Diagnosis of maize nitrogen status under contrasting water and nitrogen regimes using a canopy-stratified and multi-source remote sensing framework

Abstract

Maize leaf nitrogen exhibits significant vertical heterogeneity within the canopy, which often compromises the accuracy of remote sensing-based nitrogen status diagnosis. To address the limited consideration of leaf-layer contributions and multi-source feature integration, this study proposed a three-dimensional coupled nitrogen diagnosis framework integrating progressive leaf-layer labeling, multi-source feature fusion, and machine learning modeling, based on spring maize experiments under various water and nitrogen regimes in Xinjiang, China. Stratified ground sampling was conducted to obtain leaf nitrogen weight (LNW) from the upper, middle, and lower canopy layers, and 22 vegetation indices (VIs), eight texture features (TFs), and 15 texture indices (TIs) were extracted from UAV multispectral imagery to construct a multi-source feature set. Results demonstrated that the combination of upper and middle canopy leaves best represented overall plant nitrogen status. Feature fusion significantly enhanced model performance, with extreme gradient boosting achieving the highest estimation accuracy for the nitrogen nutrition index (NNI) (R² = 0.68, RPD = 1.77), and convolutional neural networks performing best in LNW estimation (R² = 0.83, RPD = 2.31). The critical nitrogen dilution curves derived from the estimated LNW revealed that irrigation levels significantly influenced the curve intercepts and slopes, highlighting the dual regulatory effects of water on nitrogen uptake and dilution. Coupled with ArcGIS-based spatial visualization, the framework enabled dynamic monitoring of NNI across V6, VT, R3, and R6 growth stages. Overall, this framework effectively improved both the spatiotemporal resolution and estimation accuracy of maize nitrogen status, providing a theoretical basis and technical support for precision nitrogen management and the transition toward sustainable agriculture.

Plant phenotyping relevance

UAVマルチスペクトル画像と多源特徴融合・機械学習により、トウモロコシの葉窒素量および窒素栄養状態を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。

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