Sea Surface Salinity Retrieval Research Articles

Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches

Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of in situ salinity measurements in the Russian Arctic seas collected during multiple field surveys is applied to train and validate the machine learning models. The error in SSS retrieval of the developed algorithm compared to the standard algorithm reduced from 3.15 to 2.15 psu, and the correlation with in situ data increased from 0.82 to 0.90. The obtained daily SSS fields are important to improve accurate assessment of spatial and temporal variability of large river plumes in the Arctic Ocean.

Drivers of Laptev Sea interannual variability in salinity and temperature

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

Correction of the SMAP Sea Surface Brightness Temperature and Retrieval of Sea Surface Salinity Incorporating CYGNSS Observables

Correction of the SMAP Sea Surface Brightness Temperature and Retrieval of Sea Surface Salinity Incorporating CYGNSS Observables

Automated retrieval of sea surface salinity from satellite data and investigation of the spatio-temporal variation of ocean salinity across the West and Central African coasts

Sea Surface Salinity (SSS) is an important variable that affects the physical properties of the oceanic environment. Satellite sensors have been developed to routinely acquire SSS concentrations. However, many in the broader scientific community do not engage with this data, because it requires relevant remote sensing skills and tools to extract and process into formats that can be readily utilized by non-remote sensing experts. To address this issue, we used Python, PostgreSQL database, and Power BI visualization tool, to develop an automated tool, that facilitates the retrieval, processing, analysis, and visualisation of data from the Optimally Interpolated Sea Surface Salinity (OISSS) dataset, comprising data from the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP), and Soil Moisture and Ocean Salinity (SMOS) satellite missions. Using this tool, we extracted long term series of SSS from 2011 to March 2022. In addition, an interactive reporting dashboard was developed in this research to enable users to visualize and interrogate the underlying dataset. Analysis of this data revealed detailed spatiotemporal patterns of SSS variations in the study area across varying spatial and temporal scales. We were able to identify the cold and hot spots of SSS, as well as longitudinal (interand intra-annual) variations in SSS concentrations. We anticipate that the outcome of this research will boost further research in this area, contribute to future climatic change studies and stimulate the democratization of remote sensing data. Climatic changes have been found to have profound effects on ocean salinity. The data obtained in this research could also feed into machine learning models for predicting future impacts of climate change on salinity concentrations across the ocean. In addition, the ability to monitor the salinity on a regular basis can be deployed alongside other relevant variables to forecast future climatic changes. Due to the fact that salinity is an excellent indicator for water cycle changes, the ability to routinely monitor salinity at relatively higher spatial and temporal resolutions is of great significance to the local community, as it could be used to understand current and future water cycle regimes in the area. The water cycle has profound effect on various socio-economic activities including agriculture and food security, ecology, hydrology (water quality), and drought.

An improved sea surface salinity retrieval algorithm for the Chinese Bohai Sea based on hyperspectral reconstruction and its applicability analysis

Salinity is regarded as a critical factor in marine systems. It is essential to monitor the long-term changes in sea surface salinity (SSS), as well as its short-term changes. Along these lines, in this work, to monitor the hourly, daily, and monthly changes in SSS in the Chinese Bohai Sea from GOCI (Geostationary Ocean Color Imager) images, the feasibility of adopting a MERIS-based SSS retrieval algorithm was systematically explored to develop a GOCI-based SSS retrieval algorithm. To avoid the influence of the atmospheric correction error on Rrs(λ) of the similar bands of MERIS (490 nm, 560 nm, and 665 nm) and those of GOCI (490 nm, 555 nm, and 660 nm), a simple linear regression model was applied based on the ρrs(λ) matchup of the similar bands by using a hyperspectral reconstruction method with the measured ρ(λ). Despite the aerosol-based scattering errors, a more reasonable result (R2 = 0.86, RMSE = 0.79 psu) over a long time scale was gained by the improved SSS retrieval algorithm, which was validated by the measured data (R2 = 0.92, RMSE = 0.72 psu). On top of that, the temporal and spatial distribution of SSS, as well as its diurnal, daily, and monthly changes, were then obtained in the Chinese Bohai Sea from April 2011 to March 2012. The impact of runoff on SSS was also analysed based on instant and lag correlations.

