Soil Moisture And Ocean Salinity Sea Surface Salinity Research Articles

Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR).

Precipitation Estimates from SMOS Sea‐Surface Salinity

Two L‐Band (1.4 GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) currently provide sea‐surface salinity (SSS) measurements. At this frequency, salinity is measured in the first centimetre below the sea surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS‐estimated negative RR due to measurement noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15 min apart (r = 0.7 at 1° × 1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time‐scale.

Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity

The Soil Moisture and Ocean Salinity (SMOS) mission has provided a unique remote sensing capability for observing key variables of the hydrological cycle, such as the Sea Surface Salinity (SSS). However, due to some limitations related to the instrument interferometric concept and its challenging data processing, SMOS SSS maps still display significant artifacts and biases, especially close to the coast, mainly due to the presence of Radio Frequency Interferences (RFI) and Land-sea contamination (LSC). In this paper, a new methodology for filtering salinity retrievals and correcting for spatial biases is introduced and validated.

Retrieval of eddy dynamics from SMOS sea surface salinity measurements in the Algerian Basin (Mediterranean Sea)

AbstractThe circulation in the Algerian Basin is characterized by the presence of fresh‐core eddies that propagate along the coast or at distances between 100 and 200 km from the coast. Enhancements in the processing of the Soil Moisture and Ocean Salinity (SMOS) data have allowed to produce, for the first time, satellite sea surface salinity (SSS) maps in the Mediterranean Sea that capture the signature of Algerian eddies. SMOS data can be used to track them for long periods of time, especially during winter. SMOS SSS maps are well correlated with in situ measurements although the former has a smaller dynamical range. Despite this limitation, SMOS SSS maps capture the key dynamics of Algerian eddies allowing to retrieve velocities from SSS with the correct sign of vorticity.

Statistical Models of Sea Surface Salinity in the South China Sea Based on SMOS Satellite Data

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. Three statistical methods applied without considering the physical effects of the input parameters are proposed to calculate SSS from soil moisture and ocean salinity (SMOS)-measured brightness temperature (TB) values and associated auxiliary data. Using these three statistical methods, named multiple linear regression (MLR) model, principal component regression (PCR) model, and quadratic polynomial regression (QPR) model, the first predictions of daily and monthly averaged SSS are made with $1 ^\circ\times1 ^\circ$ spatial resolution in the South China Sea (SCS, in the study area of 4°N-25 °N, 105°E-125°E) during the period between April and June 2013. Results are compared with the corresponding SMOS SSS products and Aquarius SSS products and validated using Argo measurements. Validation results show that the root-mean-squared error (RMSE) of the QPR model is around 0.46 practical salinity units (psu) compared to 0.58 psu for Aquarius daily SSS products. World Ocean Atlas (WOA13) SSS data are also used for validation in the SCS and the QPR model gives a 0.54-psu value of RMSE, which may be compared with 0.69 psu, 0.73 psu for SMOS and Aquarius Level-3 (L3) SSS products, respectively.

The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state

AbstractSalinity is a key ocean state property, changes in which reveal the variation of the water cycle and the ocean thermohaline circulation. However, prior to this century, in situ salinity observations were extremely sparse, which decreased the reliability of simulations of ocean general circulation by ocean and climate models. In 2009, sea surface salinity (SSS) observations covered the global ocean via the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission, and several versions of global SSS products were subsequently released. How can these data benefit model performance? Previous studies found contradictory results. In this work, we assimilated SMOS‐SSS data into the LASG/IAP Climate system Ocean Model (LICOM) using the Ensemble Optimum Interpolation (EnOI) assimilation scheme. To assess and quantify the contribution of SMOS‐SSS data to model performance, several tests were conducted. The results indicate that the CECOS/CATDS 2010.V02 SMOS‐SSS product can significantly improve model simulations of sea surface/subsurface salinity fields. This study provides the basis for the future assimilation of SMOS‐SSS data for short‐range climate forecasting.

Improving time and space resolution of SMOS salinity maps using multifractal fusion

Abstract The multifractal fusion is a non-parametric technique that merges remotely-sensed maps of different ocean variables to produce higher quality remote sensing products. This method was first introduced to reduce the noise level in a map of a given ocean variable provided that a higher-quality map of a second ocean variable is available. In this work, we go one step further in both the theoretical justification and the application of the multifractal fusion. In the first part of the paper we explore if the quality of the derived maps can be improved by using a higher-dimensional tensor relating the gradient of one variable with that of the other. Higher-dimension tensors require the introduction of appropriate integration schemes to avoid inducing long-range artifacts, something that is not completely solved yet. The results show that although higher-dimension tensors lead to an improvement in the spatial consistency of the derived maps (i.e., a higher quality of the associated singularity exponents), the comparison with in situ data still show significant reconstruction errors as compared to the simplified scalar approach. In the second part of the paper we have used the scalar approach to increase the spatial and temporal resolution of the current Soil Moisture and Ocean Salinity (SMOS) Level 3 sea surface salinity (SSS) maps produced at the Barcelona Expert Center (BEC). As such, new daily SMOS SSS global maps are generated at a 0.05° × 0.05° grid resolution. The quality of the resulting products has been assessed using in-situ comparison, singularity analysis and power density spectra. It is concluded that the multifractal fusion can be used to significantly increase the spatial and temporal resolution of the SSS maps.

