Development of a predictive tool for rapid assessment of soil total nitrogen in wheat-corn double cropping system with hyperspectral data

ABSTRACT Precision farming based on soil total nitrogen status is crucial to informed fertilization strategies while enhancing environmental quality of agricultural ecosystems. Hyperspectral technology was applied for rapid assessment of soil total nitrogen content in a long-term fertilization experiment of wheat-corn rotation system. The first-order derivative of squared spectra and Soil Adjusted Spectral Index transformation provided the highest correlation with soil total nitrogen contents. Inversion modeling results involving multiple linear regression, back propagation neural network (BPNN), and partial least square regression indicated that BPNN provided the best performance with coefficient of determination (R2) reaching 0.8 and 0.9, respectively, for wheat and corn seasons. Model performance with wheat and corn season combined was poor due to seasonal difference in hyperspectral patterns and different decomposition of straws returned to the field. This study provided a quantitative tool for rapid and accurate diagnosis of soil total nitrogen content for precision agriculture