高光谱遥感技术在作物水氮监测中的应用研究：方法、模型及应用

Applications of hyperspectral remote sensing in crop water and nitrogen monitoring: methods, models and applications

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高光谱分辨率、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。本研究旨在系统综述高光谱遥感技术在作物水氮监测中的应用现状,分析当前面临的主要挑战,并提出未来研究方向。【方法】本文梳理了近年来农作物水氮监测研究进展,重点探讨了基于高光谱数据的水氮监测模型和方法。【结果】研究表明,高光谱遥感技术在作物水氮监测中的应用已取得显著进展,尤其是在水氮状况的非破坏性监测和空间分布评估方面。然而,当前模型的普适性和精度仍然不足,难以适应不同作物类型和环境条件。数据质量、模型参数优化和算法选择是影响估算精度的主要因素。此外,遥感信号与作物生理特征之间的物理意义解读仍需进一步深入研究。【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,未来研究应着重构建更具普适性的模型,优化数据质量和参数选择,并结合无人机和机器学习技术,提升模型的估算精度。同时,建立高光谱数据共享平台,制定数据标准,有助于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, focusing on key models and methods based on hyperspectral data, including spectral information empirical statistical methods, optical physical model analysis, and vegetation index methods. The analysis highlighted key issues, such as model complexity, difficulties in parameter acquisition, and the inadequacy of physical interpretation. [Result] The findings showed that significant progress has been made in applying HSRS to non-destructive monitoring of water and nitrogen status and in assessing their spatial distribution. Machine learning has significantly enhanced the precision and generalization of hyperspectral data processing. However, the current models still lack generalizability and accuracy, making them difficult to adapt to different crop types and environmental conditions. Challenges remained in model structure, parameter acquisition and optimization, in-depth physiological interpretation, and improving data quality. [Conclusion] To improve the effectiveness of HSRS in crop water and nitrogen monitoring, future research should strengthen the integration of hyperspectral data with crop growth models. By combining remote sensing data with crop growth models and interdisciplinary technologies, data quality and parameter selection can be optimized. Additionally, the development of a hyperspectral data-sharing platform and the establishment of data standards will be essential to promote broader adoption of HSRS in precision agriculture.