Sea Surface Salinity Retrieval Research Articles

An Empirical Algorithm for Mitigating the Sea Ice Effect in SMAP Radiometer for Sea Surface Salinity Retrieval in the Arctic Seas

The L-band radiometer onboard the Soil Moisture Active Passive (SMAP mission is used to retrieve sea surface salinity (SSS over global ocean. In the Arctic seas, one of the major challenges of SSS remote sensing is the presence of sea ice. This paper proposes a data-driven ice correction (IC algorithm which extracts emission from the water portion of measured brightness temperature (TB in scenes mixed with water and ice. Emission of the ice portion was removed based on estimation according to the ice fraction (fice in the satellite footprint and ice signature derived from surrounding pixels. The IC algorithm is applied to SMAP TB data to obtain TB with ice correction (TBIC, which are used for SSS retrieval using the standard JPL SMAP CAP processing system. We show that the algorithm is most effective near the ice edge, thereby increasing the fice threshold for possible SSS retrieval to 15% from the current 3% without IC. SMAP SSS are validated using in situ salinity collected during NASAs Ocean Melting Greenland (OMG mission from 2016 to 2020 along the Greenland coast. The number of collocations between OMG and SMAP daily gridded salinity increased by more than 30% with IC. The statistical analysis shows a similar retrieval accuracy with or without IC, with the standard deviation of the difference between OMG and SMAP of 1.41 psu (with IC and 1.42 psu (without IC . The bias adjusted SMAP SSS depicts salinity patterns and gradients around Greenland consistent with OMG measurements.

New insights into SMOS sea surface salinity retrievals in the Arctic Ocean

Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band. It provides the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, the SSS retrieval at high latitudes is a challenge because of the low sensitivity L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents, of sea ice and of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval induce significant systematic errors at low temperatures. We propose a novel empirical correction to mitigate those sources of errors at high latitudes.Comparisons with in-situ measurements ranging from 1 to 11 m depths spotlight huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.28 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD between TOPAZ and in-situ SSS = 1.86 pss), particularly in river plumes with very large SSS spatial gradients.

Comprehensive Vector Radiative Transfer Model for Estimating Sea Surface Salinity From L-Band Microwave Radiometry

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<; 1%) for low wind speeds (<; 1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K.

Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing

Monsoon rain and rivers bring large freshwater input to the Northern Bay of Bengal (BoB), yielding low Sea Surface Salinity (SSS) after the monsoon. The resulting sharp upper-ocean salinity stratification is thought to influence tropical cyclones intensity and biological productivity by inhibiting vertical mixing. Despite recent progresses, the density of in situ data is far from sufficient to monitor the BoB SSS variability, even at the seasonal timescale. The advent of satellite remotely-sensed SSS (SMOS, Aquarius, SMAP) offers a unique opportunity to provide synoptic maps of the BoB SSS every ~8 days. Previous SMOS SSS retrievals did not perform well in the BoB. Here, we show that improved systematic error corrections and quality control procedures yield a much better performance of the new “debiased v4” CATDS level-3 SSS from SMOS (~0.8 correlation, 0.04 bias and 0.64 root-mean-square difference to more than 28,000 collocated in situ data points over 2010–2019). The SMOS product now performs equivalently to Aquarius, and is slightly inferior to SMAP over the BoB. In particular, SMAP and SMOS are able to capture salinity variations close to the east coast of India (r > 0.8 within 75–150 km of the coast). They thus capture the seasonal freshening there, associated with equatorward advection of the Northern BoB low-salinity water by the East Indian Coastal Current (EICC) after the summer monsoon. The 10-year long SMOS record further allows to describe the BoB interannual SSS variability, which is strongest in boreal fall in relation with the Indian Ocean Dipole (IOD). Positive IOD events induce a weakening of the southward export of freshwater by the EICC, and hence negative SSS anomalies in the Northern BoB and positive ones along the East Indian coast. This confirms results from earlier studies based on modelling, sparse in situ data, or shorter satellite records, but this time from a 10-year long SSS record. Overall, our study indicates that the new SMOS retrieval can be confidently used to monitor the BoB SSS and to study its mechanisms. We end by a brief description of the BoB SSS anomalies associated with the extreme 2019 IOD event and highlight the very good performance over the BoB of a new multi-satellite product developed by the European Space Agency merging SMOS, Aquarius and SMAP data.

Variability and Uncertainty of Satellite Sea Surface Salinity in the Subpolar North Atlantic (2010–2019)

Satellite remote sensing of sea surface salinity (SSS) in the recent decade (2010–2019) has proven the capability of L-band (1.4 GHz) measurements to resolve SSS spatiotemporal variability in the tropical and subtropical oceans. However, the fidelity of SSS retrievals in cold waters at mid-high latitudes has yet to be established. Here, four SSS products derived from two satellite missions were evaluated in the subpolar North Atlantic Ocean in reference to two in situ gridded products. Harmonic analysis of annual and semiannual cycles in in situ products revealed that seasonal variations of SSS are dominated by an annual cycle, with a maximum in March and a minimum in September. The annual amplitudes are larger (&gt;0.3 practical salinity scale (pss)) in the western basin where surface waters are colder and fresher, and weaker (~0.06 pss) in the eastern basin where surface waters are warmer and saltier. Satellite SSS products have difficulty producing the right annual cycle, particularly in the Labrador/Irminger seas where the SSS seasonality is dictated by the influx of Arctic low-salinity waters along the boundary currents. The study also found that there are basin-scale, time-varying drifts in the decade-long SMOS data records, which need to be corrected before the datasets can be used for studying climate variability of SSS.

