Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models

Abstract

For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, which is not conducive to high-precision nitrogen inversion, whereas the multi-angle remote sensing monitoring method can effectively extract the canopy’s physicochemical information. However, multi-angle spectral information is affected by a variety of factors, which frequently causes shifts in the band associated with nitrogen uptake, and lowers the estimation accuracy. The capacity of the spectral index to estimate aerial nitrogen concentration (ANC) in cotton was therefore investigated in this work under various observation zenith angles (VZAs), and the Relief−F method was employed to select the best spectral band with weight for ANC that is insensitive to VZA. Therefore, in this study, the ability of the spectral index to estimate ANC in cotton was explored under different VZAs, and the Relief-F algorithm was used to optimize the optimal spectral band with weight for ANC that is insensitive to VZA. The angle insensitive nitrogen index (AINI) for various VZAs was calculated using the expression (R530 − R704)/(R1412 + R704). The results show that the correlation between the spectral index and the ANC chosen in this study is stronger than the correlation between off-nadir observations, and the correlation coefficients between Photochemical Reflectance Index (PRI), AINI, and ANC are highest when VZA is −20° and −50° (r = 0.866 and 0.893, respectively). Compared with the traditional vegetation index, AINI had the best correlation with ANC under different VZAs (r > 0.84), and the performance of ANC in the backscatter direction was estimated to be better than that in the forward-scatter direction. At the same time, the ANC estimation model of the optimal indices AINI and PRI was combined with the machine learning method to achieve better accuracy, and the prediction accuracy of the random forest (RF) model was R2 = 0.98 and RMSE = 0.590. This study shows that the AINI index can estimate cotton ANC under different VZAs. Simultaneously, the backscattered direction is revealed to be more conducive to cotton ANC estimation. The findings encourage the use of multi-angle observations in crop nutrient estimation, which will also help to improve the use of ground-based and satellite sensors.