Mitigation of systematic errors in SMOS sea surface salinity

Sea Surface Salinity (SSS) acquired by the Soil Moisture and Ocean Salinity (SMOS) satellite mission are subject to systematic errors originating from various non-geophysical contaminations such as land contamination. These systematic errors reach more than 2 pss in some regions close to the land with very strong spatial gradients according to the coast orientation and the across-track position within the satellite swath. An empirical method to estimate and correct the time independent systematic errors from resampled quasi L2 SMOS SSS is presented. The method is based on self-consistency hypothesis of long term (July 2010–June 2014) variability of SMOS SSS among SMOS dwell-lines and orbit orientation (ascending and descending). The bias correction is performed by first adjusting SSS relative variations among dwell-lines and orbits orientation and then by determining a mean correction over four years. A reference time series of SSS and the associated relative systematic error for each dwell-line and orbit orientation is estimated using a least square approach. Then, the 4-year mean value of corrected SMOS SSS is adjusted relative to a 4-year mean in situ data climatology. Four years of salinity maps mitigated from systematic errors are presented. Independent validation using in situ thermosalinograph SSS from ships of opportunity is presented. The new SMOS bias corrected SSS over the global ocean shows an improvement of 32% of the RMSD.

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.

Analysis of SMOS sea surface salinity data using DINEOF

Abstract An analysis of daily Sea Surface Salinity (SSS) at 0.15 ° × 0.15° spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite mission using DINEOF (Data Interpolating Empirical Orthogonal Functions) is presented. DINEOF allows reconstructing missing data using a truncated EOF basis, while reducing the amount of noise and errors in geophysical datasets. This work represents a first application of DINEOF to SMOS SSS. Results show that a reduction of the error and the amount of noise is obtained in the DINEOF SSS data compared to the initial SMOS SSS data. Errors associated to the edge of the swath are detected in 2 EOFs and effectively removed from the final data, avoiding removing the data at the edges of the swath in the initial dataset. The final dataset presents a centered root mean square error of 0.2 in open waters when comparing with thermosalinograph data at their original spatial and temporal resolution. Constant biases present near land masses, large scale biases and latitudinal biases cannot be corrected with DINEOF because persistent signals are retained in high order EOFs, and therefore these need to be corrected separately. The signature of the Douro and Gironde rivers is detected in the DINEOF SSS. The minimum SSS observed in the Gironde plume corresponds to a flood event in June 2013, and the shape and size of the Douro river shows a good agreement with chlorophyll-a satellite data. These examples show the capacity of DINEOF to remove noise and provide a full SSS dataset at a high temporal and spatial resolution with reduced error, and the possibility to retrieve physical signals in zones with high initial errors.

Sea surface salinity variability in the Agulhas Current region inferred from SMOS and Aquarius

Abstract Understanding of the Agulhas Current's unique retroflection and ring shedding has been greatly improved by satellite altimetry data over the last several decades, but salinity remote sensing technology has not been available to conduct similar studies with respect to regional sea surface salinity (SSS) variability. ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Aquarius/SAC-D salinity missions can provide such data, but it remains unclear if the accuracy of these sensors is high enough to properly study this dynamic region. To address this issue, this study validates both SMOS and Aquarius against monthly in situ data from Argo floats. All available, corresponding data points over a three year period have root mean square differences less than 0.2 pss and correlation coefficients greater than 0.9. Additionally, over 80% of differences between the products fall between ± 0.5 pss. Surface salt transports were calculated to see how well the satellite products estimate SSS advection in the Agulhas region. Zonal and meridional surface salt transports have annual maxima in austral winter and minima in austral summer, although the seasonal range is small. Using Argo, SMOS, and Aquarius SSS for this calculation produces statistically identical results, suggesting the utility of using the satellites to monitor regional salinity advection at higher frequencies and horizontal resolutions than previously possible using only sparse in situ data. SSS gradients along the subtropical front reveal that SMOS and Aquarius are much more effective tools for observing small-scale structure prevalent in the meandering of the Agulhas Return Current. Finally, we conclude that when compared to Argo floats in situ observations SMOS and Aquarius are statistically accurate and may yet prove powerful tools for observing Agulhas salinity processes on smaller time and spatial scales.

