Salinity Retrieval Research Articles

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within ±0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

A vector radiative transfer model for sea-surface salinity retrieval from space: a non-raining case

ABSTRACTSea-surface salinity (SSS) can be measured from space using a microwave sensor. However, achieving the desired accuracy in SSS retrieval is challenging due to the lower sensitivity of the brightness temperature to SSS especially at low sea-surface temperature conditions. The retrieval accuracy can be further degraded due to the atmospheric and sea-surface effects (including emission and reflection), which require more accurate correction methods based on the radiative transfer model. In this article, a vector radiative transfer model (VRTM) was developed based on a matrix operator method that considers the ocean–atmosphere system under non-raining conditions. The results from this model were compared with measurement data provided by the Soil Moisture and Ocean Salinity (SMOS) satellite sensor and the results from two other RT models (RT4 model and a forward model of the European Space Agency, ESA). Statistical evaluation of these results revealed that estimation errors of top of atmosphere (TOA) radiance by the VRTM model was less than 0.3% as compared to the RT4 model results. The difference of the brightness temperatures predicted by the VRTM model and measured by the SMOS was within 1.5 K which was better than the ESA’s forward model predictions. These results suggest that the VRTM is relatively more accurate and has high computational efficiency for simulating the TOA brightness temperature for various scientific research and remote-sensing applications.

Comparison of the Retrieval of Sea Surface Salinity Using Different Instrument Configurations of MICAP

The Microwave Imager Combined Active/Passive (MICAP) has been designed to simultaneously retrieve sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS), and its performance has also been preliminarily analyzed. To determine the influence of the first guess values uncertainties on the retrieved parameters of MICAP, the retrieval accuracies of SSS, SST, and WS are estimated at various noise levels. The results suggest that the errors on the retrieved SSS have not increased dues poorly known initial values of SST and WS, since the MICAP can simultaneously acquire SST information and correct ocean surface roughness. The main objective of this paper is to obtain the simplified instrument configuration of MICAP without loss of the SSS, SST, and WS retrieval accuracies. Comparisons are conducted between three different instrument configurations in retrieval mode, based on the simulation measurements of MICAP. The retrieval results tend to prove that, without the 23.8 GHz channel, the errors on the retrieved SSS, SST, and WS for MICAP could also satisfy the accuracy requirements well globally during only one satellite pass. By contrast, without the 1.26 GHz scatterometer, there are relatively large increases in the SSS, SST, and WS errors at middle/low latitudes.

Development of a MODIS data based algorithm for retrieving nearshore sea surface salinity along the northern Gulf of Mexico coast

ABSTRACTRemote sensing algorithms for retrieval of Sea Surface Salinity (SSS) were generally developed for deep ocean waters while practical applications of SSS are commonly involved in coastal (particularly nearshore) waters. To fill the gap, this paper presents a new SSS algorithm, called Nearshore SSS Algorithm, developed using the Artificial Neural Networks toolbox in the MATLAB Program and 7 years of cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data as well as in situ data collected from United States Geological Survey (USGS) stations along the Northern Gulf of Mexico coast. The independent input variables involved in the new algorithm include the water-leaving reflectance at 412 nm, 443 nm, 488 nm, 555 nm, 678 nm, and 645 nm of MODIS Aqua sensor. Results of sensitivity analysis indicate that SSS in nearshore waters is most sensitive to reflectance at 412 nm and 488 nm, followed by the reflectance at 555 nm and 645 nm. While reflectance at 678 nm is also important, reflectance at 443 nm is the least important to SSS in nearshore waters, providing new insights into SSS in coastal waters. MODIS data collected from January 2012 to September 2014 were used for validation of the nearshore SSS algorithm. The linear correlation coefficient of this algorithm for Louisiana coast was 0.7256 and 0.6985 for the development and validation, respectively. A unique feature of this paper is that while the new algorithm is particularly useful to the retrieval of SSS in nearshore waters, it is also expanded to further offshore waters, providing reliable SSS data for various applications of coastal environment and resources management particularly along the northern Gulf of Mexico coast.

Sea Surface Salinity Retrieval Under Rain Based on Aquarius Combined Active/Passive Observations

Equipped with an L-band radiometer, SMOS, and Aquarius provide an unprecedented sea surface salinity (SSS) dataset of the global oceans in days. The sensitivity of L-band brightness temperature (TB) to SSS variation is about 0.3–0.8 K/psu, which means the salinity signal in TB is very weak. Enormous efforts are devoted to the development, evaluation, and improvement of the SSS retrieval algorithm especially under some unfavorable conditions, i.e., the rain. Rain drops inducing freshening and roughness effects on the sea surface have made the SSS retrieval challenging for years. This paper describes a new method to separate the freshening and roughness effects of rainfall based on the combined active/passive observations of Aquarius. The dependence of the sea surface emissivity (sensitive to both roughness and freshening) on the backscatter (only sensitive to roughness) is obtained and the rain-induced roughness is corrected. The method is applied to the salinity retrieval under rain. The retrieval results ( $\text{SSS}_{rc}$ ) are compared with HYCOM data corrected by the rain impact model ( $\text{SSS}_{\text{HYCOM}\_\text{RIM}}$ ). The bias of $\text{SSS}_{\text{rc}}$ shows no clear dependence on rain rate. However, the bias of the standard product of Aquarius ( $\text{SSS}_{\text{ADPS}}$ , V4.0) rises sharply with rain rate. Furthermore, the standard deviation of $\text{SSS}_{\text{rc}}$ is about 0.5 psu, which is also superior to $\text{SSS}_{\text{ADPS}}$ (0.9 psu). The above results confirm the feasibility of this new retrieval algorithm for the SSS remote sensing in rainy weather.

