The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 219–223, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-219-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 219–223, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-219-2017

  27 Sep 2017

27 Sep 2017

SPATIAL AND TEMPORAL ANALYSIS OF SEA SURFACE SALINITY USING SATELLITE IMAGERY IN GULF OF MEXICO

S. Rajabi, M. Hasanlou, and A. R. Safari S. Rajabi et al.
  • School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: Sea surface salinity, Aquarius, Sea surface temperature, Chlorophyll-a concentration, Gulf of Mexico, Monitoring

Abstract. The recent development of satellite sea surface salinity (SSS) observations has enabled us to analyse SSS variations with high spatiotemporal resolution. In this regards, The Level3-version4 data observed by Aquarius are used to examine the variability of SSS in Gulf of Mexico for the 2012-2014 time periods. The highest SSS value occurred in April 2013 with the value of 36.72 psu while the lowest value (35.91 psu) was observed in July 2014. Based on the monthly distribution maps which will be demonstrated in the literature, it was observed that east part of the region has lower salinity values than the west part for all months mainly because of the currents which originate from low saline waters of the Caribbean Sea and furthermore the eastward currents like loop current. Also the minimum amounts of salinity occur in coastal waters where the river runoffs make fresh the high saline waters. Our next goal here is to study the patterns of sea surface temperature (SST), chlorophyll-a (CHLa) and fresh water flux (FWF) and examine the contributions of them to SSS variations. So by computing correlation coefficients, the values obtained for SST, FWF and CHLa are 0.7, 0.22 and 0.01 respectively which indicated high correlation of SST on SSS variations. Also by considering the spatial distribution based on the annual means, it found that there is a relationship between the SSS, SST, CHLa and the latitude in the study region which can be interpreted by developing a mathematical model.