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首页> 外文期刊>Earth-Science Reviews: The International Geological Journal Bridging the Gap between Research Articles and Textbooks >Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
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Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

机译：使用遥感监测内陆水质：光谱指标，生物光学模拟，机器学习和云计算的潜在和限制

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Given the recent advances in remote sensing analytics, cloud computing, and machine learning, it is imperative to evaluate capabilities of remote sensing for water quality monitoring in the context of water resources management and decision-making. The objectives of this review were to analyze recent advances in water quality remote sensing and determine limitations of current systems, estimation methods, and suggest future improvements. To that end, we collected over 200 sets of water quality data including blue-green algae phycocyanin (BGA-PC), chlorophyll-a (Chl-a), dissolved oxygen (DO), specific conductivity (SC), fluorescent dissolved organic matter (fDOM), turbidity, and pollution-sediments from 2016 to 2018. The water quality data, generated from laboratory analysis of grab samples and in situ real-time monitoring sensors distributed in eight lakes and rivers in Midwestern United States, were paired with synchronous proximal spectra, tripod-mounted hyperspectral imagery, and satellite data. The results showed that both proximal and satellite-based sensors have great potential to provide accurate estimate of optically active parameters, and remote sensing of non-optically active parameters may be indirectly estimated but still remains a challenge. Data-driven empirical approaches, i.e., deep learning outperformed the other competing methods, providing promising possibility for operational use of remote sensing in water quality monitoring and decision-making. As the first-time review of deep neural networks for water quality estimation, the paper concludes that anomaly detection utilizing multi-sensor data fusion and virtual constellation in cloud-computing is the most promising means for predicting impending water pollution outbreaks such as algal blooms.
机译：鉴于近期遥感分析，云计算和机器学习的进步，因此在水资源管理和决策背景下，必须评估遥感水质监测的能力。本综述的目标是分析最近水质遥感的进步，并确定当前系统的限制，估算方法，并提出未来的改进。为此，我们收集了200套水质数据，包括蓝绿藻植物（BGA-PC），叶绿素-A（CHL-A），溶解氧（DO），比电导率（SC），荧光溶解有机物质（FDOM），浊度和污染沉积物从2016到2018年。从抓取样品的实验室分析和在美国八个湖泊和河流中分布的实验室分析中产生的水质数据与同步搭配近端光谱，三脚架安装的高光谱图像和卫星数据。结果表明，近端和卫星的传感器具有极大的潜力，可以提供精确的光学活动参数估计，并且可以间接估计非光学活动参数的遥感，但仍然是一个挑战。数据驱动的经验方法，即深度学习优于其他竞争方法，提供了在水质监测和决策中进行遥感的操作使用的有希望的可能性。作为对水质估算深神经网络的第一次审查，该论文得出结论，利用多传感器数据融合和虚拟星座在云计算中的异常检测是预测即将到来的水污染爆发等藻类绽放的最有希望的手段。

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