Sea Surface Salinity Estimates Research Articles

Estimation of sea surface salinity from MODIS-Aqua data in the northern South China Sea

Sea Surface Salinity (SSS) is a key variable in physical oceanography and biogeochemical research. Microwave remote sensing technology has been used to monitor SSS in open ocean areas. However, the limited spatial resolution and suboptimal precision exhibited by microwave remote sensing derived SSS data have led to the omission of crucial oceanic information, consequently constraining its utilization. In this study, an artificial neural network (ANN) model for the regional SSS inversion was constructed utilizing in situ SSS measurements in the northern South China Sea and remote sensing sea surface temperature and reflectance at wavelengths of 412, 443, 488, 555 and 667 nm derived from MODIS-Aqua satellite Level 2 data. The model showed a good performance of root mean square error (RMSE) of 0.32 and 0.34 for the model training and testing datasets, with mean bias (MB) of 0 and 0.01, and correlation coefficients (R2) reaching 0.93. The model validation indicated that the ANN model reproduced the spatial distribution of SSS highly consistent with the measured SSS (RMSE = 2.75, MB = −1.67, R2 = 0.95), demonstrating its strong applicability in the NSCS. In contrast to other local SSS inversion models, our ANN model clearly showed the fine-scale SSS feature under the intrusion of Pearl River plume near to Dongsha Island with the help of anticyclonic-cyclonic eddies. This prediction closely aligned with the observations from the Soil Moisture Active Passive SSS dataset. The ANN model was applied to MODIS-Aqua data to generate the monthly SSS from 2003 to 2022. A decreasing SSS trend in SSS of 0.02 psu y−1 during 2005–2013 and 2017–2020 and a slightly increasing SSS trend of 0.01 psu y−1 from 2013 to 2017 were observed. Further analysis shows the low-frequency variability of SSS is significantly correlated with the Pacific Decadal Oscillation.

Continuity and Enhancements in Sea Surface Salinity Estimation in the East China Sea Using GOCI and GOCI-II: Challenges and Further Developments

During the summer, substantial freshwater discharge from the Changjiang River into the East China Sea (ECS) results in extensive low-salinity water (LSW) plumes that significantly affect regions along the southern Korean Peninsula and near Jeju Island. Previous research developed an empirical equation to estimate sea surface salinity (SSS) in the ECS during the summer season using remote-sensing reflectance (Rrs) data from bands 3–6 (490, 555, 660, and 680 nm) of the Geostationary Ocean Color Imager (GOCI). With the conclusion of the GOCI mission in March 2021, this study aims to ensure the continuity of SSS estimation in the ECS by transitioning to its successor, the GOCI-II. This transition was facilitated through two approaches: applying the existing GOCI-based equation and introducing a new machine learning method using a random forest model. Our analysis demonstrated a high correlation between SSS estimates derived from the GOCI and GOCI-II when applying the equation developed for the GOCI to both satellites, as indicated by a robust R2 value of 0.984 and a low RMSD of 0.8465 psu. This study successfully addressed the challenge of maintaining continuous SSS estimation in the ECS post-GOCI mission and evaluated the accuracy and limitations of the GOCI-II-derived SSS, proposing future strategies to enhance its effectiveness.

Prediction of sea-surface salinity using MODIS and Sentinel-3 data

ABSTRACT Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions.

Southward Shift and Intensification of the Intertropical Convergence Zone in the North Pacific Across the Mid‐Pleistocene Transition

AbstractThe Hadley Circulation and associated westerlies strengthened and moved equatorward across the mid‐Pleistocene transition (MPT). However, the evolution of the intertropical convergence zone (ITCZ) is still elusive due to the scarcity of long‐term hydrological records from regions sensitive to the ITCZ change. Here, high‐resolution sea surface salinity estimates derived from surface‐dwelling planktic foraminiferal δ18O and Mg/Ca in Ocean Drilling Program Site 871 reveal a long‐term freshening trend in the central equatorial Pacific across the MPT. We attribute this secular reorganization of the precipitation‐evaporation balance to the gradual southward migration and intensification of ITCZ in the North Pacific. It is inferred that the long‐term evolution of the ITCZ was modulated by the increased meridional sea surface temperature gradients and the enhancements of trade winds across the MPT.

