高光谱遥感技术在作物水氮监测中的应用：方法、模型及挑战

Applications of hyperspectral remote sensing in crop water and nitrogen monitoring：Methods，models and challenges

关键词:  高光谱遥感  作物水氮监测  机器学习  跨学科技术融合  数据共享  精准农业

Key words:  hyperspectral remote sensing  crop water and nitrogen monitoring  machine learning  interdisciplinary integration  data sharing  precision agriculture

摘要:

【目的】 高光谱遥感技术以其光谱分辨率高、覆盖范围广、数据获取便捷等优势，在作物水氮监测中展现出巨大潜力。文章综述了高光谱遥感技术在作物水氮监测中的应用现状，分析了当前面临的主要挑战，并提出未来研究方向。【方法】 通过梳理国内外相关文献，系统总结农作物水氮监测方法，重点探讨了基于高光谱数据的水氮监测技术。【结果】 （1）相比于传统的农学指标监测方法，高光谱遥感技术显著提升了作物水氮状况的监测精度和效率，在叶片尺度、冠层尺度和多角度观测上取得显著进展。（2）现有监测模型主要包括经验统计模型、光学物理模型、植被指数法和机器学习模型，各具优势，但普适性和参数优化不足限制了其应用。（3）技术发展面临模型复杂性高、数据质量差、普适性低及估算精度不足等挑战。【结论】 为提高高光谱遥感技术在作物水氮监测中的应用效果，未来研究应注重模型普适性与精度提升、加强数据与作物模型及跨学科技术融合以及拓展数据共享与应用领域，以此推动该技术在精准农业中的广泛应用。

Abstract:

[Purpose] Hyperspectral remote sensing（HSRS）technology，with its advantages of high spectral resolution，wide coverage，and convenient data acquisition，shows great potential in monitoring crop water and nitrogen status. This study systematically reviews the current applications of HSRS in crop water and nitrogen monitoring，analyzes the main challenges，and proposes future research directions to improve its effectiveness in precision agriculture.[Method] Through a literature review，this paper summarized recent applications of HSRS in crop water and nitrogen monitoring，with a focus on key models and methods based on hyperspectral data.[Result] Compared to traditional agronomic indicator monitoring methods，HSRS had significantly improved the accuracy and efficiency of monitoring crop water and nitrogen status，achieving remarkable progress at leaf scale，canopy scale，and in multi-angle observations. Current monitoring models，including empirical statistical models，optical physical models，vegetation index methods，and machine learning models，had distinct advantages but were constrained by insufficient generalizability and parameter optimization. Additionally，the development of this technology faced challenges such as high model complexity，poor data quality，limited generalizability，and insufficient estimation accuracy.[Conclusion] To improve the effectiveness of HSRS in crop water and nitrogen monitoring，future research should focus on enhancing model generalization and accuracy，strengthening the integration of data with crop models and interdisciplinary technologies，and expanding data sharing and application fields to promote the widespread use of this technology in precision agriculture.