Abstract：
In recent years, increasing demand for inland river water quality precision management has heightened the necessity for real-time, rapid, and continuous monitoring of water conditions. By analyzing the optical properties of water bodies remotely, unmanned aerial vehicle (UAV) hyperspectral imaging technology can assess water quality without direct contact, presenting a novel method for monitoring river conditions. However, there are currently some challenges to this technology that limit the promotion application of this technology, such as underdeveloped sensor calibration, atmospheric correction algorithms, and limitations in modeling non-water color parameters. This article evaluates the advantages and disadvantages of traditional sensor calibration methods and considers factors like sensor aging and adverse weather conditions that impact calibration accuracy. It suggests that future improvements should target hardware enhancements, refining models, and mitigating external interferences to ensure precise spectral data acquisition. Furthermore, the article summarizes the limitations of various traditional atmospheric correction methods, such as complex computational requirements and the need for multiple atmospheric parameters. It discusses the evolving trends in this technology and proposes streamlining atmospheric correction processes by simplifying input parameters and establishing adaptable correction algorithms. Simplifying these processes could significantly enhance the accuracy and feasibility of atmospheric correction. To address issues with the transferability of water quality inversion models regarding non-water color parameters and varying hydrological conditions, the article recommends exploring the physical relationships between spectral irradiance, solar zenith angle, and interactions with water constituents. By understanding these relationships, more accurate and transferable inversion models can be developed, improving the overall effectiveness of water quality assessment. By leveraging the sensitivity and versatility of hyperspectral sensors and integrating interdisciplinary approaches, a comprehensive database for water quality assessment can be established. This database enables rapid, real-time monitoring of non-water color parameters which offers valuable insights for the precision management of inland river water quality.
摘要：
近年来,对内河水质精确管理的需求不断增加，提高了实时的必要性，快速,和持续监测水的状况。通过远程分析水体的光学性质,无人机(UAV)高光谱成像技术可以在不直接接触的情况下评估水质,提出了一种监测河流状况的新方法。然而,目前这项技术存在一些挑战，限制了这项技术的推广应用，如不足的传感器校准，大气校正算法，和非水色参数建模的局限性。本文评估了传统传感器校准方法的优缺点，并考虑了影响校准精度的传感器老化和不利天气条件等因素。它表明未来的改进应该以硬件增强为目标，精炼模型，和减轻外部干扰，以确保精确的光谱数据采集。此外，本文总结了各种传统大气校正方法的局限性，例如复杂的计算要求和对多个大气参数的需要。它讨论了该技术的发展趋势，并提出通过简化输入参数和建立适应性校正算法来简化大气校正过程。简化这些过程可以显着提高大气校正的准确性和可行性。为了解决有关非水色参数和变化的水文条件的水质反演模型的可转移性问题，本文建议探索光谱辐照度之间的物理关系，太阳天顶角,以及与水成分的相互作用。通过理解这些关系，可以开发更准确和可转移的反演模型，提高水质评价的整体效果。通过利用高光谱传感器的灵敏度和多功能性，并整合跨学科方法，可以建立一个全面的水质评估数据库。这个数据库可以快速，实时监测非水色参数，为内河水质的精确管理提供有价值的见解。
