Estimation of canopy nitrogen concentration in maize based on UAV multispectral data and spatial nitrogen heterogeneity
Remote sensing diagnosis of crop canopy nitrogen nutrition is crucial for guiding precise nitrogen application and im-proving crop nitrogen efficiency and yield.To address the issue of maize canopy depth significantly affecting the accuracy of UAV-based nitrogen concentration estimation,this study analyzed the spatial heterogeneity characteristics of maize canopy nitro-gen concentration.This analysis was based on multi-spectral data and measured nitrogen concentration data from UAV across fields with different nitrogen fertilizer treatments in 2022 and 2023.Using the random forest algorithm,we identified the effective leaf layer for estimating canopy nitrogen concentration.We further constructed an estimation model for effective leaf nitrogen concentration by combining the random forest algorithm with multi-spectral vegetation indices,and then converted the effective leaf nitrogen concentration to the canopy scale to estimate the overall canopy nitrogen concentration.The results were as follows:(1) The nitrogen concentration of the maize canopy at the 9-leaf extension and large trumpet stages was highest in the upper leaves,followed by the middle and lower leaves.At the silk-spinning and milk-ripening stages,the nitrogen concentration was highest in the middle leaves,followed by the upper and lower leaves.(2) The effective leaf layers for estimating canopy nitrogen concentra-tion at each growth stage were the lower layer,middle layer,middle layer,and middle layer,respectively.The random forest regres-sion model demonstrated higher accuracy in estimating canopy nitrogen concentration compared to the support vector regression model.(3) Using the random forest algorithm,the average RMSE,NRMSE,and MAE for estimating canopy nitrogen concentration based on effective leaf nitrogen concentration were 0.10％,4.41％,and 0.07％,respectively.In contrast,the average RMSE,NRMSE,and MAE for estimation based on direct vegetation indices were 0.19％,9.00％,and 0.15％,respectively.In conclusion,the study identified the spatial differentiation of maize canopy nitrogen concentration.Considering effective leaf nitrogen concentration based on random forest and vegetation index estimation significantly improved the accuracy of canopy nitrogen concentration estimation.The canopy nitrogen concentration estimation framework,which accounts for the spatial heterogeneity of canopy nitrogen concentra-tion,established in this study can provide theoretical support for real-time nitrogen nutrition diagnosis of maize.
万方数据
维普
