Development of a MODIS data based algorithm for retrieving nearshore sea surface salinity along the northern Gulf of Mexico coast

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.

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Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico

Remote Sensing ◽

10.3390/rs13050881 ◽

2021 ◽

Vol 13 (5) ◽

pp. 881

Author(s):

Zhiyi Fu ◽

Fangfang Wu ◽

Zhengliang Zhang ◽

Linshu Hu ◽

Feng Zhang ◽

...

Keyword(s):

Gulf Of Mexico ◽

River Discharge ◽

River Flow ◽

High Accuracy ◽

Sea Surface ◽

Sea Surface Salinity ◽

Temporal Heterogeneity ◽

Surface Salinity ◽

Circular Distribution ◽

Heterogeneity Analysis

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

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Retrieval of remotely sensed sea surface salinity using MODIS data in the Chinese Bohai Sea

International Journal of Remote Sensing ◽

10.1080/01431161.2017.1375570 ◽

2017 ◽

Vol 38 (23) ◽

pp. 7357-7373 ◽

Cited By ~ 6

Author(s):

Xiang Yu ◽

Bei Xiao ◽

Xiangyang Liu ◽

Yebao Wang ◽

Buli Cui ◽

...

Keyword(s):

Bohai Sea ◽

Remotely Sensed ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Modis Data

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Retrieval of sea surface salinity with MERIS and MODIS data in the Bohai Sea

Remote Sensing of Environment ◽

10.1016/j.rse.2013.04.016 ◽

2013 ◽

Vol 136 ◽

pp. 117-125 ◽

Cited By ~ 34

Author(s):

Song Qing ◽

Jie Zhang ◽

Tingwei Cui ◽

Yuhai Bao

Keyword(s):

Bohai Sea ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

The Bohai Sea ◽

Modis Data

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w4-219-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 219-223 ◽

Cited By ~ 3

Author(s):

S. Rajabi ◽

M. Hasanlou ◽

A. R. Safari

Keyword(s):

Gulf Of Mexico ◽

Temporal Analysis ◽

Sea Surface ◽

Sea Surface Salinity ◽

Loop Current ◽

Surface Salinity ◽

Spatiotemporal Resolution ◽

Study Region ◽

Distribution Maps ◽

Saline Waters

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.

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The Downscaling of the SMOS Global Sea Surface Salinity Product Based on MODIS Data Using a Deep Convolution Network Approach

Proceedings of the 2019 3rd International Conference on Advances in Image Processing ◽

10.1145/3373419.3373462 ◽

2019 ◽

Author(s):

Qixin Liu ◽

Linlin Xu ◽

Zhiwen Zhang

Keyword(s):

Sea Surface ◽

Sea Surface Salinity ◽

Network Approach ◽

Surface Salinity ◽

Modis Data ◽

Deep Convolution Network

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Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

Remote Sensing ◽

10.3390/rs13152995 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2995

Author(s):

Frederick M. Bingham ◽

Severine Fournier ◽

Susannah Brodnitz ◽

Karly Ulfsax ◽

Hong Zhang

Keyword(s):

High Resolution ◽

Time Window ◽

Single Point ◽

Ocean Model ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Argo Floats ◽

Statistical Measures ◽

Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

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