Monitoring Rice Leaf Nitrogen Content Based on the Canopy Structure Effect Corrected With a Novel Model PROSPECT-P

Spectral remote sensing can effectively, rapidly, and nondestructively detect the nitrogen status of crop plants. Estimation of crop leaf nitrogen concentration (LNC, %) using canopy bidirectional reflectance factor (BRF) is an effective method to diagnose nitrogen deficiency in crops. It is challenging to estimate LNC with empirical remote sensing models because the variability of the canopy structure at different growth stages affects the model accuracy. Over the years, the canopy scattering coefficient [CSC, the ratio of BRF to directional area scattering factor (DASF)] has been used for LNC estimation by suppressing the effect of the canopy structure on BRF. However, this method often regards leaves as the main factor and has less consideration for the canopy structure effects on BRF caused by other organs (e.g., panicles). Incorporating the changes with the emergence of rice panicles into the DASF algorithm may generate reliable results in LNC estimation. Herein, we propose the PROSPECT-P model, which is based on the PROSPECT model and combines the panicle spectra to quantify the structural properties of the panicles and realize the simulation of the panicle albedo at different growth stages. Utilizing the spectral invariants theory, a panicle-leaf structure correction factor (DASFLP) was calculated based on canopy BRF, panicle albedo, leaf albedo, and canopy component fraction. CSC after correction for panicle and leaf structure (CSCLP) from 400–2500 nm can be obtained by the ratio of BRF and DASFLP. The CSCLP was further subjected to continuous wavelet analysis (CWA) and achieved an accurate estimation of the LNC using four machine learning (ML) models. The results showed that when combined with eight wavelet features (WFs), CSCLP can accurately invert rice LNC using the random forest algorithm ( $ {R} ^{2} =0.81$ , RMSE =0.30, RE =14.02%), which was more exact than CSC ( $ {R} ^{2} =0.76$ , RMSE =0.33, RE =16.13%) that corrected only for leaf structure. Moreover, the results on UAV multispectral also showed that UAV-CSCLP predicted LNC by XGBoost model ( $ {R} ^{2} =0.61$ , RMSE =0.35, RE =17.27%) more accurately than the traditional method UAV-CSC ( $ {R} ^{2} =0.50$ , RMSE =0.40, RE =19.52%) on the independent test set. Herein, we propose the accurate inversion of crop growth parameters by remote sensing using PROSPECT-P to correct for panicle and leaf structure effects.