Hyperspectral Assessment of Canopy Nitrogen Content in Rice - Comparative Analysis Using Multiple Datasets -

Assessment of canopy nitrogen content (CNC) is an important basis for growth diagnosis, precision management, and yield prediction in rice crop. High-resolution spectral reflectance measurement (Hyperspectra) has been suggested to have significant roles in assessment of crop variables, especially biochemical components (e.g., chlorophyll content) and physiological functioning (e.g., light use efficiency) as well as biotic and abiotic stresses (e.g., water deficit, disease infection) [1, 2]. Nevertheless, optimal use of hyperspectra for each target variable has not been well examined. Therefore, our objective was to explore accuracy and robustness of hyperspectral indices and multivariable methods for assessment of CNC based on comparative analysis using multiple datasets obtained in a wide range of sites, sensors, and conditions.