Turbidity measurement from ALOS satellite imagery

Abstract

Remote sensing data have been widely used in environmental studies like land cover change, flood observation, environmental pollution monitoring. The turbidity is commonly measured by turbidity meter. However, it is only accurate for single point measurement but ineffective for wide spatial and temporal coverage. The launching of Earth Observation Satellites (EOS) of ALOS by carrying high spectral resolution sensors provided the solutions. The ideal sensor for remote sensing is high spectral, spatial and temporal resolution. Water samples of turbidity measurements were collected using a small boat simultaneously with the acquisition of the satellite image. The water locations were determined using a handheld Global Positioning System (GPS). The study area is Penang Island, Malaysia which is situated within latitudes 5° 12' N to 5° 30' N and longitudes 100° 09' E to 100° 26' E. The digital numbers were extracted corresponding to the ground-truth locations for each band and then converted into radiance values and reflectance values. The reflectance values were used for calibration of the developed algorithm. A simple atmospheric correction, namely darkest pixel technique was performed in this study. This is a very simple correction, based on 2 assumptions: ● The first assumption is that in the darkest water pixel of the image there is total light absorption and the radiation light recorded by this pixel comes from the atmospheric path radiance. ● Secondly it is assumed that the atmospheric path radiance is uniform all over the image. The radiation of the darkest water pixel (assumed to represent the atmosphere) is subtracted from the whole image. The darkest pixel is found by searching for the lowest values over water for all wavelengths. The pixel with the lowest value for each band was selected as the darkest pixel. The turbidity estimated from algorithm by ALOS image gave a higher correlation compared with in situ turbidity measurements. The efficiency of the proposed algorithm was investigated based on the observations of correlation coefficient (R) and root-mean-square deviations (RMS) with the sea-truth data. The proposed algorithm is considered superior to other tested algorithms based on the values of the correlation coefficient, R=0.91 and root-mean-square error, RMS=2 NTU. This algorithm was then used to map the turbidity distribution over Penang, Malaysia. The turbidity map was color-coded and geometrically corrected for visual interpretation.