Abstract 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.

中文翻译：

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使用遥感监测内陆水质：光谱指数、生物光学模拟、机器学习和云计算的潜力和局限性

摘要 鉴于遥感分析、云计算和机器学习的最新进展，在水资源管理和决策的背景下，评估遥感对水质监测的能力势在必行。本次审查的目的是分析水质遥感的最新进展，确定当前系统、估计方法的局限性，并提出未来的改进建议。为此，我们收集了200多组水质数据，包括蓝藻藻蓝蛋白（BGA-PC）、叶绿素-a（Chl-a）、溶解氧（DO）、比电导率（SC）、荧光溶解有机物(fDOM)、浊度和 2016 年至 2018 年的污染沉积物。水质数据，通过对分布在美国中西部八个湖泊和河流的抓样和原位实时监测传感器的实验室分析生成的数据与同步近端光谱、三脚架安装的高光谱图像和卫星数据配对。结果表明，近端和基于卫星的传感器都具有提供光学有源参数准确估计的巨大潜力，非光学有源参数的遥感可以间接估计，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，为遥感在水质监测和决策中的实际应用提供了有希望的可能性。作为深度神经网络水质估计的第一次回顾，