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

AbstractBased on a joint analysis of an ensemble mean of satellite sea surface salinity retrievals and the output of a high‐resolution numerical ocean circulation simulation, physical processes are identified that control seasonal variations of mixed‐layer salinity (MLS) in the Indian Ocean, a basin where salinity changes dominate changes in density. In the northern and near‐equatorial Indian Ocean, annual salinity changes are mainly driven by respective changes of the horizontal advection. South of the equatorial region, between 45°E and 90°E, where evaporation minus precipitation has a strong seasonal cycle, surface freshwater fluxes control the seasonal MLS changes. The influence of entrainment on the salinity variance is enhanced in mid‐ocean upwelling regions but remains small. The model and observational results reveal that vertical diffusion plays a major role in precipitation and river runoff dominated regions balancing the surface freshwater flux. Vertical diffusion is important as well in regions where the advection of low salinity leads to strong gradients across the mixed‐layer base. There, vertical diffusion explains a large percentage of annual MLS variance. The simulation further reveals that (1) high‐frequency small‐scale eddy processes primarily determine the salinity tendency in coastal regions (in particular in the Bay of Bengal) and (2) shear horizontal advection, brought about by changes in the vertical structure of the mixed layer, acts against mean horizontal advection in the equatorial salinity frontal regions. Observing those latter features with the existing observational components remains a future challenge.

Improvements on Calibration and Image Reconstruction of SMOS for Salinity Retrievals in Coastal Regions

Estimating sea surface salinity by European Space Agency's soil moisture and ocean salinity (SMOS) mission in coastal regions is still a challenging task. The brightness temperature measured by the SMOS interferometric radiometer is biased at coastal areas by the presence of land–sea contamination and other variable sources of error, mainly human-induced radio frequency interferences (RFI). In this paper, we will discuss on the benefits for retrieving salinity maps in coastal regions of applying two correction techniques that enhance the quality of brightness temperatures: the correction of residual multiplicative errors at calibration level (for the mitigation of systematic biases close to landmasses) and the nodal sampling at imaging level (for the reduction of Gibbs-like contamination). The joint application of both techniques leads to significant improvements in salinity retrievals not only in open ocean, but also in strongly contaminated coastal regions. Comparisons of SMOS salinity observations with in situ measurements show that biases of salinity differences are lower and standard deviations are significantly reduced with respect to those obtained from the current SMOS brightness temperatures.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

Several remotely sensed sea surface salinity (SSS) retrievals with various resolutions from the soil moisture and ocean salinity (SMOS) and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles (S) using multilinear regressions. The performance is evaluated using a total root mean square (RMS) error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°, which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.

Sea surface salinity observed from the HY-2A satellite

Motivated by the shortcomings of radio frequency interferences (RFI) associated with the spaceborne L-band radiometers near the Northwest Pacific and previous study near the Amazon plume, this study presents a sea surface salinity (SSS) retrieval algorithm from the microwave radiometer onboard the HY-2A satellite. The SSS signal is improved by differentiating the reflectance between the C and X band. A reflectance calibration method is proposed by using a combination of radiative transfer model (RTM) and the Klein-Swift emissivity model. Evaluations of the retrieved SSS from the HY-2A satellite indicate that the root mean square error (RMSE) is about 0.35 psu on 0. 5 degree grid spacing and monthly time scale which is comparable to the accuracy of SMOS and Aquarius-SAC/D satellites.

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.

Sea surface salinity observed from the HY-2A satellite

Motivated by the shortcomings of radio frequency interferences (RFI) associated with the spaceborne L-band radiometers near the Northwest Pacific and previous study near the Amazon plume, this study presents a sea surface salinity (SSS) retrieval algorithm from the microwave radiometer onboard the HY-2A satellite. The SSS signal is improved by differentiating the reflectance between the C and X band. A reflectance calibration method is proposed by using a combination of radiative transfer model (RTM) and the Klein-Swift emissivity model. Evaluations of the retrieved SSS from the HY-2A satellite indicate that the root mean square error (RMSE) is about 0.35 psu on 0.5 degree grid spacing and monthly time scale which is comparable to the accuracy of SMOS and Aquarius-SAC/D satellites.

