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| 1. Introduction |
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| Salinity is one of the most important marine parameters, which controls many processes such as physical circulations, biogeochemistry dynamics from regional to global ocean [1,2]. |
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| Although drifters and buoys, together with cruises, have accumulated large amount in-situ water salinity data in different regions, it is still difficult to monitor global ocean salinity with high temporal-spatial resolution by the in situ measurements. |
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| In the past decade, with the successful launch of the soil moisture and ocean salinity (SMOS) satellite by the European Space Agency (ESA) [3], the Aquarius/SAC-D satellite [4], and the soil moisture active passive (SMAP) satellite [5,6] by the National Aeronautics and Space Administration (NASA), global sea surface salinity (SSS) observations from space have become possible and a significant improvement has been made to understand the ocean dynamics and climate change. |
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| The early concept of remote sensing of SSS has been demonstrated in the late 1970s with observations by Skylab [7] and two airborne L-band radiometry experiments [8,9]. |
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| At the end of 1990, two airborne microwave interferometers, the electronically scanned thinned array radiometer (ESTAR) and the scanning low-frequency microwave radiometer (SLFMR), successfully produced SSS maps in coastal areas in agreement with in-situ measurements with an accuracy of about 1 psu. |
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| Based on many experiments, the L-band is evidenced as the optimal frequency for remote sensing of SSS, which has been adopted by SMOS, Aquarius/SAC-D and SMAP. |
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| However, the sensitivity of satellite measured brightness temperature to SSS is quite low. |
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| For example, the sensitivity of vertically polarized brightness temperature to SSS variation is 0.4 to 0.8 K/psu for different observing angles and sea surface temperatures (SST), and it is only 0.2 to 0.6 K/psu for the horizontal polarization brightness temperature [10]. |
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| Thus, remote sensing of SSS requires a highly accurate retrieval model. |
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| It is widely accepted that the corrections of the sea surface and atmospheric effects are essential for remote sensing of SSS, since these effects could alter the value of sensor-measured brightness temperature and introduce errors into the SSS retrieval process. |
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| Besides the atmospheric effects, the increasing of sea surface emissivity due to the sea surface roughness and foam effects is the main source of error, which could significantly hamper the accuracy of SSS retrieval [11]. |
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| Over the past decades, the correction for sea surface roughness effects were studied based on the in-situ and airborne measurements; for example, the experiments made from a tower [12], wind and salinity experiments (WISE) [13,14], airborne Passive-Active L-band Sensor (PALS) campaign [15] and Combined Airborne Radio instruments for Ocean and Land Studies (CAROLS) campaigns [16,17]. |
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| Many rough surface emission models have also been developed based on the theoretical and empirical methods. |
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| Among these models, the small-slope approximation/small perturbation model (SSA / SPM) [18,19,20,21], two scale model (TSM) [22,23,24] and empirical/semi-empirical models [25,26] have been widely used by the research community and implemented in different satellite data processing systems. |
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| As the foam effect is significant at high wind speed conditions (above a threshold of 12 m/s) due to strong wave breaking, it has been corrected by numerous models; for example, the semi-empirical models [27,28] and radiative transfer equation (RTE) based models [29,30], which were developed to estimate the foam covered sea surface emissivity. |
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| Although many theoretical and empirical models have been developed, some problems are still unsolved. |
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| For example, the TSM originally proposed to estimate brightness temperature at higher frequencies, uses the sea surface wave spectrum by multiplying a factor of 2. |
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| However, whether this modification can be applied to L-band is still unclear, and the choice of cutoff wavenumber is arbitrary and needs to be clarified in the L-band. |
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| Moreover, the sea surface reflection of downwelling atmospheric emission is another contribution to satellite-observed brightness temperature [31], thus the determination of the cutoff wavenumber is required not only for sea surface emission but also for reflection. |
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| In addition, the widely used empirical models decouple the wind effect from SSS and SST effects, which means that the surface emission is due to a perfectly flat sea surface and the wind-roughened sea surface. |
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| The wind-roughened sea surface is associated with the increased brightness temperature (due to sea surface roughness effect), which induces regional biases when applied to different areas [32]. |
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| Furthermore, the satellite measurements of SSS are hampered by the effect of radio frequency interference (RFI) in offshore areas of China (i.e., Bohai sea, Yellow sea and East China sea), which causes a large amount of data to be discarded [33]. |
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| Thus, the compatibility of these models in coastal area of China needs to be assessed and tested in order to achieve higher accuracies of SSS from the space-borne observations. |