The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SI...The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.展开更多
This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity prof...This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS （SMOS_SSS） with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error （RMSE） of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.展开更多
The European Space Agency will launch the first salinity satellite for remotely sensing the global soil moisture and ocean salinity (SMOS) at a sun-synchronous orbit in 2009. One of the payloads on the satellite is a ...The European Space Agency will launch the first salinity satellite for remotely sensing the global soil moisture and ocean salinity (SMOS) at a sun-synchronous orbit in 2009. One of the payloads on the satellite is a synthetic aperture microwave radiometer (MIRAS), which is an innovative instrument designed as a two-dimensional (2D) interferometer for acquiring brightness temperature (TB) at L-band (1.4 GHz). MIRAS allows measuring TB at a series of incidences for full polarizations. As the satellite travels, a given location within the 2D field of view is observed from different incidence angles. The authors develop a new scheme to retrieve the sea-surface salinity (SSS) from SMOS's TB at multi-incidence angles in a pixel, utilizing the properties of emissivity changing with incidence angles. All measurements of a given Stokes parameter in a pixel are first fitted to incidence angles in three order polynomial, and then the smoothed data are used for retrieving the SSS. The procedure will remove the random noise in TB greatly. Furthermore, the new method shows that the error in retrieved SSS is very sensitive to the system biases in the calibrated TB of the sensor, but the error in the retrieval is also a system bias, which can be corrected by post-launch validation. Therefore, this method may also serve as a means to evaluate the calibration precision in TB.展开更多
基金The National Natural Science Foundation of China under contract Nos 41830536 and 41925027the Guangdong Natural Science Foundation under contract No.2023A1515011235the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.311021008.
文摘The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.
基金The Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under contract No.S8113078001
文摘This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS （SMOS_SSS） with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error （RMSE） of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.
文摘The European Space Agency will launch the first salinity satellite for remotely sensing the global soil moisture and ocean salinity (SMOS) at a sun-synchronous orbit in 2009. One of the payloads on the satellite is a synthetic aperture microwave radiometer (MIRAS), which is an innovative instrument designed as a two-dimensional (2D) interferometer for acquiring brightness temperature (TB) at L-band (1.4 GHz). MIRAS allows measuring TB at a series of incidences for full polarizations. As the satellite travels, a given location within the 2D field of view is observed from different incidence angles. The authors develop a new scheme to retrieve the sea-surface salinity (SSS) from SMOS's TB at multi-incidence angles in a pixel, utilizing the properties of emissivity changing with incidence angles. All measurements of a given Stokes parameter in a pixel are first fitted to incidence angles in three order polynomial, and then the smoothed data are used for retrieving the SSS. The procedure will remove the random noise in TB greatly. Furthermore, the new method shows that the error in retrieved SSS is very sensitive to the system biases in the calibrated TB of the sensor, but the error in the retrieval is also a system bias, which can be corrected by post-launch validation. Therefore, this method may also serve as a means to evaluate the calibration precision in TB.
