MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

[1]  Mik Wisniewski,et al.  Applied Regression Analysis: A Research Tool , 1990 .

[2]  E. Padan,et al.  Primary production in a desert-enclosed sea— the Gulf of Elat (Aqaba), Red Sea , 1979 .

[3]  B. Jena,et al.  Validation of integrated potential fishing zone (IPFZ) forecast using satellite based chlorophyll and sea surface temperature along the east coast of India , 2007 .

[4]  I. Hoteit,et al.  Impacts of Climate Modes on Air–Sea Heat Exchange in the Red Sea , 2015 .

[5]  A. Negm,et al.  Analysis of Egyptian Red Sea Fishing Ports , 2017 .

[6]  C. D. Young.  Review of the state of world marine capture fisheries management , 2006 .

[7]  G. Chavula,et al.  Mapping Potential Fishing Grounds in Lake Malawi Using AVHRR and MODIS Satellite Imagery , 2012 .

[8]  Virginie Lafon,et al.  Sea surface temperature spatio-temporal variability in the Azores using a new technique to remove invalid pixels , 2004, SPIE Remote Sensing.

[9]  C. Walton,et al.  Nonlinear Multichannel Algorithms for Estimating Sea Surface Temperature with AVHRR Satellite Data , 1988 .

[10]  Raphael M. Kudela,et al.  Development of synthetic salinity from remote sensing for the Columbia River plume , 2009 .

[11]  A. Behairy,et al.  Mineralogical variations in the unconsolidated sediments of El Qasr reef, north of Jeddah, west coast of Saudi Arabia , 1984 .

[12]  V. Klemas.  Remote sensing of environmental indicators of potential fish aggregation: An overview , 2012 .

[13]  Maged Marghany,et al.  Linear algorithm for salinity distribution modelling from MODIS data , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Young-Joon Kim,et al.  Modeling of Suspended Solids and Sea Surface Salinity in Hong Kong using Aqua/MODIS Satellite Images , 2007 .

[15]  B. Cui,et al.  Retrieval of remotely sensed sea surface salinity using MODIS data in the Chinese Bohai Sea , 2017 .

[16]  Cédric Jamet,et al.  Retrieval of the spectral diffuse attenuation coefficient Kd(λ) in open and coastal ocean waters using a neural network inversion , 2012 .

[17]  Zijun Gan,et al.  Predicted positions of tidal fronts in continental shelf of South China Sea , 2010 .

[18]  Yuhai Bao,et al.  Retrieval of sea surface salinity with MERIS and MODIS data in the Bohai Sea , 2013 .

[19]  Wenzhao Li,et al.  An Assessment of Atmospheric and Meteorological Factors Regulating Red Sea Phytoplankton Growth , 2018, Remote. Sens..

[20]  H. U. Solanki,et al.  Evaluation of remote-sensing-based potential fishing zones (PFZs) forecast methodology , 2005 .

[21]  A. Eladawy,et al.  Characterization of the northern Red Sea's oceanic features with remote sensing data and outputs from a global circulation model , 2017 .

[22]  Liangyun Liu,et al.  Mapping C3 and C4 plant functional types using separated solar-induced chlorophyll fluorescence from hyperspectral data , 2011 .

[23]  A. Omstedt.  Guide to Process Based Modeling of Lakes and Coastal Seas , 2015 .

[24]  S. Nayak,et al.  Locating oceanic Tuna resources in the Eastern Arabian sea using remote sensing , 2005 .

[25]  T. Lihan,et al.  Determination of potential fishing grounds of Rastrelliger kanagurta using satellite remote sensing and GIS technique , 2015 .

[26]  Peter J. Minnett,et al.  A pathway to generating Climate Data Records of sea-surface temperature from satellite measurements , 2012 .

[27]  Ricardo M Letelier,et al.  An analysis of chlorophyll fluorescence algorithms for the moderate resolution imaging spectrometer (MODIS) , 1996 .

[28]  Jong-Kuk Choi,et al.  GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity , 2012 .

[29]  W. Gregg,et al.  Improving the consistency of ocean color data: A step toward climate data records , 2010 .

[30]  J. Farrar,et al.  Zonal surface wind jets across the Red Sea due to mountain gap forcing along both sides of the Red Sea , 2009 .

[31]  Ronald L. Vogel,et al.  Assessing satellite sea surface salinity from ocean color radiometric measurements for coastal hydrodynamic model data assimilation , 2016 .

[32]  I. Hoteit,et al.  Atmospheric forcing of the winter air–sea heat fluxes over the Northern Red Sea , 2013 .

[33]  I. Ioannou,et al.  Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS. , 2011, Applied optics.

[34]  Kendall L. Carder,et al.  Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a , 2004 .

[35]  N. Stambler.  Bio-optical properties of the northern Red Sea and the Gulf of Eilat (Aqaba) during winter 1999 , 2005 .

[36]  Tong Zhu,et al.  Remotely-sensed chlorophyll a observations of the northern Red Sea indicate seasonal variability and influence of coastal reefs , 2008 .

[37]  V. Vantrepotte,et al.  Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach. , 2010, Optics express.

[38]  Roland Romeiser,et al.  Wind Retrieval From Shipborne Nautical X-Band Radar Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Brent N. Holben,et al.  An analysis of potential cloud artifacts in MODIS over ocean aerosol optical thickness products , 2005 .

[40]  G. Lucchini,et al.  The Yku70–Yku80 complex contributes to regulate double‐strand break processing and checkpoint activation during the cell cycle , 2008, EMBO reports.

[41]  William E. Johns,et al.  An Oceanic General Circulation Model (OGCM) investigation of the Red Sea circulation: 2. Three‐dimensional circulation in the Red Sea , 2003 .

[42]  M. Heron,et al.  The use of HF radar surface currents for computing Lagrangian trajectories: Benefits and issues , 2010, OCEANS'10 IEEE SYDNEY.

[43]  E. Boss,et al.  Regional ocean-colour chlorophyll algorithms for the Red Sea , 2015 .

[44]  W. Hovis,et al.  Remote Sensing of Ocean Color , 1977 .

[45]  D. Lampkin,et al.  Empirical Retrieval of Surface Melt Magnitude from Coupled MODIS Optical and Thermal Measurements over the Greenland Ice Sheet during the 2001 Ablation Season , 2008, Sensors.

[46]  H. U. Solanki,et al.  Satellite observations of main oceanographic processes to identify ecological associations in the Northern Arabian Sea for fishery resources exploration , 2008, Hydrobiologia.

[47]  Matthew D. Grossi,et al.  Satellite-derived coastal ocean and estuarine salinity in the Mid-Atlantic , 2013 .

[48]  C. Béné,et al.  Valuing Africa's inland fisheries: overview of current methodologies with an emphasis on livelihood analysis , 2003 .

[49]  A. R. Mahmud,et al.  Potential fish habitat mapping using MODIS-derived sea surface salinity, temperature and chlorophyll-a data: South China Sea Coastal areas, Malaysia , 2013 .