Abstract
Accurate, non-destructive quantification of leaf nitrogen content (LNC) is crucial for monitoring crop health and growth. Traditional empirical methods require extensive in-situ data for training, while physically-based methods are limited by ill-posed inversion, and hybrid methods suffer from domain shift between in-situ and simulated data. To overcome these limitations, this study introduces DeepSpecN, a novel hybrid method for maize LNC estimation using leaf-scale hyperspectral bidirectional reflectance. Without requiring in-situ data for training, DeepSpecN combines four key components: continuous wavelet transform (CWT) for reducing specular reflection, PROSPECT-PRO for simulating training data, an improved Transformer model for feature learning, and a spectral similarity-based sample selection method for selecting more valuable training samples. DeepSpecN and other methods, including physically-based methods, non-parametric regression based hybrid methods, and parametric regression methods based on vegetation indices (VIs), were validated using bidirectional reflectance data from 1724 maize leaves. The results showed that, when trained on representative samples, DeepSpecN achieved the highest estimation accuracy among all the methods (RMSE = 0.247 g/m(2), R(2) = 0.665). The sample selection strategy mitigated the effects of domain shift by identifying representative training samples with high spectral similarity from the simulated database. Furthermore, the results showed that the Chlorophyll (Chl)-based empirical formulas estimated maize LNC more accurately than those based on leaf protein content. Moreover, the validation results on four different crop species confirm the generalizability of DeepSpecN. Our findings demonstrate the potential of hybrid methods that utilize bidirectional reflectance spectra, developed by addressing the domain shift issue, to improve the LNC estimation accuracy.