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 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

Dynamic biogeochemical provinces in the global ocean

In recent decades, it has been found useful to partition the pelagic environment using the concept of biogeochemical provinces, or BGCPs, within each of which it is assumed that environmental conditions are distinguishable and unique at global scale. The boundaries between provinces respond to features of physical oceanography and, ideally, should follow seasonal and interannual changes in ocean dynamics. But this ideal has not been fulfilled except for small regions of the oceans. Moreover, BGCPs have been used only as static entities having boundaries that were originally established to compute global primary production. In the present study, a new statistical methodology based on non-parametric procedures is implemented to capture the environmental characteristics within 56 BGCPs. Four main environmental parameters (bathymetry, chlorophyll a concentration, surface temperature, and salinity) are used to infer the spatial distribution of each BGCP over 1997–2007. The resulting dynamic partition allows us to integrate changes in the distribution of BGCPs at seasonal and interannual timescales, and so introduces the possibility of detecting spatial shifts in environmental conditions