Impact of Rain Effects on L-Band Passive Microwave Satellite Observations Over the Ocean

L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good accuracy for rain effect correction.

Can We Retrieve Sea Surface Salinity With Polarimetric Radar Measurements?

Can We Retrieve Sea Surface Salinity With Polarimetric Radar Measurements?

Aquarius sea surface salinity retrieval in coastal regions based on deep neural networks

A sea surface salinity (SSS) retrieval model for coastal regions (0–200 km) is here proposed using a deep neural network (DNN) which demonstrates the capability of the DNN model to retrieve SSS. The rain filters are applied to the Aquarius observation data and SSS data used in this paper when there is heavy rainfall. Then, Aquarius V5 L2 observation data and hybrid coordinate ocean model SSS (HYCOM SSS) data are used to train the model, and the grid search and 10-fold cross-validation method is used to obtain optimal model parameters. After screening, a DNN model with three hidden layers and 30, 40, and 50 neurons in each layer is constructed. The performance of the proposed DNN model is then estimated and analyzed using independent testing data. We compared the SSS retrieval results with different types of salinity data sources. Compared to in-situ SSS, the retrieved SSS root mean squares error (RMSE) is 0.35 and the mean bias (MB) is −0.01. The RMSE and MB of retrieved SSS are reduced by 0.14 and 0.03 than those of the Aquarius Remote Sensing Systems (Aquarius RSS) SSS, and 0.31 and 0.03 than those of the Aquarius Combined Active-Passive (Aquarius CAP) SSS. Results show that our DNN model had better performance than Aquarius official in most coastal regions. Our results provide a dependable reference for SSS retrievals in global coastal regions.

The Contribution of the Vendée Globe Race to Improved Ocean Surface Information: A Validation of the Remotely Sensed Salinity in the Sub-Antarctic Zone

The Vendée Globe is the world’s most famous solo, non-stop, unassisted sailing race. The Institute of Marine Sciences and the Barcelona Ocean Sailing Foundation installed a MicroCAT on the One Ocean One Planet boat. The skipper, Dídac Costa, completed the round trip in 97 days, from 8 November 2020 to 13 February 2021, providing one measurement of temperature and conductivity every 30 s during navigation. More than half of the ship’s route was in the sub-Antarctic zone, between the tropical and polar fronts, and it passed through areas of oceanographic interest such as Southern Patagonia (affected by glacier melting), the Brazil–Malvinas confluence, the Southern Pacific Ocean, and the entire Southern Indian Ocean. This sailing race gave a rare opportunity to measure in-situ sea surface salinity in a region where satellite salinity measurements are not reliable. Due to the decreased sensitivity of brightness temperature to salinity in cold seas, retrieving sea surface salinity at high latitudes remains a major challenge. This paper describes how the data are processed and uses the data to validate satellite salinity products in the sub-Antarctic zone. The sailing race measurements represent surface information (60 cm depth) not available from drifters or Argo floats. Acquiring measurements using round-the-world sailing races would allow us to analyse the evolution of ocean salinity and the impact of changes in the ice extent around Antarctica.

Performance of SMAP and SMOS Salinity Products under Tropical Cyclones in the Bay of Bengal

To compare the accuracy of satellite salinity data of level-3 Soil Moisture Active Passive V4.0 (SSMAP) and debiased v5 CATDS level-3 Soil Moisture and Ocean Salinity (SSMOS) before and after tropical cyclones (TCs) in the Bay of Bengal (BoB), this study used the sea surface salinity of Argo (SArgo) to assess SSMAP and SSMOS before and after the passage of 10 TCs from 2015 to 2019. The results indicate that the SSMAP and SSMOS agreed well with SArgo before and after 10 TCs. It can be seen that the correlation between SSMAP and SArgo (before TCs: SSMAP = 0.95SArgo + 1.52, R2 = 0.83; after TCs: SSMAP = 0.87SArgo + 4.34, R2 = 0.79) was obviously higher than that of SSMOS and SArgo (before TCs: SSMOS = 0.68SArgo + 10.38, R2 = 0.62; after TCs: SSMOS = 0.88SArgo + 3.98, R2 = 0.58). The root mean square error (RMSE) was also significantly higher between SSMOS and SArgo (before TCs: 0.84 psu; after TCs: 0.78 psu) than between SSMAP and SArgo (before TCs: 0.58 psu; after TCs: 0.47 psu). In addition, this study compared SSMAP and SSMOS during two TCs that swept in nearshore and offshore waters, and the results show good agreement between SSMAP and SArgo in the nearshore and offshore waters of BoB. In the BoB, both SSMAP and SSMOS can retrieve sea surface salinity well, and SSMAP is overall better than SSMOS, but the SMOS salinity product can fill the gap of SMAP from 2010 to 2015.

