Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted VIs (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACI _red-edge showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACI _red-edge and AAEVI yielded the highest accuracy ( R ² = 0.78, RMSE = 0.26%, and rRMSE = 10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels ( R ² <0.40). These findings illustrated that the AAVIs have great potential in monitoring nitrogen status at early growth stages with high-resolution aerial or satellite images in the context of precision crop management.

中文翻译：

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AAVI：一种利用无人机早期和中期生长阶段多光谱图像估算水稻叶片氮浓度的新方法

准确及时地监测水稻叶片氮浓度（LNC）对于优化氮肥管理和减少环境污染至关重要。现有的植被指数 (VI) 通常在高冠层覆盖条件下表现良好，但由于背景材料的显着暴露和诱导的光谱混合效应，它们的性能在早期生长阶段变得很差。本研究提出了一种新方法，通过使用来自无人机 (UAV) 多光谱图像的丰度调整 VI (AAVI) 来估计水稻早期和中期生长阶段的 LNC。AAVI 是通过结合传统的 VI 和从无人机图像的线性光谱混合分析中丰富的水稻来构建的。随后，将 AAVI 的性能与从所有像素或绿色像素派生的传统 VI 进行比较，以针对单个生长阶段或多个阶段进行评估。结果表明，AAVIs 在 LNC 估计中表现出更好的性能，无论是各个阶段还是整个早期季节。特别地，AACI _red-edge显示了在评估 LNC 估计的 AAVI 中的最佳性能。对于早期阶段的通用建模，AACI _red-edge和 AAEVI的组合 产生了最高的准确度（ R ² = 0.78、RMSE = 0.26% 和 rRMSE = 10.4%）在所有像素或绿色像素（ R ² <0.40)。这些发现表明，在精确作物管理的背景下，AAVI 在通过高分辨率航空或卫星图像监测早期生长阶段的氮状态方面具有巨大潜力。