N status of rice can be diagnosed conveniently in the field.

Plant nitrogen (N) uptake is a good indicator of crop N status. In this study, a new method was designed to determine the central wavelength, optimal bandwidth and vegetation indices for predicting plant N uptake (g N m-2) in winter wheat (Triticum aestivum L.). The data were collected from the ground-based hyperspectral reflectance measurements in eight field experiments on winter wheat of different years, eco-sites, varieties, N rates, sowing dates, and densities. The plant N uptake index (PNUI) based on NDVI of 807 nm combined with 736 nm was selected as the optimal vegetation index, and a linear model was developed with R2 of 0.870 and RMSE of 1.546 g N m-2 for calibration, and R2 of 0.834, RMSE of 1.316 g N m-2, slope of 0.934, and intercept of 0.001 for validation. Then, the effect of the bandwidth of central wavelengths on model performance was determined based on the interaction between central wavelength and bandwidth expansion. The results indicated that the optimal bandwidth varies with the changes of the central wavelength and with the interaction between the two bands in one vegetation index. These findings are important for prediction and diagnosis of plant N uptake more precise and accurate in crop management.

Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822,R738) during the early-mid period (from jointing to booting), and it was RVI (R822,R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.