Monitoring of winter wheat nitrogen nutrition based on UAV hyperspectral images

Abstract: Nitrogen is a large number of elements required by crop life and is closely related to crop yield and quality. Accurate diagnosis and dynamic monitoring of crops are important contents of precision agriculture research. With the advantages of high timeliness, high resolution, and low cost, Unmanned Aerial Vehicle (UAV) remote sensing is developing rapidly in the diagnosis and monitoring of nitrogen nutrition in winter wheat. In this study, Nitrogen Nutrition Index (NNI) was selected as an indicator of the nitrogen nutritional status of winter wheat. In order to quantitatively estimate the nitrogen nutrition index of winter wheat at a regional scale, hyperspectral images of the winter wheat experimental plots in Qianxian, Shaanxi province during the year 2016-2017 was obtained by the Cubert UHD185 imaging spectrometer boarded on the UAV. Meanwhile, the above-ground biomass and plant nitrogen concentration data were collected during the flying and were used to calculate the NNI according to the local critical nitrogen concentration dilution model of wheat. Three types of spectral parameters were selected, including trilateral parameters, any two bands spectral indices, and vegetation indices. According to the correlation analysis between spectral parameters and plant nitrogen content, aboveground biomass, and nitrogen nutrition index, ten spectral parameters significantly related to each of the three indicators were screened out, including red-edge area, green peak reflectance maximum, red valley reflectivity minimum, difference spectral index, normalized spectral index, ratio spectral index, red-edge position by linear interpolation, red-edge normalized difference vegetation index, Vogelmann red-edge index, and photochemical reflectance index. Finally, nitrogen nutrition index estimation models of winter wheat at the heading stage were established based on the ten spectral parameters above with simple regression, Multiple Linear Stepwise Regression (MLSR), Partial Least Squares Regression (PLSR), and Random Forest Regression (RFR) algorithms respectively. The coefficient of determination, Root Mean Square Error (RMSE), and Relative Prediction Deviation (RPD) were used to compare the accuracy of each model. The results showed that any two bands' spectral indices were sensitive to the nitrogen nutrition index. The correlation coefficients between any two bands spectral indices and nitrogen nutrition index were dramatically superior to trilateral parameters and typical vegetation indices. The optimal any two bands spectral index was the ratio spectral index made up of the reflectance at 718 and 738 nm. Of all the nitrogen nutrition index estimation models by simple regression, Difference Spectral Index (DSI) and Red-edge Normalized Difference Vegetation Index (RNDVI) had rough estimation capabilities of nitrogen nutrition index, the relative prediction deviation of which were 1.53 and 1.56 respectively. Among the nitrogen nutrition index estimation models of winter wheat established through ten spectral parameters based on multiple linear stepwise regression, partial least squares regression and random forest regression, the model based on the random forest algorithm had the highest precision, strong reliability, and excellent prediction ability. The validation results showed that the coefficient of determination of the model was 0.79, the root mean square error was 0.13, and the relative prediction deviation was 2.25. The second was the model through the partial least squares regression method, the relative prediction deviation of which was1.54. The model by multiple linear stepwise regression methods could not be used to estimate the nitrogen nutrition index correctly. Overall, the model with the random forest algorithm was considered as the best estimation model of the nitrogen nutrition index, which could be used for the remote sensing mapping of the winter wheat nitrogen nutrition index at a regional scale. It would provide a scientific basis for the diagnosis of nitrogen nutrition, monitoring of yield and quality, and late field management of winter wheat.