Introduction

Optimizing fruit quality and yield in agriculture requires accurately monitoring leaf nitrogen (N) status spatially and temporally throughout the growing season. Standard remote sensing approaches for assessing leaf N rely on proxies like vegetation indices or leaf chlorophyll a + b (C_ab) content. However, limitations exist due to the C_ab-N relationship’s saturation and early nutrient deficiency insensitivity.

Methods

The study utilized Sentinel-2 satellite imagery to estimate a set of plant biochemical traits in large almond orchards in a two-year study. These traits, including leaf dry matter, leaf water content, and leaf C_ab retrieved from the radiative transfer model, were used to explain the observed variability of leaf N. Airborne hyperspectral imagery-derived leaf N using C_ab and solar-induced fluorescence served as a benchmark for validation.

Results

Results demonstrate that plant traits quantified from Sentinel-2 were strongly associated with leaf N variability across the orchard, with a strong contribution from the estimated leaf C_ab content and leaf dry matter biochemical constituent, outperforming the consistency of vegetation indices. The Sentinel-2 model explaining leaf N variability yielded r² = 0.82 and nRMSE = 13% in a two-year dataset, obtaining consistent performance and trait contribution across both years.

Conclusion

This study highlights the potential application of Sentinel-2 satellite imagery for monitoring leaf N variability in almond tree orchards. Incorporating plant biochemical traits allows for a more consistent and reliable prediction of leaf N compared to traditional vegetation indices over two years, making it a promising method for precision agriculture applications.

中文翻译：

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评估源自 Sentinel-2 的植物性状以表征杏仁园叶片氮的变异性：使用机载高光谱图像进行建模和验证

 介绍

优化农业水果质量和产量需要在整个生长季节从空间和时间上准确监测叶片氮 （N） 状态。评估叶片 N 的标准遥感方法依赖于植被指数或叶片叶绿素 a + b （C_ab） 含量等指标。然而，由于 C_ab-N 关系的饱和度和早期营养缺乏不敏感性，存在局限性。

 方法

该研究利用 Sentinel-2 卫星图像在一项为期两年的研究中估计了大型杏仁园中的一组植物生化特征。这些性状，包括叶片干物质、叶片含水量和从辐射传输模型中检索的叶片 C_ab，用于解释观察到的叶片 N 的变化。使用 C_ab 和太阳诱导荧光的航空高光谱图像衍生的叶片 N 作为验证的基准。

 结果

结果表明，从 Sentinel-2 量化的植物性状与整个果园的叶片氮变异性密切相关，估计的叶片 C_ab 含量和叶片干物质生化成分的贡献很大，优于植被指数的一致性。解释叶片 N 变异性的 Sentinel-2 模型在两年数据集中产生 r² = 0.82 和 nRMSE = 13%，在两年中获得了一致的性能和性状贡献。

 结论

本研究强调了 Sentinel-2 卫星图像在监测杏仁树园叶片 N 变化方面的潜在应用。与传统植被指数相比，结合植物生化性状可以在两年内对叶片氮进行更一致和可靠的预测，使其成为精准农业应用的有前途的方法。