The Radiative Transfer Mechanism And Retrieval Method Of Remote Sensing Of Sea Surface Salinity

Sea surface salinity(SSS)is an important physical property of seawater that,together with sea surface temperature(SST),influences the density of seawater and therefore plays an important role in physical circulation,ecosystem dynamics,water identification,and global water circulation.In the past,salinity data sources were generally ship-borne observations and continuous buoy observations.Although the measurement accuracy is high,the measured data have the disadvantage of being sparse and unevenly distributed in space,and therefore insufficient to address the spatial and temporal variability of salinity in the global ocean.Since 2009,the launch of three satellites for remote sensing of SSS,namely the Soil Moisture and Ocean Salinity(SMOS)satellite from European Space Administration(ESA),the Aquarius/SAC satellite,and the Soil Moisture Active Passive(SMAP)satellites developed by the National Aeronautics and Space Administration(NASA),together with the release of operational remote sensed salinity products,the feasibility of global sea surface salinity observations from satellite platforms has been demonstrated.However,compared with other sea surface parameters(e.g.,SST,wind speed,etc.),accurate retrieval of SSS is more challenging due to the low sensitivity of the satellite observed brightness temperatures to the salinity changes(the sensitivity of bright sea surface temperature to salinity changes is less than 1 K/psu for high water temperatures and less than 0.5 K/psu for low water temperatures).Thus accurate retrieval of sea surface salinity requires an accurate radiative transfer model,correcting for the effects of impact factors(sea surface roughness,foam,atmospheric absorption emission,exogenous radiation,etc.)during radiation transportation.In this paper,the main sources of error from the sea surface and the atmosphere in the salinity retrieval process are investigated to support the production of high precision remote sensed SSS products.First,a coupled ocean-atmosphere microwave radiative transfer model is proposed in this paper.The model integrates the influence of the coupled sea surface-atmosphere system on the radiative transfer process.The model simulates the emission and scattering effects of the anisotropic rough sea surface and the atmospheric attenuation effects.Finally,the brightness temperature reaching the Top of Atmosphere(TOA),which is observed by space-borne microwave sensors,is calculated by integrating the effects of all the oceanic or atmospheric factors.The accuracy of the developed model is also validated in this paper.The model simulations are compared with the simulation results of RTTOV and RT4 radiative transfer models as well as SMOS and Aquarius satellite observations,respectively.And the results show that the model has relatively high accuracy.The results of the inter-model comparison show that under two different sea surface conditions(flat sea surface conditions and rough sea surface conditions),the simulated results of the present model are in good agreement with the two developed classical radiative transfer models(RT4 model and RTTOV model),and the deviation between models is basically less than 2%.On the other hand,the model simulations are validated by comparing with SMOS and Aquarius observations.The results show that the model simulations have a good consistency with the satellite observations,and in general,the deviation of the present model reaches about 0.9 K-1.3 K.Based on the developed radiative transfer model,this paper estimates the effects of sea surface-atmosphere parameters,mainly sea surface temperature,wind speed,rainfall,atmospheric attenuation,foam,and Faraday rotation,and explores the correction methods for the effects of each parameter.The results show that each parameter significantly affects the salinity retrieval processes due to the low sensitivity(0.2-0.8 K/psu)of the brightness temperature to the SSS.Therefore,the correction methods for the effects of each parameter are demonstrated in this paper.Then,the developed model is applied to the real situation,and the model accuracy is tested,especially for extreme atmospheric conditions(high wind speed or heavy rain).For high wind speed conditions,a region in the Westerly zone of the Southern Ocean is selected to verify the accuracy of the correction model and the retrieved SSS are compared with the Argo measurements for validation.For the heavy rain conditions,a typical study area is also selected for validation,and the model simulations are compared with the satellite observations for validation.The results show that the accuracy is greatly improved after the model correction.Finally,this paper explores the methods of salinity retrieval in the near-shore area,and a new optimization method for near-shore sea surface emissivity and a spatial resolution enhancement algorithm for microwave salinity products are developed.The results showed that the optimized method improved the accuracy compared with the original satellite algorithm and improved the spatial resolution of salinity products in nearshore areas.