A general methodology for the quantification of crop canopy nitrogen across diverse species using airborne imaging spectroscopy

Accurate monitoring of crop nitrogen (N) across spatial and temporal scales is a fundamental goal for meeting precision agriculture requirements and promoting sustainable agriculture. The planning and implementation of several spaceborne imaging spectroscopy missions in recent years holds great promise for such large scale and intricate monitoring. Several N retrieval models have been developed for specific crop species, but a generalized model across diverse species is lacking. By leveraging imaging spectroscopy data collected by the Global Airborne Observatory (GAO), and leaf samples collected from commercial and research farms, we used partial least squares regression to calibrate and validate the retrieval of mass-based crop canopy N concentrations across diverse species and several major agricultural regions in the contiguous United States. The performance statistics indicated high precision and accuracy of the model results, suggesting that the development of a generalized N retrieval model is possible (R2: 0.78; RMSE: 0.49% N). Maps derived from GAO data provided quantitative crop N information at fine spatial resolution (i.e., 0.6 m), capturing both inter- and intra-species variations across agricultural locations. The algorithm was also successfully tested on simulated moderate resolution (i.e., 30 m) imagery, corresponding to data to be collected by forthcoming spaceborne imaging spectroscopy missions. Imaging spectroscopy offers an effective approach to quantify crop N concentration that could be incorporated to promote sustainable agriculture and improve global food security.