Evaluating the spatial transferability and temporal repeatability of remote sensing-based lake water quality retrieval algorithms at the European scale: a meta-analysis approach

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 showed potential for estimating lake water quality properties in the form of chlorophyll a (algal biomass) and Secchi disk depth, SDD (water transparency) in independent studies. Nearly half (19) algorithms were unsuitable for use with the remote sensing data available to this study. The remainder 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 dataset 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 Secchi disk depth 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 R2 = 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.