MAPPING THE SPATIAL DISTRIBUTION OF WATER QUALITY PARAMETERS IN LAKE NASSER USING REMOTE SENSING TECHNIQUE

Abstract

The main objective of this research is to use remote sensing in estimating water quality parameters in Lake Nasser as it is considered the main and strategic storage of fresh water in Egypt. Results of six field missions, measured in different seasons, are used with Envisat/MERIS match-up images to validate Case 2 water quality processors (Case2 Regional (C2R), Eutrophic Lake and boreal Lake),with and without correcting land adjacency effect using Improved Contrast between Ocean and Land (ICOL) processor, to estimate optical parameters, Total Suspended Solids (TSS) and Chlorophyll-a (Chl-a).Processors’ validation is based on using of some statistical measures. For TSS,results show that, Eutrophic lake processor without ICOL is suitable for the end of flood season, C2R and eutrophic lake processor without ICOL is suitable forthe falling period, whileall processors failed during rising period. For Chl-a,Eutrophic lake processor with ICOL indicates good resultsduring falling period and the end of flood season.Also, regression models are created to estimate optical and non-optical water quality parameters in differentflood seasons using atmospherically corrected MERIS images, where bands 5 and 6 are found to be the common predictors. Models are validated at the end of flood season and applied to 913 images for period from 2002 to 2012 to create time series curves/maps for each parameter, after excluding cloudy images. It is concluded that TSS and Total Phosphorus concentrations indicate increasing until 2005 and started to decrease again, Silica and TDS illustrate an opposite changing pattern and transparency doesn’t have a specific pattern of change. It is also concluded that period from September to November indicate the lowest cloud probability and is suitable for preforming optical measurement which helps in creating bio-optical model for Lake Nasser. Keywords: Remote Sensing, Water Quality, Lake Nasser, Case2 Water Quality Processors, regression models.