Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS
Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS
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DOI：
10.1016/j.rse.2010.04.013
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发表时间：
2010-09
影响因子：
13.5
通讯作者：
M. Matthews;S. Bernard;K. Winter
中科院分区：
文献类型：
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作者：
M. Matthews;S. Bernard;K. Winter
富营养化和蓝藻水华对淡水生态系统的健康以及利用这些资源进行饮用和娱乐的人类构成越来越大的威胁。遥感正越来越多地用作监测内陆和近海岸沃茨这些现象的一种工具。本研究使用中分辨率成像光谱仪（MERIS）查看Zeekoevlei，一个小型的富营养化淡水湖位于开普敦平原在开普敦，南非，微囊藻蓝藻为主。湖泊的小尺寸，高度浑浊的水，和协变水成分提出了一个具有挑战性的情况下，算法的开发和大气校正。这项研究的目的是评估湖泊的光学特性，评估各种大气校正程序，并比较经验和半分析算法在富营养化水体中的性能。在MERIS立交桥上同时进行了现场水质参数和辐射测量。对0.66 m深度的上升流辐射测量值进行仪器自遮蔽校正，并使用下降流辐射测量值和上升流辐射垂直衰减系数Ku的估计值处理离开水面的反射率，Ku是从一个简单的生物光学模型估计总吸收系数a（λ）和后向散射系数bb（λ）生成的。归一化的离水反射率用于评估基于图像的暗物体减法和6S辐射传输代码大气校正程序应用于MERIS的准确性。根据同时采集的现场和MERIS测量数据，推导了估算叶绿素a（Chl a）、总悬浮固体（TSS）、Secchi圆盘深度（zSD）和CDOM吸收（aCDOM）的经验算法。经验算法给出了高的相关系数值，虽然它们具有有限的能力，从协变水成分之间的信号分离。在标准2级案例2沃茨产品和富营养化湖泊处理器中使用的MERIS神经网络算法也用于推导水成分浓度。然而，由于大气校正失败以及用于训练算法的光学属性和范围与Zeekoevlei的光学属性和范围之间的分歧，这些未能与现场测量进行合理的比较。使用经验算法制作的地图有效地显示了2008年4月期间水质参数的时空变异性。结果的基础上，有人认为，MERIS是目前最佳的传感器，在内陆沃茨频繁变化检测应用。这项研究也表明了相当大的潜在价值，简单的TOA算法的肥大系统。建议将南部非洲的区域算法开发列为优先事项，并将遥感纳入今后的实际水质监测系统。
Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwater ecosystems and to humans who use these resources for drinking and recreation. Remote sensing is being used increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This study uses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophic freshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria. The lake's small size, highly turbid water, and covariant water constituents present a challenging case for both algorithm development and atmospheric correction. The objectives of the study are to assess the optical properties of the lake, to evaluate various atmospheric correction procedures, and to compare the performance of empirical and semi-analytical algorithms in hypertrophic water. In situ water quality parameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwelling radiance measurements at depth 0.66m were corrected for instrument self-shading and processed to water-leaving reflectance using downwelling irradiance measurements and estimates of the vertical attenuation coefficient for upward radiance, Ku, generated from a simple bio-optical model estimating the total absorption, a(λ), and backscattering coefficients, bb(λ). The normalised water-leaving reflectance was used for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a (Chl a), Total Suspended Solids (TSS), Secchi Disk depth (zSD) and absorption by CDOM (aCDOM) were derived from simultaneously collected in situ and MERIS measurements. The empirical algorithms gave high correlation coefficient values, although they have a limited ability to separate between signals from covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2 waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations. However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of atmospheric correction and divergence between the optical properties and ranges used to train the algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland waters. This study also demonstrates the considerable potential value for simple TOA algorithms for hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern Africa and that remote sensing be integrated into future operational water quality monitoring systems.