OCEAN COLOR RETRIEVAL USING LANDSAT-8 IMAGERY IN COASTAL CASE 2 WATERS (CASE STUDY PERSIAN AND OMAN GULF)
Authors: Moradi N. , Hasanlou M. , Saadatseresht M. 
Published: August 2016

Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (&lt;i&gt;chla&lt;/i&gt;), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms &lt;i&gt;chla&lt;/i&gt; related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;2&lt;/sub&gt;)/𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;1&lt;/sub&gt;), the three bands ratio with variable [𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;1&lt;/sub&gt;)−1−𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;2&lt;/sub&gt;)−1]×𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;3&lt;/sub&gt;) and four bands ratio with variable [𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;1&lt;/sub&gt;)−1−𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;2&lt;/sub&gt;)−1]/[𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;4&lt;/sub&gt;)−1−𝑅&lt;sub&gt;𝑟𝑠&lt;/sub&gt;(λ&lt;sub&gt;3&lt;/sub&gt;)−1] that desired wavelength (i.e. λ&lt;sub&gt;1&lt;/sub&gt;, λ&lt;sub&gt;2&lt;/sub&gt;, λ&lt;sub&gt;3&lt;/sub&gt; and λ&lt;sub&gt;4&lt;/sub&gt;) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.