摘要:This study investigates the potential for remote sensing of lake water bathymetry and geochemical by 1) examining the empirical based technique for retrieving depth information from passive optical image worldview-2 satellite data, 2) performing atmospheric correction, 3) assessing the accuracy of spectrally based depth retrieval under field condition via field measurement, 4) producing bathometry and geochemistry mapping by examining spectral variations for identifying pairs of wavelengths that produce strong linear correlation coefficient between the band ratio. The results indicate that optical remote sensing of bathymetry and geochemical investigation is not only feasible but more accurate under conditions of typical lake water, supporting field survey. The Pearson correlation matrix (R) between the examined water samples/depth and the TOA reflectance values of the worldview-2 (WV-2) satellite data have been investigated and found good correlation. The models developed using the combination of different band pairs also show high accuracy. Cartographical maps were generated depending on the linear correlation coefficient between the measured parameters and the TOA reflectance values of the worldview-2 data. The investigation shows that dissolved oxygen (DO) of the lake water is slight lower than the permissible limit of Saudi standards for lake water. The shallow water has high DO concentration, whereas the deeper shows significantly lower down. Electrical conductivity measurements serve as a useful indicator of the degree of mineralization in the water sample. All the samples which have EC exceed limit. The spatial distribution of EC and TDS inferred that the EC and TDS concentration is the highest at the eastern part of the lake whereas concentration drops down towards the southern side. This study confirms that remote sensing incorporated with GIS and GPS could afford an integrated scheme for mapping water quality and bathometry of the surface water.
关键词:Geochemical Water Properties; Bathymetry; Worldview-2 Data; Remote Sensing and GIS