###### Abstract  
Remote sensing estimation of crop nitrogen content allows real-time monitoring of growth to develop scientific methods. However, most of the current remote sensing estimates of crop nitrogen contents have limitations in accurately reflecting the vertical distribution of nutrients in plants. Firstly, the original hyperspectrum is first-order differential (FD), second-order differential (SD), and continuous removal (CR), and the corresponding sensitive bands were screened by correlation analysis in this paper. Then, the spectral reflectance, vegetation indices, and wavelet coefficients were used as input features to construct models for estimating nitrogen content of flag leaf, upper 1 leaf, upper 2 leaf, upper 3 leaf, and upper 4 leaf based on partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and multiple linear regression (MLR), respectively. The results showed that the accuracy of nitrogen content prediction based on wavelet coefficients was the highest. The combination of MLR and SVM with wavelet coefficients had high accuracy and robustness in the prediction of nitrogen content at different leaf positions. Additionally, the prediction accuracy of nitrogen gradually increased as the leaf position of winter wheat increased. The study can provide technical support for remote sensing estimation of nutrient elements at vertical leaf position of crops. The study can provide a reference for prediction of other crops.  
hyperspectral remote sensing; machine learning; nitrogen; wheat Article
During the growth of winter wheat, the population exhibits different canopy spatial structures, and there are characteristics of vertical distribution of nitrogen in the crop and corresponding changes in chlorophyll, etc., in the middle and lower leaves of winter wheat as the reproductive period progresses [7]. Therefore, the study of vertical heterogeneity of biochemical parameters of winter wheat leaves is important for monitoring various growth conditions such as the growth potential of winter wheat. Previously, some studies have reported the vertical estimation of nitrogen and chlorophyll level using hyperspectral information at different leaf positions in wheat. [PERSON] et al. [8] have studied the vertical distribution of nitrogen and chlorophyll in winter wheat leaves under different nitrogen application levels through field experiments. [PERSON] et al. [9] showed that there were differences in nitrogen and chlorophyll in cotton leaves in various leaf positions. The above results demonstrated that increment in nitrogen fertilizer application rate could enhance nitrogen content across the leaves for better photosynthesis activity in plants [10]. However, most of the existing studies mainly focus on the estimation of nitrogen in crop canopy, as well as the difference patterns and spectral characteristics of nitrogen distribution in different leaf positions. To our knowledge, there are few studies using remote sensing technology to monitor the growth parameters of crops at different leaf positions.  
The main objectives of this study are as follows: (1) to analyze the differences in spectral variation characteristics of physiological parameters at different leaf positions in winter wheat; (2) to evaluate the ability of spectral reflectance, vegetation index, and wavelet transform coefficients for estimating N content at different leaf positions; and (3) to screen optimal estimation models for N content at different leaf positions based on four machine learning algorithms. This paper can provide theoretical and technical references for studying the vertical variation characteristics of other crop growth parameters using remote sensing technology.