A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton

Nitrogen is the key biochemical component of chlorophyll, protein and enzymes, and it is widely used as an indicator of photosynthesis and plant nutrient levels. Hyper-spectral data-based estimation of nitrogen allows for a low-cost, effective and environmentally beneficial diagnosis of plant growth. In this paper, a novel approach to characterize the Total Nitrogen Content (TNC) from canopy spectral reflectance through a fractional order derivative (FOD) and optimized spectral indices (NDSI, RSI) is proposed. A total of 60 sampling plots designed in field experiments, canopy spectral data and total nitrogen content are tested for each plot. Optimized remote sensing indices derived from FOD spectra were applied to investigate sensitive wavebands; finally, a Support Vector Machine Regression model for estimating cotton TNC was generated. Our results showed that small FOD orders improved the spectral resolution and provided abundant absorption features; as the orders increased, the spectral strength decreased and the curves were smoothed gradually. The coefficient of correlation (R) peak appeared at the 1.25 order with a value of 0.652. The coefficient of determination (R-2) between TNCs and optimized spectral indices peaked at NDSI beyond the 1.5 order (R-2 = 0.592). Fourteen TNC estimation models were created via SVR methods using optimized spectral indices. Modeling results indicated that the optimal model was original reflectance-RSI, where the highest R-2 was 0.784, the lowest root mean square error (RMSE) was 1.333, and the residual prediction deviation (RPD) was 1.80. Overall, FOD can potentially exploit spectral characteristics and eliminate spectral redundancy. However, original reflectance still shows a high potential for accurate predictions of the total nitrogen content in cotton.