Satellite-derived Sea Surface Salinity Research Articles

Inter-annual and seasonal cycle of satellite derived sea surface salinity in the western Bay of Bengal

Inter-annual and seasonal cycle of satellite derived sea surface salinity in the western Bay of Bengal

Increasing stratification as observed by satellite sea surface salinity measurements

Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable reflects the balance between precipitation and evaporation over the ocean, since the upper layers of the ocean are the most sensitive to atmosphere–ocean interactions. In situ measurements lack spatial and temporal synopticity and are typically acquired at few meters below the surface. Satellite measurements, on the contrary, are synoptic, repetitive and acquired at the surface. Here we show that the satellite-derived sea surface salinity measurements evidence an intensification of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ near-surface salinity measurements. The largest positive differences between surface and near-surface salinity trends are located over regions characterized by a decrease in the mixed layer depth and the sea surface wind speed, and an increase in sea surface temperature, which is consistent with an increased stratification of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fluxes.

Seasonal Variability of Sea Surface Salinity in the NW Gulf of Guinea from SMAP Satellite

The advent of satellite-derived sea surface salinity (SSS) measurements has boosted scientific study in less-sampled ocean regions such as the northwestern Gulf of Guinea (NWGoG). In this study, we examine the seasonal variability of SSS in the NWGoG from the Soil Moisture Active Passive (SMAP) satellite and show that it is well-suited for such regional studies as it is able to reproduce the observed SSS features in the study region. SMAP SSS bias, relative to in-situ data comparisons, reflects the differences between skin layer measurements and bulk surface measurements that have been reported by previous studies. The study results reveal three broad anomalous SSS features: a basin-wide salinification during boreal summer, a basin-wide freshening during winter, and a meridionally oriented frontal system during other seasons. A salt budget estimation suggests that the seasonal SSS variability is dominated by changes in freshwater flux, zonal circulation, and upwelling. Freshwater flux, primarily driven by the seasonally varying Intertropical Convergence Zone, is a dominant contributor to salt budget in all seasons except during fall. Regionally, SSS is most variable off southwestern Nigeria and controlled primarily by westward extensions of the Niger River. Anomalous salty SSS off the coasts of Cote d’Ivoire and Ghana especially during summer are driven mainly by coastal upwelling and horizontal advection.

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

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.

Ocean color as a proxy to predict sea surface salinity in the Banda Sea

Salinity is an important ocean parameter that greatly influences physical, chemical, and biological ocean properties and processes. Salinity combines with sea temperature and chlorophyll-a (Chl-a) that mostly sourced from remote sensing-based measurements can reveal ocean quality and supports fisheries. However, the satellite-derived Sea Surface Salinity (SSS) dataset (∼ 9 years) is not as temporally adequate as SST and Chl-a datasets (∼3 decades) and thus, preventing a comprehensively spatio-temporal analysis of this water quality aspect. Since (SSS) can be approximated using satellite-derived ocean color products having the similar temporal length of datasets to the available SST and Chl-a datasets, predicted SSS can be produced from these ocean color products to fill the gap of the existing SSS dataset. This study aims to estimate the SSS from ocean color products of Aqua-MODIS satellite with a spatial and temporal resolution of 4 km and 8-daily by developing an empirical model. The ocean color data used were remote sensing reflectance (Rrs) of blue, green and red wavelengths (412, 433, 469, 488, 531, 547, 555, 645, 667 and 678 nm). The absorption coefficients due to detritus material non-algae, Gelbstof and CDOM (ADG) at 443 nm and the absorption coefficient due to phytoplankton (APH) at 443 nm data were also used. The Banda Sea was chosen due to its large-scale upwelling system (∼300 km × 300 km) that providing an important ocean process related to fishery and the availability of in-situ salinity measurements (i.e. CTD casts from series of Research Vessel (R/V) Baruna Jaya III, VII and VIII cruises and Argo floats), which a part of these datasets will be used to validate predicted SSS. Results showed that of all ocean color parameters tested, ADG at 443 nm was strongly correlated with in-situ SSS through the polynomial order 5 regression equation with a high R2 of 0.94 and a low RMES value of 0.101 PSU. Although this empirical model has high accuracy, but based on RMSE analysis results from various locations within and outside the Banda Sea that influenced by the Pacific and the Indian ocean water masses indicates that this model actually good to predict in-situ SSS only for a narrow range SSS of 33.4-34.5 PSU. Nevertheless, this model has a limitation, it is still can be used for predicting and mapping the SSS for Banda Sea as well as for most of the Indonesian waters. The long-term meteorological SSS map (2003-2017) derived by this model together with the SST and Chl-a maps can show clearly the upwelling phenomena of the Banda Sea, which occurred during the southeast monsoon (June-July-August, JJA). This study proves that ocean color data from Aqua-MODIS satellite can be applied to estimate and to map the SSS for most of the Indonesian waters, but validations for this model is still needed