Quantitative Assessment of Sea Surface Salinity Estimates Using a High-Frequency Radar in Ise Bay, Japan

Changes in sea surface salinity (SSS) caused by the discharge of freshwater plumes from rivers affect the marine environment in estuaries; therefore, monitoring SSS is essential for understanding the changes in physical phenomena within coastal ecosystems induced by river plume discharge. Previous studies showed that salinity could be estimated using a very-high-frequency radar; however, this method was only validated over a short period and few qualitative evaluations were performed. Therefore, to verify quantitative assessments of SSS estimates for practical use, we estimated SSS using the Doppler spectrum of a 24.5-MHz phased-array high-frequency (HF) radar installed in Ise Bay, Japan, and data of approximately 1 year were used for verification. The radar-estimated SSS map was consistent with the velocity field and salinity distribution reported in previous studies. The root mean square error (RMSE) of the SSS estimate for 1-h radar data compared with in situ observations was 4.42 psu when the effect of wind on the received power was removed and 5.04 psu when it was not. For the daily (25-h) average, the RMSE when the effect of wind was considered was 3.32 psu. These results were considered sufficiently applicable in closed coastal areas such as Ise Bay, where the SSS decreases rapidly by 10 psu or more due to river flooding. The results revealed that the HF radar, which can continuously measure sea surface velocity and SSS with a high spatiotemporal resolution, can be a useful tool for providing a deeper understanding of the physical and environmental phenomena that are greatly affected by river water discharge.

GOCI-II based sea surface salinity estimation using machine learning for the first-year summer

ABSTRACT Estimation of sea surface salinity (SSS) using the Geostationary Ocean Color Imager-II (GOCI-II) measurements in the East China Sea (ECS) was conducted from July to September of 2021, when the first-year observations are available after GOCI-II launch in 2020. The SSS in the ECS is mainly affected by the Changjiang River plume, which varies from under 20–35 psu, and the discharged freshwater disperses from the river mouth towards Jeju Island, Korea. For the SSS estimation, a multi-layer perceptron neural network (MPNN) was employed to train the nonlinear processes of GOCI-II spectral measurements as inputs and the SSS of Soil Moisture Active Passive (SMAP) as the target. Because GOCI-II has four additional spectral bands (380, 510, 620 and 709 nm) compared to the bands in the first generation of GOCI (413, 443, 490, 555, 660 and 680 nm), we developed a new MPNN algorithm and analysed (1) how much these new spectral measurements increase SSS accuracy, and (2) how the enhanced spatial and temporal resolution of GOCI-II make SSS features different from GOCI. The first results showed that the root mean square error (RMSE) and coefficient of determination (R 2) were 0.68 psu and 0.92, respectively. Furthermore, R 2, when compared with in-situ measurements at the Ieodo Ocean Research Station (I-ORS), increased as much as 0.23 (0.20–0.43) for the 10-band model, which performed much better than previous 6-band model. The second result suggests how the improved spatial features at more frequent SSS measurements can be utilized to avoid low salinity intrusion into the aquafarming sites off the coast of Korea. More importantly, we elucidated why the MPNN algorithm performs better than conventional SSS estimation methods by comparing various optical properties with SSS variation; thus, the newly developed model can provide SSS not only in ECS but also low salinity water near southwest Korean coasts at hourly spatial resolution of 250 m.

Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data

Sea surface salinity (SSS) is one of the Essential Climate Variables (ECVs), defined by the Global Climate Observing System (GCOS). Salinity is modified by river discharge, land run-off, precipitation, and evaporation, and it is advected by oceanic currents. In turn, ocean circulation, the water cycle, and biogeochemistry are deeply impacted by salinity variations. The Mediterranean Sea represents a hot spot for the variability of salinity. Despite the ever-increasing number of moorings and floating buoys, in situ SSS estimates have low coverage, hindering the monitoring of SSS patterns. Conversely, satellite sensors provide SSS surface data at high spatial and temporal resolution, complementing the sparseness of in situ datasets. Here, we describe a multidimensional optimal interpolation algorithm, specifically configured to provide a new daily SSS dataset at 1/16° grid resolution, covering the entire Mediterranean Sea (Med L4 SSS). The main improvements in this regional algorithm are: the ingestion of satellite SSS estimates from multiple satellite missions (NASA’s Soil Moisture Active Passive (SMAP), ESA’s Soil Moisture and Ocean Salinity (SMOS) satellites), and a new background (first guess), specifically built to improve coastal reconstructions. The multi-sensor Med L4 SSS fields have been validated against independent in situ SSS samples, collected between 2010–2020. They have also been compared with global weekly Copernicus Marine Environment Monitoring Service (CMEMS) and Barcelona Expert Centre (BEC) regional products, showing an improved performance. Power spectral density analyses demonstrated that the Med L4 SSS field achieves the highest effective spatial resolution, among all the datasets analysed. Even if the time series is relatively short, a clear interannual trend is found, leading to a marked salinification, mostly occurring in the Eastern Mediterranean Sea.

