Abstract: Leaf Nitrogen Concentration (LNC) can play a crucial role in the nutrient activity of soybeans, ultimately leading to the photosynthetic efficiency and yield formation. UAV-based remote sensing technology has been commonly used to estimate crop phenotypic traits. However, the traditional remote sensing monitoring of crop nitrogen can usually rely on the spectral data from a single sensor. It is mostly limited to the canopy-level assessment. In this study, a systematic analysis of the vertical heterogeneity of nitrogen was carried out to integrate the UAV-derived multispectral and texture features in the flowering period of soybeans. UAV multi-spectral technology was also combined with two-year (2021–2022) field experiments. The LNC data of each leaf layer were collected with its UAV multi-spectral image data. Firstly, a preliminary analysis was performed on the soybean LNC data. It was found that the LNC concentration in the canopy was higher than that in the middle layer, while the LNC concentration in the bottom layer was the lowest. Secondly, 10 vegetation indices were selected and then calculated. 48 texture features were extracted from 8 second-order probability matrices of each band. Subsequently, 6 texture indices were constructed after random combinations. There were some correlations between the parameters and the LNC of each leaf layer of soybeans. The correlation coefficients of the parameters reached a significant level (P<0.05). Among them, 4 combinations were constructed to serve as the input variables for the model: Combination 1 was the vegetation indices; Combination 2 was the texture features; Combination 3 was the texture indices; and Combination 4 was the group of the vegetation indices, texture features, and texture indices. Three machine-learning models were used to simulate the LNC in each leaf layer of the soybeans, namely Random Forest (RF), Back Propagation Neural Network (BPNN), and Extreme Gradient Boosting (XGBoost). The results showed that the correlation coefficients of the most vegetation indices with the LNC of each leaf layer of soybeans were higher than those of the texture features, thereby reaching a significant correlation level (P<0.05). The correlations with the LNC of each leaf layer decreased in the order of the canopy, middle layer, and bottom layer. The texture indices after random combinations shared the highest correlation coefficients with the LNC in each leaf layer of the soybeans. Among them, the LNC of the canopy, middle, and bottom layers of soybeans had the highest correlation coefficients with the constructed Additive Texture Index (ATI), which were 0.774, 0.726, and 0.650, respectively. Once the single-source data (Combination 1, Combination 2, and Combination 3) was used as the input variables to construct the prediction models, all three machine-learning models showed that the performance of Combination 3 (texture indices) was better than that of Combination 1 (vegetation indices), while Combination 2 (texture features) had the worst performance. The best performance was achieved in the prediction models with the LNC of each leaf layer of soybeans that was constructed by the XGBoost model, when the input variables were Combination 4 (vegetation indices, texture features, and texture indices). The canopy-layer LNC prediction model demonstrated that the coefficient of determination (R²) of the validation set was 0.853, the Root Mean Square Error (RMSE) was 0.321%, and the Mean Relative Error (MRE) was 7.120%; The middle-layer LNC prediction model indicated that the R² of the validation set was 0.822, the RMSE was 0.349%, and the MRE was 7.448%; The bottom-layer LNC prediction model showed that the R² of the validation set was 0.809, the RMSE was 0.340%, and the MRE was 8.042%. The finding can provide a practical technical path to monitor the physiological growth of the crops using an unmanned multispectral platform. Meanwhile, a reliable technical basis can also offer to dynamically monitor the vertical distribution of nitrogen in the precise fertilization of modern agriculture.