Search

Semi-analytical remote sensing applications for eutrophic waters are not applicable to oligo- and mesotrophic lakes in the perialpine area, since they are insensitive to chlorophyll concentration variations between 1-10 mg/m3. The neural network based Case-2-Regional algorithm for MERIS was developed to fill this gap, and a dedicated validation campaign on Lake Constance revealed promising results. For a more extensive validation, the algorithm is applied to a collection of 239 satellite images from 2003-2008, and the results are compared to experimental and official water quality data collected on seven perialpine lakes in the same period. It is shown that remote sensing estimates can provide an adequate supplementary data source to fluorescence probe monitoring data series of the top 5-10 m water layer, provided that a sufficient number of matchups for a site specific maximum temporal offset is available. Comparison with 20 m mixed profiles is less adequate. The supplemental correction of adjacency effects by the Improved Contrast between Ocean and Land algorithm performs well regarding the retrieval of accurate remote sensing reflectances, while its improvement to the accuracy of estimated chlorophyll concentrations is less significant.