Water quality and Sea Surface Temperature mapping using NOAA AVHRR data

Abstract

Environmental pollution is coeval with the appearance of humans. Water pollution problem becomes increasingly critical in this present-day, whether in developed or developing countries. Sediment is the primary cause of water pollution. The environmental pollution problem can be measured using ground instruments such as turbidity meters for water measurements. Field measurements cannot provide fine spatial resolution maps with detailed distribution pattern over a large study area. The study was carried out to verify the validity of National Oceanic and Atmospheric Administration Multi-Channel Sea Surface Temperature (SST) (NOAA MCSST) algorithm by NOAA at South China Sea. SST is verified by comparing the SST calculated by algorithm with sea-truth data collected by Research on the Sea and Islands of Malaysia (ROSES). ROSES had travelled and collected data at South China Sea from 26 Jun 2004 to 1 August 2004. In this study the transmittance function for each band was modeled using the MODTRAN code and radiosonde data. The expression of transmittance as a function of zenith view angle was obtained for each channel through regression of the MODTRAN output. The in-situ data (ship collected SST values) were used for verification of results. The derived SST value was compared with the ground truth data collected during Research on the Seas and Islands (ROSES) project and the standard deviation is less than 1 degree Celsius. SST map was created and comparison between the in-situ SST patterns was made in this study. The satellite NOAA AVHRR data used in SST analysis was used for water quality mapping. The DN values were converted into radiance values and later reflectance values - AVHRR Radiometric Correction and Calibration. The reflectance values corresponding to the ground truth sample locations were extracted from all the images. In this study, the multidate data were corrected to minimize the difference in atmospheric effects between the scenes. The reflectance values for window size of 3 by 3 were used because the data set produced higher correlation coefficient and lower RMS value. Finally, an automatic geocoding technique from PCI Geomatica 10.1 - AVHRR Automated Geometric Correction was applied in this study to geocode the SST and TSS maps.