scholarly journals Remotely sensed sea surface salinity in the hyper-saline Arabian Gulf: Application to landsat 8 OLI data

2017 ◽  
Vol 187 ◽  
pp. 168-177 ◽  
Author(s):  
Jun Zhao ◽  
Marouane Temimi ◽  
Hosni Ghedira
Keyword(s):  
Arabian Gulf ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Surface Salinity

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

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

2017 ◽  
Vol 38 (23) ◽  
pp. 7357-7373 ◽  
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

scholarly journals MEASURING SEA SURFACE SALINITY OF THE JAKARTA BAY USING REMOTELY SENSED OF OCEAN COLOR DATA ACQUIRED BY MODIS SENSOR

2015 ◽  
Vol 36 (2) ◽  
pp. 51-70 ◽  
Author(s):  
Sam Wouthuyzen
Keyword(s):  
Chlorophyll A ◽  
Sediment Resuspension ◽  
Ocean Color ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Minor Role ◽  
Water Runoff ◽  
Surface Salinity ◽  
Chl A

Observations on oceanographic parameters using remote sensing techniques intensively have been done for more than 3 decades for estimating and mapping the sea surface temperature (SST) and the abundance of phytoplankton expressed as the concentration of chlorophyll-a and applied them in studying the ocean phenomenon. As a result, the product of these 2 parameters for all over the oceans in the world has been established and available in daily basis. However, on the contrary, there is still limited application for sea surface salinity (SSS) which is also one of the most important oceanographic features. This paper describes a novel method of deriving SSS from remotely sensed ocean color. The method is based on two important observations of optical properties in regions of freshwater influences. The first is the strong effect of Colored Dissolved Organic Matter (CDOM or yellow substance) on ocean color when present in relatively high concentrations. The second is the close relationship between salinity and CDOM originating from fresh water runoff. In this paper, these relationships are demonstrated for the Jakarta Bay, Indonesia. The MODIS sensor in Terra and Aqua satellites imageries and 10 in situ measurements conducted near-simultaneously with the satellites over flight over the bay in 2004 and 2006 were implemented for deriving CDOM and SSS. The empirical relationships demonstrated in this study allow the satisfactory prediction of CDOM and SSS in the Jakarta Bay from remotely sensed ocean color. The root mean square (r.m.s) error difference between the observed and predicted parameters are 0.14 m-1 and 0.93 psu for CDOM g440 g and SSS, respectively, over a range of salinity from 24 to 33 psu. This range is in good agreement with field surveys. Parameters that may influence CDOM, such as Chlorophyll-a (CHL-a) and total suspended material (TSM) concentrations were also analyzed. Results showed that there were no relationship at all between CDOM and CHL-a, and between CDOM and TSM. These indicate that phytoplankton plays a minor role in regulating CDOM abundance, and also suggest that CDOM contribution from sediment and/or from sediment resuspension is negligible. Thus, CDOM sources in the Jakarta Bay are mainly from riverine inputs. SSS maps created from the satellite-retrieved ocean color identify features in the surface salinity distribution such as salinity front of > 32 psu that migrated in and out of the bay according to seasons. Therefore, the ability to obtain synoptic views of SSS such as presented in this paper provides great potential in furthering the understanding of coastal environments.

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

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.

scholarly journals Sea Surface Salinity Response to Tropical Cyclones Based on Satellite Observations

2021 ◽  
Vol 13 (3) ◽  
pp. 420
Author(s):  
Jingru Sun ◽  
Gabriel Vecchi ◽  
Brian Soden
Keyword(s):  
Tropical Cyclones ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Translation Speed ◽  
Salinity Stratification ◽  
Left Hand ◽  
In Situ Data ◽  
Salinity Response ◽  
The Right

Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution is examined with multiple satellite-based and in-situ data. Global storm-centered composites indicate that TCs act to initially freshen the ocean surface (due to precipitation), and subsequently salinify the surface, largely through vertical ocean processes (mixing and upwelling), although regional hydrography can lead to local departure from this behavior. On average, on the day a TC passes, a strong SSS decrease is observed. The fresh anomaly is subsequently replaced by a net surface salinification, which persists for weeks. This salinification is larger on the right (left)-hand side of the storm motion in the Northern (Southern) Hemisphere, consistent with the location of stronger turbulent mixing. The influence of TC intensity and translation speed on the ocean response is also examined. Despite having greater precipitation, stronger TCs tend to produce longer-lasting, stronger and deeper salinification especially on the right-hand side of the storm motion. Faster moving TCs are found to have slightly weaker freshening with larger area coverage during the passage, but comparable salinification after the passage. The ocean haline response in four basins with different climatological salinity stratification reveals a significant impact of vertical stratification on the salinity response during and after the passage of TCs.

scholarly journals Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

2021 ◽  
Vol 13 (5) ◽  
pp. 831
Author(s):  
Jorge Vazquez-Cuervo ◽  
Chelle Gentemann ◽  
Wenqing Tang ◽  
Dustin Carroll ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Future Research ◽  
Surface Salinity ◽  
Cold Temperatures ◽  
Yukon River ◽  
Arctic Sea ◽  
The Arctic Ocean

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.

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