Feasibility Analysis of MERIS as a Tool for Monitoring Lake Guiers (Senegal) Water Quality

ENVISAT/MERIS scenes of Lake Guiers covering the period 2003-2010 were processed for concentration retrieval of chlorophyll a (CHLa), suspended particulate matter (SPM) and colored fraction of dissolved organic matter (CDOM), i.e. the three main parameters relevant to the water quality management of the lake. Estimates in the range of 30 - 117 μg CHLa L-1 (average 62.13 μg·L-1), 0.10 - 29.0 mg SPM L-1 (average 22.01 mg·L-1), and 1.10 - 1.90 CDOM m-1 (average 1.33 m-1) were recorded, suggesting the possibility of occasional poor quality waters in some compartments of the lake. The values calculated as part of this study are consistent with literature data. On the basis of these estimates, interpretations were made as to the feasibility of applying MERIS data for synoptic environmental monitoring purposes. The data were subjected to statistical analysis, including regression analysis and significance tests. Estimates of CHLa and CDOM revealed some level of correlation, which suggests that phytoplankton biomass degradation may account for nearly 47% of the dissolved optical compounds CDOM. Notable areas of high CHLa and CDOM concentrations are found in the southern inshore zone, an environment with less water agitation. In contrast, SPM concentrations tend to increase in environments of very shallow water marked by high water turbulence and bottom mobility. However, it was not possible to fully assess the model performance and detection accuracy of the results due to lack of ground truths. Nonetheless, the results show concentrations that compared well with the insitu data from earlier studies and data reported elsewhere from other lacustrine systems. Therefore, it can be inferred from this study that MERIS data present a useful low-cost (i.e. cost effective and readily available) approach for environmental monitoring of Lake Guiers waters with excellent spatial coverage. In addition, the study highlighted the minimal effect of the so-called “bottom effect” on model predictions, despite the small depth of the lake.