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimates – such as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI time-series measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.

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

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.

Intramonth oscillations of Atlantic ITCZ observed in SMAP satellite salinity

ABSTRACTThe time variability of wind and rainfall in the Atlantic Intertropical Convergence Zone (ITCZ) has a wide oscillation spectrum that includes strong intra-month periodicities. These latter short-period oscillations span from individual convection lifecycle events of a few hours to longer pentad and multi-week convective cluster periods. This study reports on measured ITCZ rain variability at periods of a few weeks and its imprint on sea surface salinity (SSS) observed using satellite remote sensing. Despite small amplitude of variations of only psu, these changes are shown to be detectable in radiometer SSS estimates collected by the Soil Moisture Active Passive (SMAP) satellite. They are shown to develop in the mid-Atlantic ITCZ latitude band and to follow its seasonal meridional progression. These rain-correlated sea surface signatures appear quasi-synchronously across a wide range of longitude. Both the ITCZ-related seasonal migration and high zonal propagation speeds distinguish these intra-month SSS oscillations from that of tropical instability waves (TIW), with TIWs having periods greater than month and slower westward propagation. While detection of tropical Atlantic TIW salinity signatures using satellite data has received significant attention, it appears that atmospheric freshwater forcing impacts on the upper ocean at these shorter periods represents an additional process resolvable from space using SMAP. Correlation analyses of precipitation and winds indicate that low latitude convection clusters are accompanied by an upward Ekman pumping anomaly, which uplifts saltier water to surface layers diluted by enhanced precipitation. These vertical processes modify the salt balance and change the phase relationship between the oceanic SSS response and the atmospheric freshwater forcing.

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.

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.

Satellite-derived CO2 flux in the surface seawater of the Austral Ocean south of Australia

ABSTRACTA step by step algorithm for air–sea CO2 flux (F(CO2)) calculation from satellite parameters is presented in this study. Parameters used for F(CO2) calculation are: (1) sea surface temperature (SST) and chlorophyll-a (chl-a) from Moderate Resolution Imaging Spectroradiometer Aqua (MODIS Aqua), (2) sea surface salinity (SSS) estimated from MODIS Aqua SST using multiple linear regression, (3) seawater CO2 fugacity (f(CO)) estimated by MODIS Aqua SST and chl-a using feedforward neural networks, (4) atmospheric CO2 fugacity (f(CO)) from the Cape Grim station, and (5) wind speed from the Quick and Advanced Scatterometer (QSCAT and ASCAT). In situ data provided by (1) the Surveillance de l’Océan Austral – Monitoring the Southern Ocean (SURVOSTRAL) project, (2) the Integrated Marine Observed System (IMOS) project, and (3) Mesures à l’INterface Eau-aiR de la Variabilité des échanges de CO2 (MINERVE) project collected on the research vessel L’Astrolabe, are used to establish and validate the models. These models are then tested using remote sensing data. This work focus on the southern ocean from the south of Australia to the Antarctica coasts (between 43.5° S and 67° S), on the period of spring and Austral summer (from October to March, between 2002/2003 and 2014/2015). In each step, the result’s precision of experiments was determined by the root mean square error (RMSE). Results show (1) an improvement of satellite SSS estimation with a precision of ±0.16 using SST and latitude, (2) an estimation of f(CO) by satellite data with a good accuracy of ±9.45 µatm (0.96 Pa), and (3) a calculation of F(CO2) using satellite data with a global RMSE of about ±3 mmol CO2 m–2 day–1 . The interpolated F(CO2) shows that, in the period of austral spring and summer, this region absorbs the atmospheric CO2, and becomes a stronger sink of CO2 throughout the years (from an overall average absorption of about 2 mmol CO2 m–2 day–1 in 2002/2003 to about 7 mmol CO2 m–2 day–1 in 2014/2015).