Validation and correction of sea surface salinity retrieval from SMAP

In this study, sea surface salinity (SSS) Level 3 (L3) daily product derived from soil moisture active passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity (SMOS) and in-situ measurements. Generally, the root mean square error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature (SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.

Can the Accuracy of Sea Surface Salinity Measurement be Improved by Incorporating Spaceborne GNSS-Reflectometry?

Sea surface salinity (SSS) can be measured by L-band (1.4 GHz) radiometry. However, the L-band brightness temperature is sensitive to ocean surface roughness, so that the precise knowledge of sea state can help to improve the accuracy of SSS retrievals. Cyclone Global Navigation Satellite System (CyGNSS) mission measures tropical ocean wind speeds, which can provide further knowledge about sea state. This letter, by investigating the sensitivity of brightness temperature derived from two L-band radiometry satellite missions [i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)] to CyGNSS data, first explores the potential of using spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) to improve the accuracy of SSS measurements. The statistical results from two-year CyGNSS data show that the systematic uncertainties of the brightness temperature up to 0.5 K in the SMOS/SMAP can be minimized at low wind speed, which proves the concept that CyGNSS wind speed data can be used to improve the accuracy of SSS retrievals across the oceans. The findings strongly suggest that the spaceborne GNSS-R instruments could be utilized as cost-effective hosted payloads in designing future ocean remote sensing missions.

Retrieval and Analysis of Sea Surface Salinity in the Adjacent Waters of the Yangtze River Estuary Based on Multisource Satellite Data

Cao, K.; Sun, W.; Chen, L.; Meng, J., and Zhang, J., 2020. Retrieval and analysis of sea surface salinity in the adjacent waters of the Yangtze River Estuary based on multisource satellite data. Journal of Coastal Research, 36(3), 590–599. Coconut Creek (Florida), ISSN 0749-0208.Moderate Resolution Imaging Spectroradiometer, Sea-Viewing Wide Field-of-View Sensor, and Visible Infrared Imaging Radiometer Suite multisource satellite remote sensing reflectance data are used with in situ sea surface salinity (SSS) measurements in the Yellow Sea and East China Sea. A linear algorithm is developed for the retrieval of summer SSS in adjacent waters of the Yangtze Estuary in the Yellow Sea and East China Sea. The accuracy of SSS retrieval results is validated using independent in situ measurements. Results show that the mean bias of the model is –0.34 psu, the root-mean-square error is 1.55 psu, and the correlation coefficient is 0.80. The linear model is extended to the retrieval of multisource satellite monthly remote sensing reflectance data to obtain monthly SSS products of the Yangtze Estuary. The spatial coverage of fused satellite SSS data is about 10% greater than that of single-satellite monthly retrieved SSS data. The SSS in the Yangtze Estuary research area has lower values nearshore and higher values offshore. The SSS gradually rises from nearshore areas to open water, and an obvious low-salinity water tongue forms in the Yangtze Estuary. The eastward expansion of freshwater flows of the Yangtze River into the sea is mainly affected by the runoff of the Yangtze River, while a southerly wind and northeastward flow in the research area are the main factors responsible for the eastward expansion of the Yangtze River freshwater flows to the NE. The effect of freshwater on the Yellow Sea and East China Sea extends to 125.5° E, and the low-salinity freshwater tongue distributes northeastward. The expansion types of the Yangtze River freshwater include NE expansion, N-NE expansion, E-NE expansion, bidirectional expansion, and nondirectional expansion.

Aquarius semi-theoretical model of sea surface salinity retrieval for foam-covered surface

ABSTRACTFor high wind speeds (7–22 m s−1), a semi-theoretical model for retrieving sea surface salinity (SSS) is derived from the version V3.0 of Aquarius data. In the model, the sea surface brightness temperature is the sum of the brightness temperatures of foam-covered sea surface and rough sea surface, which is weighted by the foam coverage. The emissivity of foam layer is dealt with the effective permittivity of foam composites and the wave approach theory, where the air volume fraction and thickness of the foam layer are optimized with Aquarius data. Due to the influence of foam coverage, the foam contribution to the sea surface emissivity is smaller than that of the rough sea surface, only about 20% at 22 m s−1. Furthermore, the model is applied to retrieve Aquarius SSS for the wind speeds larger than 7 m s−1. The inverse accuracy is assessed by comparison with ARGO monthly gridded salinity products. The average root mean square error (RMSE) between the retrieved SSS and Argo SSS are about 0.20 and 0.30 psu for the latitude within 40° S–40° N and 70° S–70 N, respectively. The model improves the retrieval accuracy of SSS for high wind speeds.

