Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany.
Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany.
Int J Environ Res Public Health. 2018 Aug 30;15(9):1881. doi: 10.3390/ijerph15091881.
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June⁻12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.
内陆水域对科学家和当局都非常重要，因为它们是重要的生态系统，以其生物多样性而闻名。在监测各自的水质时，水质参数的现场测量在空间上受到限制，成本高且耗时。在本文中，我们提出了一种组合使用高光谱数据和机器学习方法来估计和监测水质的不同参数。与通常应用的技术（如波段比）相比，这种方法是数据驱动的，不依赖于任何领域知识。我们专注于 CDOM、叶绿素和浊度以及两种藻类（硅藻和绿藻）的浓度。为了研究我们建议的潜力，我们依赖于 2017 年 6 月 24 日至 7 月 12 日在德国易北河使用三个不同传感器测量的数据。详细描述了带有两个探头传感器和高光谱传感器的测量设置。为了估计这五个提到的变量，我们提出了一个适当的回归框架，涉及十个机器学习模型和两种预处理方法。这允许评估每个模型和变量的回归性能。对于每个变量，性能最佳的模型的决定系数 R 2 在 89.9%到 94.6%之间。这清楚地揭示了使用高光谱数据进行机器学习方法的潜力。在进一步的研究中，我们专注于回归框架的推广，为其应用于不同类型的内陆水域做准备。