Roughness and foam signature on SMOS-MIRAS brightness temperatures: A semi-theoretical approach

Abstract The influence of wind speed (WS) on sea surface emission at L-band is revisited using the updated version v6.2 of Soil Moisture and Ocean Salinity (SMOS) brightness temperatures (TB) and an incoherent two-layer foam emissivity model. The influence of the roughness effect due to surface waves is consistent with the one found with an older version of SMOS TB. The two-layer incoherent model of foam emissivity accounts for weak volume scatterings and multiple reflections within a medium with an exponential vertical permittivity profile. The foam emissivity simulated using this model at L band varies from 0.35 to close to 1 with thickness varying from 0.01 cm to 2 cm. The wind induced brightness temperature components deduced from the multi-angular SMOS TB is used to optimize the foam void fraction (defined as the fraction of a unit volume of seawater that is occupied by air) at the air–sea interface, an effective thickness of foam layer and the dependency of foam coverage with wind speed. A new set of parameters for the foam emissivity model and the foam coverage model that can be used for WS up to 22 m s − 1 for the SMOS sea surface salinity retrieval is proposed. Our foam coverage model derived from SMOS data is now in much better agreement with other estimates derived from other sensors, although it predicts slightly lower coverage at all winds speeds, likely due to the longer wavelength of SMOS measurements.

Sea Surface Salinity and Temperature Budgets in the North Atlantic Subtropical Gyre during SPURS Experiment: August 2012-August 2013

Variability at large to meso-scale in sea surface salinity (SSS) and sea surface temperature (SST) is investigated in the subtropical North Atlantic Ocean during the Subtropical Atlantic Surface Salinity Experiment Strasse/SPURS in August 2012 - August 2013. The products of the Soil Moisture and Ocean Salinity (SMOS) mission corrected from large scale systematic errors are tested and used to retrieve meso-scale salinity features, while OSTIA products, resolving meso-scale temperature features are used for SST. The comparison of corrected SMOS SSS data with drifter's in situ measurements from SPURS experiment shows a reasonable agreement, especially during winter time with RMS differences on the order of 0.15 pss (for 10 days, 75 km resolution SMOS product). The analysis of SSS (SST) variability reveals that the meso-scale eddies contribute to a substantial freshening (cooling) in the central high salinity region of the subtropical gyre, albeit smaller than Ekman and atmospheric freshwater (heat) seasonal flux, which are the leading terms in SSS (SST) budget. An error is estimated along with SSS and SST budgets; as well as sensitivity to the different products in use and residuals are discussed. The residuals in the SSS budget are large and can arise from errors in the advection fields and freshwater flux, from neglected small scale or unresolved local processes (salt fingering, vertical mixing and small scale subduction, etc.). However, their magnitude is similar to what is often parameterized as eddy horizontal diffusion to close large scale budgets.

Pacific Ocean surface freshwater variability underneath the double ITCZ as seen by satellite sea surface salinity retrievals

AbstractThe salinity budget of the upper tropical Pacific Ocean underneath the double Intertropical Convergence Zone (ITCZ) is studied using the Soil Moisture and Ocean Salinity (SMOS) and Aquarius surface salinity observations as well as in situ salinity measurements. In this shallow mixed layer region of the ocean, precipitation effects on the near‐surface salinity budget are large, typically leading to a band of fresh sea surface salinity (SSS) between March and June. The role of precipitation during the freshening period is documented here through a direct correlation between the SMOS SSS fields and the monthly accumulated precipitation. During the same period, the mixed layer salinity budget is impacted by advection, which, based on in situ observations, is found to be another important mechanism for the evolution of the near‐surface salinity as documented through a connection between the north equatorial eastern Pacific Fresh water pool and this south equatorial freshwater pattern in boreal spring. However, given the information at hand, the near‐surface salinity budget cannot be closed, suggesting that other processes are important too, such as nonlinear effects, mixing, and entrainment.

Erratum to: Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

Erratum to: Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

A method for correcting regional bias in SMOS global salinity products

Soil Moisture and Ocean Salinity (SMOS) Level 3 (L3) sea surface salinity (SSS) products are provided by the Barcelona Expert Centre (BEC). Strong biases were observed on the SMOS SSS products, thus the data from the Centre Aval de Traitement des Donnees SMOS (CATDS) were adjusted for biases using a large-scale correction derived from observed differences between the SMOS SSS and World Ocean Atlas (WOA) climatology data. However, this large-scale correction method is not suitable for correcting the large gradient of salinity biases. Here, we present a method for the correction of SSS regional bias of the monthly L3 products. Based on the stable characteristics of the large SSS biases from month to month in some regions, corrected SMOS SSS maps can be obtained from the monthly mean values after removing the regional biases. The accuracy of the SMOS SSS measurements is greatly improved, especially near the coastline, at high latitudes, and in some open ocean regions. The SMOS and ISAS SSS data are also compared with Aquarius SSS to verify the corrected SMOS SSS data. The correction method presented here only corrects annual mean biases. The measurement accuracy of the SSS may be improved by considering the influence of atmospheric and ocean circulation in different seasons and years.

Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic

AbstractSpaceborne sea surface salinity (SSS) measurements provided by the European Space Agency's (ESA) “Soil Moisture and Ocean Salinity” (SMOS) and the National Aeronautical Space Agency's (NASA) “Aquarius/SAC‐D” missions, covering the period from May 2012 to April 2013, are compared against in situ salinity measurements obtained in the northern North Atlantic between 20°N and 80°N. In cold water, SMOS SSS fields show a temperature‐dependent negative SSS bias of up to −2 g/kg for temperatures <5°C. Removing this bias significantly reduces the differences to independent ship‐based thermosalinograph data but potentially corrects simultaneously also other effects not related to temperature, such as land contamination or radio frequency interference (RFI). The resulting time‐mean bias, averaged over the study area, amounts to 0.1 g/kg. A respective correction applied previously by the Jet Propulsion Laboratory to the Aquarius data is shown here to have successfully removed an SST‐related bias in our study area. For both missions, resulting spatial structures of SSS variability agree very well with those available from an eddy‐resolving numerical simulation and from Argo data and, additionally they also show substantial salinity changes on monthly and seasonal time scales. Some fraction of the root‐mean‐square difference between in situ, and SMOS and Aquarius data (approximately 0.9 g/kg) can be attributed to short time scale ocean processes, notably at the Greenland shelf, and could represent associated sampling errors there.

SMOSSea Surface Salinity signals of tropical instability waves

Sea Surface Salinity (SSS) measurements from the Soil Moisture and Ocean Salinity (SMOS) mission provide an unprecedented opportunity to observe the salinity structure of tropical instability waves (TIWs) from space, especially during the intense 2010 La Nina condition. In the eastern equatorial Pacific Ocean, SMOS SSS signals correlate well and have similar amplitude to 1 m salinity from the Tropical Atmosphere Ocean (TAO) array at six locations with strong TIW signals. At these locations, the linear negative relationships between SMOS SSS and OSTIA SST signals vary from −0.20°C−1 to −0.25°C−1, which are comparable to the ones obtained from TAO. From June to December 2010, the largest TIW signals and meridional gradients of both SSS and SST appear around 2°N west of 100°W. They shift southward and cross the equator at 90°W. In addition to the large negative correlation band around 2°N, a band of negative correlations between SSS and SST signals also exists around 8°N west of 110°W for the 33 day signals. The peak amplitude of the 33 day SMOS SSS signals west of 135°W is reduced by >40% with respect to values east of 135°W, while the reduction for SST is much lower (<20%). The amplitudes and longitudinal extents of TIW signals and the dominant westward propagation speed of 17 day TIWs (as detected by SMOS and Aquarius) at the equator decrease from 2010 to 2013 associated with the transition from a strong La Nina to non-La Nina conditions.

Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

The SMOS (soil moisture and ocean salinity) mission undertaken by the European Space Agency (ESA) has provided sea surface salinity (SSS) measurements at global scale since 2009. Validation of SSS values retrieved from SMOS data has been done globally and regionally. However, the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail. In this study, we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea (SCS) expedition during autumn 2011. The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data. Accuracy in terms of bias and root mean square error (RMS) of the SSS retrieved using three different sea surface roughness models is very consistent, regardless of ascending or descending orbits. When ascending and descending measurements are combined for comparison, the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements, with bias -0.33 practical salinity units and RMS 0.74. We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS. The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements. Therefore, accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.

A first estimation of SMOS‐based ocean surface T‐S diagrams

AbstractA first estimation of satellite‐based ocean surface T‐S diagrams is performed by using SMOS Sea Surface Salinity (SSS) and OSTIA Sea Surface Temperature (SST) and comparing them with in situ measurements interpolated fields obtained by the Argo‐buoys for the North Atlantic and over the entire year 2011. The key objectives at the base of this study are: (1) To demonstrate the feasibility of generating routinely satellite‐derived surface T‐S diagrams, obviating the lack of extensive sampling of the surface open ocean, (2) To display the T‐S diagrams variability and the distribution/dynamics of SSS, altogether with SST and the relative density with respect to in situ measurements, and (3) To assess the SMOS SSS data added value in detecting geophysical signals not sensed/resolved by the Argo measurements. To perform the latter analysis, the satellite‐Argo mismatches have been overlapped with geophysical parameters of precipitation rates, surface heat and freshwater fluxes and wind speed data. Ongoing and future efforts focus on enlarging the study area and the temporal frame of the analysis and aim at developing a method for the systematic identification of surface water masses formation areas by remotely sensed data.