Assessment and Improvement of Sea Surface Microwave Emission Models for Salinity Retrieval in the East China Sea

Accurate prediction of sea surface emission is the key for sea surface salinity retrieval from satellite microwave radiometer. In order to retrieve salinity from satellite observation, several sea surface microwave emission models have been developed based on theoretical or empirical methods and validated by in-situ measurements in different regions. However, their performances are still unclear in the Chinese coastal waters. In this study, based on two cruises measurements in the East China Sea (ECS), including the brightness temperature measured by a shipborne microwave radiometer and other auxiliary data (sea surface salinity, sea surface temperature and wind speed), the performances of different sea surface emission models are tested. The results showed that the developed models provide fairly good accuracy in predicting brightness temperature; for example, the accuracy of small perturbance/small scale approximation model (SPM/SSA), two-scale model (TSM) and empirical model is in the range from 0.6 K to 3 K. Moreover, the TSM and empirical models are further improved by optimizing the model parameters in the ECS. Finally, the sea surface salinity were retrieved from shipborne measured data based on the improved models, and the results show that the root mean square (rms) differences between retrieved and in-situ sea surface salinity is about 0.4 psu, indicating the significant improvement by the regional model parameters.

Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction

AbstractThe present article introduces a new sea surface salinity (SSS) retrieval technique from the Soil Moisture Active Passive (SMAP) L–band (1.4 GHz) microwave radiometer. The ocean surface wind speed (WS) correction, which is very critical in any of the SSS retrieval techniques, is accounted for with a different approach. A model function between ΔSSS (the difference between SSS of flat and rough ocean surfaces) and ocean surface WS is developed and used to correct the impact of ocean surface roughness due to the winds on SSS. In addition to WS correction, the retrieval technique appropriately accounts for the effects of atmospheric attenuation on L‐band frequency due to water vapour and oxygen, and ocean surface temperature on SSS. The performance of the estimations is statistically evaluated by comparing the estimations with the in situ Argo, buoy SSS, and SMAP version 3, 70 km resolution SSS observations in global oceans during 2016. The retrieved salinity well captured the global distribution patterns of SSS, including low and high values. Comparison of retrieved salinity with Argo and SMAP products show a root‐mean‐square error (RMSE) of 0.32 and 0.39 psu, a Pearson correlation coefficient of 0.94 and 0.93, and a scatter index of 0.0093 and 0.011 respectively on a monthly time‐scale. In addition, retrieved SSS promisingly captured day‐to‐day variability in SSS observed by tropical moored buoy SSS with RMSE of 0.36 psu and a Pearson correlation coefficient of 0.94. The validation confirms the potential ability of the new retrieval technique in capturing SSS changes on daily and monthly time‐scales.

Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

Empirical Characterization of the SMOS Brightness Temperature Bias and Uncertainty for Improving Sea Surface Salinity Retrieval

After more than eight years of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) acquisitions, an exhaustive, empirical characterization of the biases and uncertainties affecting SMOS brightness temperatures over the ocean is possible. We show that both parameters strongly depend not only on the position in the field of view, but also on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and the uncertainty are developed and used for quantitatively assessing the locations where SMOS errors are currently not accurately characterized. This characterization can be used for the definition of a new empirical SMOS sea surface salinity (SSS) bias correction, a better cost function retrieval, and more accurate filtering criteria, which are expected to lead to a better SMOS SSS Level 2 product. We present a new L2 SMOS SSS product based on the described investigation. The performance of this preliminary product is similar to that of the version v662 of the official L2 SMOS SSS product at medium and low latitudes. However, it provides a better coverage at high latitudes and coastal regions affected by radio frequency interference (RFI), which correspond to those regions where the SMOS errors are currently poorly estimated.

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

Preliminary Analysis of the Potential and Limitations of MICAP for the Retrieval of Sea Surface Salinity

A new payload concept has been proposed: the microwave imager combined active/passive (MICAP). MICAP is a combined one-dimensional microwave interferometric radiometer that operates at 1.4, 6.9, 18.7, and 23.8 GHz and L-band (1.26 GHz) scatterometer. It has the capability to simultaneously remotely sense sea surface salinity (SSS), sea surface temperature (SST), and wind speed. MICAP will be a candidate payload onboard the Ocean Salinity Satellite led by the State Oceanic Administration of China to monitor SSS and reduce geophysical errors caused by surface roughness and SST. To provide an “all-weather” estimation of SSS with high accuracy from space, the errors of the simultaneous retrieval of multiparameters using MICAP are analyzed, and noise levels and stability requirements of instruments are estimated. Preliminary analysis shows that MICAP can provide SSS with an accuracy of 1 psu for single measurement and 0.1 psu over the global ocean for 200 × 200 km resolution pixels and one month at middle and low latitudes with default instrument noises (0.1 K, 0.3 K and 0.3 K for the L, C, and K band radiometers, respectively, and 0.1 dB for the L-band scatterometer) while uncertainties of the drift corrections are less than the radiometer sensitivities.

Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics

Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.

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.