Estimation of Surface Freshwater Fluxes in the Arctic Ocean Using Satellite-Derived Salinity

The temporal and spatial distribution of sea surface salinity (SSS) is not well known in the Arctic region, and only recent satellite-derived measurements and models have allowed for potential enhancement, though their accuracy remains uncertain. We use NASA’s Soil Moisture Active Passive (SMAP) and the European Space Agency’s Soil Moisture Ocean Salinity (SMOS) to investigate the variability of SSS in the Arctic from 2015 to 2017, as well as to calculate surface advective freshwater fluxes. These data sets are compared with Argo and European Center for Medium-Range Weather Forecasts Ocean Reanalysis version 4 (ORAS) to assess the ability of satellites in detecting freshwater fluxes. Salinity and surface freshwater fluxes are estimated for the Bering Strait and Barents Sea Opening (BSO). In this study, we have compared the Jet Propulsion Laboratory’s (JPL) SMAP salinity to the remote sensing system’s (RSS) SMAP salinity product, as well as the The Centre Aval de Traitement des donneees SMOS salinity product. There is disagreement between the reanalysis product and satellites on the mean and variability of surface freshwater fluxes in the BSO; however, the meridional fluxes of the satellites and reanalysis product were significantly correlated within the Bering Strait. This shows the capability of using satellites to measure surface freshwater fluxes in this pathway. However, the discrepancies between satellite-derived SSS and fluxes in other regions of the Arctic Ocean emphasizes the need to increase in situ monitoring to help validate satellites in the higher latitudes.

Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.

Remote Sensing Estimation of Sea Surface Salinity from GOCI Measurements in the Southern Yellow Sea

Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak.

Tracking sea surface salinity and dissolved oxygen on a river-influenced, seasonally stratified shelf, Mississippi Bight, northern Gulf of Mexico

River discharge, and its resulting region of freshwater influence (ROFI) in the coastal ocean, has a critical influence on physical and biogeochemical processes in seasonally stratified shelf ecosystems. Multi-year (2010–2016) observations of satellite-derived sea surface salinity (SSS) and in situ water column hydrographic data during summer 2016 were used to investigate physical aspects of the ROFI east of the Mississippi River Delta to better assess regional susceptibility to hypoxia in the summer months. Time series of SSS data indicate that the shelf region impacted by the seasonal expansion of freshwater can be as extensive as the well-known “dead zone” region west of the Delta, and hydrographic observations from a shelf-wide survey indicate strong stratification associated with the ROFI. Peak buoyancy frequencies typically ranged between 0.15 and 0.25 s−1 and were concentrated in a 2–3 m layer around 4–10 m deep across much of the shelf. This ROFI is expected to be influenced by local freshwater sources which, while individually small, make a notable contribution in aggregate to the region (annually averaged daily discharge of approximately 2880 m3 s−1). The dissolved oxygen (DO) conditions under this freshwater cap were spatially and temporally variable, with areas of hypoxia and near-hypoxic conditions over portions of the shelf, demonstrating the utility of satellite-derived SSS in identifying coastal areas potential vulnerability to hypoxia. These regions of low bottom dissolved oxygen persisted throughout the peak summer season at several sites on the shelf, with the northeastern corner of Mississippi Bight having the most intense and persistent hypoxia.

A Comparison of Satellite-Derived Sea Surface Salinity and Salt Fluxes in the Southern Ocean

Sea surface salinity (SSS) derived from the multi-satellite missions, NASA’s Aquarius/SAC–D and Soil Moisture Active and Passive (SMAP), and ESA’s Soil Moisture and Ocean Salinity (SMOS) are compared and used to estimate horizontal advective salt fluxes in the Southern Ocean (SO). In comparison with an Argo product, all three satellites estimate similar SSS in the Southern Hemisphere mid-latitudes (30° S–45° S) with low variability among the products. At high latitudes, there are temporal patterns of bias (relative to Argo) in Aquarius during Austral summer and in SMOS during Austral winter. Differences in the satellite products and Argo exist along coastal boundaries, low temperatures, and strong currents. Satellite-derived salinity indicates low temporal–mean standard deviations with Aquarius (0.215) and moderate standard deviations with SMOS (0.294) and SMAP (0.325) against Argo in the SO. Differences in satellite-derived zonal and meridional SSS gradients are large; standard deviation values are 2.52 and 1.49 × 10−6 psu m−1, respectively, and similarly located within the sub-tropical salinity maxima, Antarctic Circumpolar Current, and coastal zones. Differences in the horizontal advective fluxes are on average small, but large variability greater than 275 mm month−1 indicates errors of similar magnitude to the estimated Argo flux. Based on these results, the use of satellite-derived salinity may prove to be a useful resource for observing salinity and horizontal salt fluxes, outside the inaccuracies associated with the high latitudes and coastal currents between the various remotely sensed products, and could significantly influence the results depending on the product.