Review of recent evaluation of sea surface salinity with satellite estimates in the tropical Indian Ocean

Aquarius is a mission that aims to measure the sea surface salinity (SSS) from space to provide the global salinity for climate studies. Accurate estimation of SSS is useful for the hydrological cycle, oceanographic processes, and climate. Recently, the new version (V4) of Aquarius data releases with the improving of the quality of the data and achieving the mission accuracy requirement globally on monthly scale. The results of the comparison of recently released Aquarius V4 with its preceding version’s (version 3; V3) data on a monthly time scale from 2012 to 2014 periods are highlighted. Spatial distribution of mean SSS shows that both products capture the SSS variability very well. Aquarius V4 SSS showed a minor improvement over the Aquarius V3 SSS with less root-mean-square error over the central and eastern equatorial Indian Ocean and part of Bay of Bengal. This is the region where both versions have a large difference in mean SSS. The frequency distribution is also improved in Aquarius V4 compared to Aquarius V3 average over the different regions. However, both the versions overestimate/underestimate the frequency of low/high salinity values. We also used the ocean reanalysis data to show the improvement in Aquarius V4 using the triple-point analysis method.

Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

Sea surface salinity (SSS) is one of the key components of the Earth’s global water cycle. Reliable information on SSS is very important for ocean modelling, data assimilation, and ocean and climate research applications. In this study, SSS variability in the tropical Indian Ocean (TIO) was analysed using the Aquarius instrument on board the SAC-D satellite and in situ observations from the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoys and Array for Real-Time Geostrophic Oceanography (ARGO) data sets for the period 2012–2013. Comparison of two recent versions (V2 and V3) of Aquarius-based SSS estimates to nine RAMA buoys on a daily timescale showed excellent mutual agreement. The systematic underestimation of SSS by satellite-based V2 products over the TIO shows a clear advantage for the new version product (V3). A larger root-mean-square error of the order of 0.50 psu in the satellite-based SSS was observed over the highly variable (larger standard deviation) Bay of Bengal region as compared with ARGO data sets. In the eastern equatorial Indian Ocean region, satellite-based SSS overestimated SSS below 34 psu and underestimated SSS of 34–35 psu as compared with ARGO data. However, the V3 SSS from Aquarius showed marginal improvement over V2 SSS. Monthly variation and fast Fourier analysis of the satellite-based SSS estimates are in reasonably good agreement with in situ observations which suggest that Aquarius is able to capture SSS variability in the TIO. The Aquarius-based V3 SSS showed a temporal autocorrelation of 0.6 over most parts of the TIO up to day 10, and decreased gradually with time. Overall analysis suggests that Aquarius-derived V3 SSS can detect variability in SSS satisfactorily in the TIO and is in reasonably good agreement with in situ observations.

A comparison of Multiple Non-linear regression and neural network techniques for sea surface salinity estimation in the tropical Atlantic ocean based on satellite data

Using measurements of Sea Surface Salinity and Sea Surface Temperature in the Western Tropical Atlantic Ocean, from 2003 to 2007 and 2009, we compare two approaches for estimating Sea Surface Salinity : Multiple Non-linear Regression and Multi Layer Perceptron. In the first experiment, we use 18,300 in situ data points to establish the two models, and 503 points for testing their extrapo-lation. In the second experiment, we use 15,668 in situ measurements for establishing the models, and 3,232 data points to test their interpolation. The results show that the Multiple Non-linear Regression is an admissible solution whether it be interpolation or extrapolation. Yet, the Multi Layer Perceptron can be used only for interpolation. Resume. En utilisant des mesures de Salinite et de Temperature a la surface de la mer, dans l'ouest de l' ocean Atlantique tropical, de 2003 a 2007 puis 2009, on compare deux approches pour la prediction de la Salinite dans l'eau de mer de surface : la Regression Non-lineaire Multiple et le Perceptron Multi Couches. Dans lapremi ere experience , 18 300 mesures in situ sont utilisees dans la construction des deux mod eles et 503 points pour tester leur extrapolation. Dans ladeuxi eme experience , 15 668 mesures in situ sont utilisees pour etablir les deux mod eles et 3 232 points pour tester leur interpolation. Les resultats montrent que la Regression Non-lineaire Multiple peut etre appliquee a la fois pour l'extrapolation et l'interpolation. Cependant, le Perceptron Multi Couches ne peut etre utilise que pour l'interpolation.

