As yet various methods have been used for determining the salinity rate of seas and oceans water. The current method of determining salinity rate of seas water has been field examination of various points of sea and determining its salinity rate. In the last decade, remote sensing satellite images have had high capability in determining sea waters salinity rate. Regarding that the present methods in remote sensing depend on the studied regions, therefore, the necessity of customization of these methods is felt. Fresh water springs due to impact on water salinity and temperature and also the environment physics and density like sound velocity are very significant and since coasts and islands of Persian Gulf are considered among arid and semi-arid regions and lack drinking water, access to fresh water springs has more significance. After studies performed, preparation of salinity rate observations and catching two series of proper images for felid data for complete coverage of the region, preprocessing and calibration was performed. For this purpose in turning the acquired radiance to reflection, ENVI software was used. The histogram of calibrated shades of gray rates in images was specified, so that reflection of each sample can be extracted from images. In this paper, the rate of least method efficiency in determining salinity rate of Persian Gulf waters was examined and finally identifying fresh water pits using remote sensing technique was done. The obtained results in the least squares methods after combining various bands of image with each other specified that combining 4 bands of 2, 3, 5 and 7 has the least standard deviation rate with training data and test, which is equal to 0.385 and 0.991978.
References
[1]
Urquhart, E.A., Zaitchik, B.F., Hoffman, M.J., Guikema, S.D. and Geiger, E.F. (2012) Remotely Sensed Estimates of Surface Salinity in the Chesapeake Bay: A Statistical Approach. Remote Sensing of Environment, 123, 522-531. https://doi.org/10.1016/j.rse.2012.04.008
[2]
Marghany, M., Hashim, M. and Cracknell, A.P. (2010) Modelling Sea Surface Salinity from MODIS Satellite Data. Computational Science and Its Applications—ICCSA 2010, Fukuoka, Japan, 23-26 March 2010, 545-556. https://doi.org/10.1007/978-3-642-12156-2_41
[3]
Alabbadi, B.M.M. and Chen, L. (2013) Applying Genetic Algorithm Combining Operation Tree (GAOT) for Estimating Salinity of Taiwan Strait Using MODIS/Terra. 2013 Fourth Global Congress on Intelligent Systems (GCIS), Taiwa, 3-4 December 2013, 16-20.
https://doi.org/10.1109/gcis.2013.8
[4]
Ahn, Y.-H., Shanmugam, P., Moon, J.-E. and Ryu, J.-H. (2008) Satellite Remote Sensing of a Low-Salinity Water Plume in the East China Sea. Annales Geophysicae, 26, 2019-2035.
https://doi.org/10.5194/angeo-26-2019-2008
[5]
Palacios, S.L., Peterson, T.D. and Kudela, R.M. (2009) Development of Synthetic Salinity from Remote Sensing for the Columbia River Plume. Journal of Geophysical Research: Oceans (1978-2012), 114, No. C2.
[6]
Zine, S., Boutin, J., Waldteufel, P., Vergely, J.-L., Pellarin, T. and Lazure, P. (2007) Issues about Retrieving Sea Surface Salinity in Coastal Areas from SMOS Data. IEEE Transactions on Geoscience and Remote Sensing, 45, 2061-2072.
https://doi.org/10.1109/TGRS.2007.894934
[7]
Ellison, W., Balana, A., Delbos, G., Lamkaouchi, K., Eymard, L., Guillou, C. and Prigent, C. (1998) New Permittivity Measurements of Seawater. Radio Science, 33, 639-648.
https://doi.org/10.1029/97RS02223
[8]
Khorram, S. (1982) Remote Sensing of Salinity in the San Francisco Bay Delta. Remote Sensing of Environment, 12, 15-22.
