作物冠层氮素营养的遥感诊断对指导作物精准施氮，提高作物氮效率和产量具有重要意义。本研究针对玉米冠层纵深大影响无人机估算氮浓度精度的问题，基于2022年和2023年不同氮肥运筹处理下田间无人机多光谱数据和氮浓度实测数据，分析玉米冠层氮浓度空间分布特征，并利用随机森林算法确定估算冠层氮浓度的有效叶层。进一步结合随机森林算法和多光谱植被指数构建有效叶层氮浓度估算模型，最终将有效叶层氮浓度转换到冠层尺度实现冠层氮浓度的估算。结果表明：(1) 九叶展期和大喇叭口期玉米冠层氮浓度表现为上层叶片>中层叶片>下层叶片，吐丝期和乳熟期表现为中层叶片>上层叶片>下层叶片。(2) 各时期估算冠层氮浓度的有效叶层分别为下层、中层、中层和中层。与支持向量回归模型相比，随机森林回归估算冠层氮浓度的精度较高。(3) 结合随机森林算法，基于有效叶层氮浓度估算冠层氮浓度的平均RMSE、NRMSE和MAE分别为0.10%、4.41%和0.07%，而直接基于植被指数估算冠层氮浓度的平均RMSE、NRMSE和MAE分别为0.19%、9.00%和0.15%。综上，玉米冠层氮浓度存在空间分异特征，估算冠层氮浓度时考虑基于随机森林和植被指数估算的有效叶层氮浓度能明显提高冠层氮浓度的估算精度。本研究确定的考虑空间分异的冠层氮浓度估算框架可为玉米氮素营养实时诊断提供理论支撑。
Remote sensing diagnosis of crop canopy nitrogen nutrition is crucial for guiding precise nitrogen application and improving 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 nitrogen concentration. This analysis was based on multi-spectral data and measured nitrogen concentration data from UAVs 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 concentration at each growth stage were the lower layer, middle layer, middle layer, and middle layer, respectively. The random forest regression 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 concentration, established in this study can provide theoretical support for real-time nitrogen nutrition diagnosis of maize.