Soil Moisture Ocean Salinity (SMOS) salinity data validation over Malaysia coastal water

The study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef and sea grass habitats. The new challenge of SSS estimation is to exploit the ocean surface brightness temperature (Tb) observed by the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture Ocean Salinity (SMOS) satellite that is specifically designed to provide the best retrieval of ocean salinity and soil moisture using the L band of 1.4 GHz radiometer. Tb observed by radiometer is basically a function of the dielectric constant, sea surface temperature (SST), wind speed (U), incidence angle, polarization and SSS. Though, the SSS estimation is an ill-posed inversion problem as the relationship between the Tb and SSS is non-linear function. Objective of this study is to validate the SMOS SSS estimates with the ground-truth over the Malaysia coastal water. The LM iteratively determines the SSS of SMOS by the reduction of the sum of squared errors between Tb SMOS and Tb simulation (using in-situ) based on the updated geophysical triplet in the direction of the minimum of the cost function. The minimum cost function is compared to the desired threshold at each iteration and this recursive least square process updates the SST, U and SSS until the cost function converged. The designed LM's non-linear inversion algorithm simultaneously estimates SST, U and SSS and thus, map of SSS over Malaysia coastal water is produced from the regression model and accuracy assessment between the SMOS and in-situ retrieved SSS. This study found a good agreement in the validation with R square of 0.9 and the RMSE of 0.4. It is concluded that the non-linear inversion method is effective and practical to extract SMOS SSS, U and SST simultaneously.

Diatom-based estimation of sea surface salinity in the south Baltic Sea and Kattegat

A new diatom-based sea surface salinity (SSS) estimation has been applied to a collection of 27 taxa in 48 present-day sediment and surface water samples recovered in the Baltic Sea and Kattegat. The sediment core 303610-12 (2005) from the Eastern Gotland was chosen for study of Holocene sequence, ranging the last 8160 yrs BP. The Artificial Neuronal Network (ANN) method allows the estimation of spring SSS (March-April) values ranging between 7.04 ‰ and 8.25 ‰ at an averaged Root Mean Squared Error (RMSE) of 0.49 ‰. The rather low amplitude of salinity change might be caused by mixing of fresh water with upper surface layer of the Baltic Sea due to high precipitation and riverine input. The estimates of spring SSS from core 303610-12 were compared with independent geochemical proxies for salinity (K, Ti and S) derived from XRF Core Scanner record. Conspicuous correlation between salinity and sulphur records and reverse-correlation to K and Ti demonstrates that the ANN method combined with quantitative and qualitative analyses of diatoms provides a useful tool for palaeosalinity reconstructions in the Holocene sediments of the Baltic Sea

Estimation of sea surface salinity in the tropical Indian Ocean by synergistic use of SMOS and RAMA buoy data

In the present study, an attempt has been made to estimate sea surface salinity (SSS) by blending in situ observations from the National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory—Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction buoy with satellite data from the Soil Moisture and Salinity Mission using objective analysis approach. A preliminary analysis is done in the tropical Indian Ocean at monthly time scale for the year 2010 at 0.25°latitude ×0.25°longitude resolution. Comparison with other independent in situ SSS observations suggests that the analyzed SSS takes the advantage of high spatial coverage by the satellite and accurate measurements from the buoy data and has potential for better SSS estimation.

Small-Scale Variability in Sea Surface Salinity and Implications for Satellite-Derived Measurements

Abstract Calibration and validation efforts of the Aquarius and Soil Moisture and Ocean Salinity (SMOS) satellite missions involve comparisons of satellite and in situ measurements of sea surface salinity (SSS). Such estimates of SSS can differ by the presence of small-scale variability, which can affect the in situ point measurement, but be averaged out in the satellite retrievals because of their large footprint. This study quantifies how much of a difference is expected between in situ and satellite SSS measurements on the basis of their different sampling of spatial variability. Maps of sampling error resulting from small-scale noise, defined here as the root-mean-square difference between “local” and footprint-averaged SSS estimates, are derived using a solution from a global high-resolution ocean data assimilation system. The errors are mostly <0.1 psu (global median is 0.05 psu), but they can be >0.2 psu in several regions, particularly near strong currents and outflows of major rivers. To examine small-scale noise in the context of other errors, its values are compared with the overall expected differences between monthly Aquarius SSS and Argo-based estimates. Results indicate that in several ocean regions, small-scale variability can be an important source of sampling error for the in situ measurements.