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

Abstract. This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightness temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land–sea and ice–sea contamination, the high contamination by radio-frequency interference (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters, and the poor characterization of dielectric constant models for the low SSS range in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics. Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9 d maps in a 0.25∘ grid; https://doi.org/10.20350/digitalCSIC/13859, González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05∘ grid; https://doi.org/10.20350/digitalCSIC/13860, González-Gambau et al., 2021b)​​​​​​​, which are computed by applying multifractal fusion to L3 SSS with SST maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatiotemporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga. The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region, and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse.

Measurement of SST and SSS Using Frequencies in the Range 0.3–2.0 GHz

AbstractWide bandwidth radiometer systems that make measurements at multiple frequencies in the range from 300 MHz to 2 GHz have been proposed to address parameters important for understanding issues in the cryosphere associated with climate change such as ice sheet thickness and temperature. It is also possible with such a system to retrieve sea surface salinity (SSS), which is important for understanding the impact of climate change on ocean circulation at high latitude. In contemporary sensors for retrieving SSS, such as on Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), sea surface temperature (SST), another parameter important for understanding ocean circulation and necessary in the retrieval of salinity, is treated as an ancillary parameter obtained from an independent source. However, both SSS and SST have peaks in sensitivity below 1 GHz; and it has been shown that measurements at multiple frequencies in this portion of the spectrum can take advantage of this peak in sensitivity to improve the accuracy of the retrieval of SSS. In this manuscript, it will be shown that there is also the potential to retrieve SST and, in cold water, the possibility for improved accuracy over existing retrievals.

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.

Automated Retrieval of Sea Surface Salinity from the Aquarius, Smap, and Smos Satellite Data and Investigation of the Spatiotemporal Variation of Ocean Salinity Across the West and Central African Coasts

Automated Retrieval of Sea Surface Salinity from the Aquarius, Smap, and Smos Satellite Data and Investigation of the Spatiotemporal Variation of Ocean Salinity Across the West and Central African Coasts

Using Saildrones to Assess the SMAP Sea Surface Salinity Retrieval in the Coastal Regions

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this study, we assess SSS retrieved from the Soil Moisture Active Passive (SMAP) mission in coastal region. SMAP SSS products from the Jet Propulsion Laboratory (JPL), and from the Remote Sensing Systems (RSS) are collocated with in situ data collected by saildrones during the North American West Coast Survey. Satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) assocciated with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference (stdD) for collocations with SMAP Level 3 (8 days average) between 40 and 100km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where there exists large freshwater variance. We analyze the different land correction approaches independently developed at JPL and RSS using SMAP Level 2 matchups. We found that JPL's land correction method is more promising in pushing SMAP SSS retrieval towards land. For future improvement, we suggest implementing dynamic land correction versus the current climatology based static land correction to reduce uncertainty in estimating land contribution. In Level 2 to Level 3 processing, a more rigorous quality control may help to eliminate outliers and deliver reliable Level 3 products without over-smoothing, which is important in resolving coastal processes such as fronts or upwelling.

SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures.

Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA/Remote Sensing Systems (RSS) SMAP SSS retrieval algorithm in regards to obtaining accurate SSS measurements in the polar oceans. Our analysis finds a strong correlation between 8-day averaged SMAP L-band brightness temperature (TB) bias and TB measurements from the Advanced Microwave Scanning Radiometer (AMSR2) in the C-through Ka-band frequency range for sea-ice contaminated ocean scenes. We show how this correlation can be employed to develop: (1) a discriminant analysis that is able to reliably flag the SMAP observations for sea-ice contamination and (2) subsequently remove the sea-ice contamination from the SMAP observations, which results in significantly more accurate SMAP SSS retrievals near the sea-ice edge. We provide a case study that evaluates the performance of the proposed sea-ice flagging and correction algorithm. Our method is also able to detect drifting icebergs, which go often undetected in many available standard sea-ice products and thus result in spurious SMAP SSS retrievals.

Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds.

Multi-Dimensional Analysis on the Temporal and Spatial Variability of Sea Surface Salinity in South China Sea

Sea surface salinity (SSS) is an important parameter of the physical marine environment and its variation has significant effects on the hydrodynamic environment in the South China Sea. With the developing of sea surface salinity retrieval technology based on SMOS data, intuitive SSS could be obtained. Study on the SSS could help better understand the role of salinity in ocean circulation and the global water cycle. In this paper, multi-dimensional analysis methods will be applied to study on the temporal and spatial variability pattern of sea surface salinity from 2014 to 2019 in South China Sea. In this paper, to reveal the temporal and spatial variability of SSS in South China Sea, time series analysis, geostatistical method and EOF decomposition algorithms were adopted. The purpose is studying on the temporal and spatial variability pattern of sea surface salinity from 2014 to 2019 in South China Sea.

Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches

ABSTRACT Sea salinity is one of the indicators of the global water cycle and affects the surface and deep circulation of the ocean. While passive microwave satellite sensors have been used to monitor sea surface salinity (SSS), the uncertainties from radio frequency interference (RFI) and low sea surface temperature often result in large errors, especially in river-dominated coastal seas. This study investigated the improvement of the Soil Moisture Active Passive (SMAP) SSS over five river-dominated oceans over the globe using three machine learning approaches (i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN)). Four SMAP products and four ancillary data used in the SMAP SSS retrieval algorithm were used as input variables to the machine learning models. The results showed that all models improved the SMAP SSS product by up to 28% reduced in the root mean square error (RMSE) for validation, and RF yielded better performance than SVR and ANN. The calibration and validation RMSEs by RF were 0.203 and 0.556 practical salinity unit (psu), while those of SMAP SSS were 0.774 psu. The improved SSS well captured the spatiotemporal patterns of SSS for not only low but also high salinity water for all five regions. The proposed approach can be used to operationally improve the global SMAP SSS product including other coastal areas and the near Polar regions in the future.

First Assessment of CyGNSS-Incorporated SMAP Sea Surface Salinity Retrieval Over Pan-Tropical Ocean

In sea surface salinity (SSS) retrieval using L-band passive radiometry, radiometer-independent ocean wind speed is needed as auxiliary data. Wind speed data from scatterometer and weather models are commonly used as auxiliary data in satellite SSS missions. This papers overarching goal is to explore the feasibility of incorporating the Cyclone Global Navigation Satellite System (CyGNSS) data into the SSS retrieval algorithm of the Soil Moisture Active and Passive (SMAP) mission over tropical and subtropical oceans. As a proof-of-concept study, empirical geophysical model functions (GMF) in the retrieval algorithm are developed using the statistics of collocated SMAP, CyGNSS, and referenced buoys measurements. The SSS accuracy of CyGNSS-incorporated salinity retrieval is investigated against the SMAP SSS data product. Comparisons show that the proposed CyGNSS-incorporated retrieval algorithm improves the SSS accuracy by 0.1~0.2 psu at low wind speed (< 2 m/s). To some extent, it proves that Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) could be a new and helpful data source to understand wind-induced emissivity over a smooth ocean. The dependencies of emissivity on different geophysical parameters (i.e., sea surface temperature, significant wave height, and precipitation) are analyzed, and the spatial and seasonal variabilities of SSS errors are shown and linked to these geophysical parameters. The findings of this research provide valuable insights for future development and operation of the radiometer-based SSS retrieval algorithm using wind speed data from spaceborne GNSS-R.