Impact of sea surface temperature on sea surface salinity retrieval by SMOS microwave radiometer

Impact of sea surface temperature on sea surface salinity retrieval by SMOS microwave radiometer

The Effects of Sea Surface Wind on Sea Surface Salinity Retrieval based on Spaceborne Microwave Radiometer

The Effects of Sea Surface Wind on Sea Surface Salinity Retrieval based on Spaceborne Microwave Radiometer

Improved Sea Ice Fraction Characterization for L-Band Observations by the Aquarius Radiometers.

Radiometers operating at L-band (1.4 GHz) are used to retrieve sea surface salinity over ice-free oceans and have been used recently to study the cryosphere. One hindrance of their use in the high latitudes is the preponderance of mixed scenes, where seawater and sea ice are both present in the sensor's field of view (FOV). Accurately characterizing the scene is crucial for oceanographic and cryospheric applications. To that end, a sea ice fraction model, composed of passive microwave sea ice concentration retrievals and an instrument simulator that integrates radiative power coming from all around the antenna, is used. We investigate the model currently used operationally to derive the ice fraction affecting the Aquarius observations and show that it can be significantly improved. On the one hand, the current model tends to overestimate sea ice fraction in the marginal ice zone where observations are used for salinity retrievals. On the other hand, the current model underestimates ice fraction within the ice pack where observations are used to derive sea ice properties. For the northern hemisphere, we also find evidence of the sea ice type impact on L-band radiometric observations. We present a model to derive sea ice fractions that are in better agreement with Aquarius radiometric observations using the Advanced Microwave Scanning Radiometer 2 Bootstrap algorithm for sea ice concentration and using high-resolution integration over the sensor's FOV.

Sensitivity of Ocean Surface Salinity Measurements From Spaceborne L-Band Radiometers to Ancillary Sea Surface Temperature

Sea surface temperature (SST) serves as a crucial ancillary input field to the retrieval algorithm for sea surface salinity (SSS) from L-band satellite radiometers, such as Soil Moisture and Ocean Salinity mission, Aquarius, and Soil Moisture Active Passive mission. It is needed for inverting the radiative transfer model equation of the ocean surface emissivity, which depends both on ocean surface salinity and ocean surface temperature. Our analysis studies the sensitivity of the performance of the Aquarius salinity retrieval algorithm to the ancillary SST that is used in the algorithm. We have retrieved Aquarius salinities using four different SST fields as ancillary input, namely, the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)-only Optimum Interpolation SST (OISST), the SST from WindSat, the SST from the Canadian Meteorological Center (CMC), and the Multi-scale Ultra-high Resolution (MUR) SST from the National Aeronautics and Space Administration's Jet Propulsion Laboratory. The retrieved Aquarius SSS is compared with ground truth data; thus, the performance of the salinity retrieval algorithm in all four cases can be evaluated. The WindSat, CMC, and MUR SST products, which are all based on or are assimilating SST measurements from the passive microwave (MW) sensors, give better performance than the NOAA AVHRR-only OISST, which does not use any MW SST data, but which is solely based on the in situ data and observations from the infrared AVHRR sensor. The CMC SST gives the best overall performance for the retrieved SSS. The sensitivity of the SSS retrievals and therefore the performance differences between the various ancillary input fields increases in cold water.

From field experiments to salinity products: a tribute to the contributions of Jordi Font to the SMOS mission

This article summarizes some of the activities in which Jordi Font, research professor and head of the Department of Physical and Technological Oceanography, Institut de Ciencies del Mar (CSIC, Spanish National Research Council) in Barcelona, has been involved as co-Principal Investigator for Ocean Salinity of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Mission from the perspective of the Remote Sensing Lab at the Universitat Politecnica de Catalunya. We have probably left out some of his many contributions to salinity remote sensing, but we hope that this review will give an idea of the importance of his work. We focus on the following issues: 1) the new accurate measurements of the sea water dielectric constant, 2) the WISE and EuroSTARRS field experiments that helped to define the geophysical model function relating brightness temperature to sea state, 3) the FROG 2003 field experiment that helped to understand the emission of sea foam, 4) GNSS-R techniques for improving sea surface salinity retrieval, 5) instrument characterization campaigns, and 6) the operational implementation of the Processing Centre of Levels 3 and 4 at the SMOS Barcelona Expert Centre.

Towards decadal soil salinity mapping using Landsat time series data

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985–2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985–2000 (−0.74gkg−1/10a, p<0.001), and increased within 2000–2015 (0.79gkg−1/10a, p<0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.

Status of Radio Frequency Interference (RFI) in the 1400–1427 MHz passive band based on six years of SMOS mission

Abstract European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by Radio Frequency Interference (RFI) that jeopardizes part of its scientific retrieval in certain areas of the world. Areas affected by RFI might experience data loss or underestimation of soil moisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put several strategies into place that help improve the RFI situation, filter the SMOS data from RFI perturbed measurements and bring awareness to the RFI problem.

Snow density and ground permittivity retrieved from L-band radiometry: Application to experimental data

Abstract The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between − 6 kg m − 3 and 15 kg m − 3 for the forest clearing site, and between 37 kg m − 3 and 90 kg m − 3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈ 45 km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.

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.