Assessment of seasonal and year-to-year surface salinity signals retrieved from SMOS and Aquarius missions in the Bay of Bengal

ABSTRACTThe Bay of Bengal (BoB) exhibits a wide range of sea surface salinity (SSS), with very fresh water induced by heavy monsoonal precipitation and river run-offs to the north, and saltier water to the south. This is a particularly challenging region for the application of satellite-derived SSS measurements because of the potential pollution of the SSS signal by radio frequency interference (RFI) and land-induced contamination in this semi-enclosed basin. The present study validates recent level-3 monthly gridded (1° × 1°) SSS products from Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions to an exhaustive in situ SSS product for the BoB. Current SMOS SSS retrievals do not perform better than existing climatologies. This is in stark contrast to Aquarius, which outperforms SMOS and available SSS climatologies everywhere in the BoB. While SMOS only captures the SSS seasonal evolution in the northern part of the Bay, Aquarius accurately captures the seasonal signal in the entire basin. The Aquarius product is also able to capture SSS non-seasonal anomalies, with an approximate correlation (r) of 0.75 with box-averaged in situ data in the northern, central, and western parts of the Bay. Aquarius can, thus, be confidently used to monitor large-scale year-to-year SSS variations in the BoB.

The salinity signature of the cross-shelf exchanges in the Southwestern Atlantic Ocean: Satellite observations.

Satellite-derived sea surface salinity (SSS) data from Aquarius and SMOS are used to study the shelf-open ocean exchanges in the western South Atlantic near 35°S. Away from the tropics, these exchanges cause the largest SSS variability throughout the South Atlantic. The data reveal a well-defined seasonal pattern of SSS during the analyzed period and of the location of the export of low-salinity shelf waters. In spring and summer, low-salinity waters over the shelf expand offshore and are transferred to the open ocean primarily southeast of the river mouth (from 36°S to 37°30′S). In contrast, in fall and winter, low-salinity waters extend along a coastal plume and the export path to the open ocean distributes along the offshore edge of the plume. The strong seasonal SSS pattern is modulated by the seasonality of the along-shelf component of the wind stress over the shelf. However, the combined analysis of SSS, satellite-derived sea surface elevation and surface velocity data suggest that the precise location of the export of shelf waters depends on offshore circulation patterns, such as the location of the Brazil Malvinas Confluence and mesoscale eddies and meanders of the Brazil Current. The satellite data indicate that in summer, mixtures of low-salinity shelf waters are swiftly driven toward the ocean interior along the axis of the Brazil/Malvinas Confluence. In winter, episodic wind reversals force the low-salinity coastal plume offshore where they mix with tropical waters within the Brazil Current and create a warmer variety of low-salinity waters in the open ocean.Key PointsSatellite salinity sensors capture low-salinity detrainment events from shelves SW Atlantic low-salinity detrainments cause highest basin-scale variability In summer low-salinity detrainments cause extended low-salinity anomalies

The North Atlantic subtropical surface salinity maximum as observed by Aquarius

Aquarius satellite-derived sea surface salinity (SSS) data from August 2011 through September 2013 reveals significant seasonal migration and freshening of the subtropical surface salinity maximum (SSS-max) area in the North Atlantic, in good agreement with in situ observations, including those obtained as part of the SPURS (Salinity Processes in the Upper Ocean Regional Study) field experiment in 2012–2013. The SSS-max fluctuated in surface area—as defined by the 37.4 surface isohaline—during the course of the Aquarius time series by about 67%. The SSS-max has a surprisingly large amount of non-seasonal variability, including a general decrease in salinity throughout the eastern subtropical North Atlantic between 2011–2012 and 2012–2013 of about 0.1–0.2. The documented seasonal variability is weakest in the maximum salinity area and increases toward the north and south, respectively. This is consistent with the important role played by Ekman transport and regional excess of evaporation over precipitation in the formation of the SSS-max.

Assessment of Satellite-Derived Sea Surface Salinity in the Indian Ocean

The study has been motivated by the desire to assess the performance of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity (SMOS) satellite launched by the European Space Agency. Daily Level 3 product on a 0.25° × 0.25° grid for the year 2010 has been used for this assessment in the Indian Ocean. Various data sets, like the in situ data sets available from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Argo floats and also the data sets from modular ocean model version 3 simulations, have been utilized for this purpose. Comparison made at two buoy locations suggests good quality of SMOS SSS with root-mean-square error of the order of 0.36 and 0.34 psu. The triple collocation method, which explicitly takes into account the error characteristics of the SMOS, Argo, and model data sets, has been used for further validation of the SMOS data. Since the Indian Ocean exhibits characteristically different patterns of SSS in its different subregions, the study area has been divided into different such subregions. The SMOS-derived SSS appears to be of very good quality in the equatorial Indian Ocean and southern Indian Ocean, while the data are of poorer quality in the Arabian Sea and the Bay of Bengal possibly because of the errors in SSS retrieval due to the land contamination and strong winds.