Inversion of nitrogen concentration in crop leaves based on improved radiative transfer model

The timely and accurate prediction of nitrogen status within crops can provide certain data support for precision fertilization. However, few studies have considered using radiative transfer models to estimate the nitrogen concentration ($Cn$) of crops. This study is based on the PIOSL-5 model to generate a large number of simulation datasets, namely the PIOSLSD dataset. The successive projections algorithm (SPA) is used to select nitrogen-related features. A crop $Cn$ inversion model based on the PIOSL-5 model is constructed using five models: Extreme Learning Machine, Genetic Algorithm Optimized Extreme Learning Machine, Particle Swarm Optimization Optimized Extreme Learning Machine, Third Generation Non Dominated Genetic Algorithm Optimized Extreme Learning Machine (NSGA-III-ELM), and Bat Algorithm Optimized Extreme Learning Machine. The model is compared with traditional data-driven methods and the accuracy of the model is verified using three datasets: RICE23, LOPEX93, and CALIFORNIA. The results showed that the nitrogen characteristic bands of the PIOSLSD dataset filtered by SPA were 1070, 1150, 1405, 1535, and 1725 nm. The Cn prediction based on the NSGA-III-ELM model, which uses these 5 feature bands as inputs, has the best performance. The determination coefficients of the validation set are 0.814, 0.785, and 0.792, respectively. The $Cn$ inversion model based on the PIOSL-5 model constructed in this article achieves remote sensing prediction of crop nitrogen mechanism model, which has certain mechanistic significance for nitrogen nutrition management of crops and improving nitrogen utilization efficiency.