Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.
Iron-catalysed enantioselective carbometalation of azabicycloalkenes