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

Song, Qian; Zhang, Jie; Cui, Tingwei; Bao, Yuhai

doi:10.1016/j.rse.2013.04.016

Cited by 70 publications

(41 citation statements)

References 14 publications

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“…These data are acquired on 23 rd ,26 th and 27 th 2004 which represent pro, and post tsunami event, respectively (Figure 12). According to Marghany et al, [18], Moderate Resolution Imaging Spectroradiometer (MODIS) has 1 km spatial resolution and having 36 bands which are ranged from 0.405 to 14.285µm [24]. The MODIS satellite takes 1 to 2 days to capture all the scenes in the entire world, acquiring data in 36 spectral bands over a 2330 km swath.…”

Section: Resultsmentioning

confidence: 99%

Simulation of Tsunami Impact on Sea Surface Salinity along Banda Aceh Coastal Waters, Indonesia

Marghany¹

2014

Advanced Geoscience Remote Sensing

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Section: Resultsmentioning

confidence: 99%

Simulation of Tsunami Impact on Sea Surface Salinity along Banda Aceh Coastal Waters, Indonesia

Marghany¹

2014

Advanced Geoscience Remote Sensing

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“…The spatial resolution of data from the ESA's Soil Moisture and Ocean Salinity as well as NASA's Aquarius missions are too coarse for coastal and estuarine environments ( Figure 2D; Urquhart et al, 2012). In time, finer resolutions, such as that offered by NASA's Soil Moisture Active Passive mission, or novel methods based on selected wavelengths of RS reflectance may provide improved estimates of SSS (Urquhart et al, 2012;Qing et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

et al. 2018

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Sea surface temperature (SST) and salinity (SSS) are essential variables at the ocean and atmosphere interface when considering risk factors for disease in farmed and wild fish stocks. Ecological research has witnessed a recent trend in use of digital and satellite technologies, including remote-sensing tools. We explored spatial coverage of remotely-sensed SST and SSS data and compared them with in situ measurements of water temperatures and salinity, which led to suggested adjustments to the remotely-sensed data for its use in aquaculture research. The in situ data were from farms and wild surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. Concurrent SST and SSS values were extracted from remotely-sensed products and compared with 20,513 and 20,038 in situ records for water temperature and salinity, respectively, from 232 different sites. Among nine SST products evaluated, the UKMO OSTIA SST (UK Meteorological Office) had the highest retrieval, and highest concordance correlation coefficient (0.86), highest index of agreement (0.93), fewest missing values, and smallest mean and SD values for bias, when compared to in situ measurements. A mixed linear regression model with UKMO OSTIA SST as the predictor for in situ measurements estimated an adjustment coefficient of 0.89 • C for UKMO OSTIA SST. None of the three SSS products evaluated provided appropriate corresponding values for in situ sites, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO OSTIA SST is currently best suited for aquaculture studies in coastal BC. The near real-time availability of these data with the estimated adjustment would allow their use in forecast models, surveillance of pathogens, and the creation of risk maps.

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“…By 2001, more than 3147 reservoirs had been built in the Yellow River basin, with a total storage capacity of 57.4 Â 10 9 m 3 (Zhang et al, 2001); and since 2002, the Xiaolangdi reservoir was built as a major regulator for water storage, with a total capacity of 12.7 Â 10 9 m 3 . During 2004During -2009, sea surface salinity in most parts of the Bohai Sea showed increasing trend and the Yellow River discharge was found to have the greatest influence on sea surface salinity near the river estuary (Qing et al, 2013). These significant environmental changes indicated the complexity of Chl-a variability in coastal waters.…”

Section: The Spatial Difference In Inter-annual Trends Of Chl-a In Thmentioning

confidence: 98%

Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns

Chen

Wang

2013

Progress in Oceanography

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a b s t r a c tThe spatial and temporal variability of sea surface chlorophyll-a (Chl-a) concentrations in the Bohai and Yellow Seas were analyzed, using satellite-derived Chl-a products from SeaWiFS and MODIS sensors over the period of September 1997-September 2011. A set of monthly and cloud-free Chl-a data was produced by the Data Interpolating Empirical Orthogonal Function (DINEOF) method. The results indicate that there are different Chl-a seasonal patterns existing in the Yangtze River mouth, coastal and offshore waters, respectively. In the Yangtze River mouth, a long-lasting Chl-a peak (May-September) is seen in summer. In coastal waters, two significant Chl-a maxima occur in winter-spring and late summer, respectively. In offshore waters, only one significant spring (March-April) Chl-a maximum is evident with a time lag of 1-3 months to coastal waters and the signal of autumn maximum is very weak. In coastal waters, wind-tide-thermohaline circulations and East Asia summer rainy monsoon may important physical factors to impact the seasonal pattern of Chl-a, but increased human activity (e.g., eutrophication, dam) could significantly enhance this process. In offshore waters, the impact on the circulation of the YSWC in winter and YSCW in summer in the central Yellow Sea could be important physical factor in explaining the variability of Chl-a in seasonal patterns. The decadal trends of Chl-a and sea surface temperature are decreasing in coastal waters, with a significantly positive correlation. In offshore waters, the decadal trends of Chl-a is increasing but a slight decreasing sea surface temperature trend is seen, and they indicate a negative correlation.

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

Cited by 70 publications

References 14 publications

Simulation of Tsunami Impact on Sea Surface Salinity along Banda Aceh Coastal Waters, Indonesia

Simulation of Tsunami Impact on Sea Surface Salinity along Banda Aceh Coastal Waters, Indonesia

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns

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