Evaluating the spatial transferability and temporal repeatability of remote-sensing-based lake water quality retrieval algorithms at the European scale: a meta-analysis approach
Evaluating the spatial transferability and temporal repeatability of remote-sensing-based lake water quality retrieval algorithms at the European scale: a meta-analysis approach
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DOI：
10.1080/01431161.2015.1054962
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发表时间：
2015-06
影响因子：
3.4
通讯作者：
E. Politi;M. Cutler;J. Rowan
中科院分区：
文献类型：
--
作者：
E. Politi;M. Cutler;J. Rowan
许多研究表明，应用遥感方法估算湖泊水质具有相当大的潜力。然而，这些方法在时间和空间上的可靠应用是复杂的，因为湖泊类型、传感器配置和提出的多种不同算法的多样性。这项研究测试了一个操作和46个经验算法，这些算法来自同行评议的文献，这些文献在独立研究中以叶绿素a（藻类生物量）和Secchi圆盘深度（SDD）（水透明度）的形式单独显示了估计湖泊水质特性的潜力。近一半（19）的算法是不适合使用的遥感数据可用于这项研究。利用Terra/Aqua卫星档案对其余28个进行了评估，以确定2001-2004年期间在四个试验湖（即Vänern、Vättern、日内瓦和巴拉顿）的准确性和可转移性方面表现最佳的算法。这些湖泊代表了欧洲大型湖泊类型的广泛连续性，在生态区域（纬度/经度和海拔），形态，混合制度和营养状态方面各不相同。所有的算法进行了测试，每个湖分别和相结合，以评估其在生态不同的网站的适用性程度。当所有四湖被组合成一个单一的数据集，大多数算法表现不佳，即使是特定的湖泊类型，在这项研究中评估的算法表现出的承诺。最初开发的富营养化湖泊的叶绿素a检索算法显示了最有前途的结果（R2 = 0.59）在贫营养湖泊。两个SDD检索算法，一个最初开发的混浊的湖泊和其他湖泊具有不同的特点，表现出良好的效果，在相对较少混浊的湖泊（R2 = 0.62和0.76，分别）。这里提出的结果突出了与遥感湖泊水质估计和高度的不确定性，由于各种限制，包括湖水的光学特性和方法的选择的复杂性。
Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods.