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

Meißner, Thomas; Manaster, Andrew

doi:10.3390/rs13245120

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Second, errors in the salinity retrieval algorithms caused by the proximity to land or sea ice provide significant challenges. Ice drift, formation, and melting could significantly decrease the accuracy of standard algorithms (Fore et al, 2016;Reul et al, 2020;Meissner and Manaster, 2021). To address these challenges, features characterizing the land and sea ice fractions are included to the analyzed data set.…”

Section: Data Usedmentioning

confidence: 99%

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

Savin,

Krinitskiy,

Osadchiev

2024

Front. Mar. Sci.

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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.

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Section: Data Usedmentioning

confidence: 99%

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

Savin,

Krinitskiy,

Osadchiev

2024

Front. Mar. Sci.

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“…Other reasons for possible degradation include proximity of the retrieval to land or sea ice and the presence of sun glint or high wind speeds. The effect of the sea ice edge on SSS retrievals has been largely mitigated by a sea ice flag developed by RSS 83 .…”

Section: Sea-surface Salinitymentioning

confidence: 99%

The Microwave Climate Data Center Repository

Wentz¹

2022

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Since 1979, Remote Sensing Systems has been providing a global community of researchers and decision makers with inter-calibrated microwave measurements and geophysical retrievals derived from passive and active spaceborne sensors. These datasets, from 35 microwave sensors covering a time period of 40 years, have been consolidated at the Microwave Climate Data Center repository. The geophysical retrievals include: sea-surface temperature, near-surface ocean wind speed and direction, columnar atmospheric water vapor, columnar cloud liquid water, sea-surface rain rate, seasurface salinity, and atmospheric temperature profiles. Consistent calibration procedures and retrieval methods have been applied during the data processing to ensure these datasets are suitable for climate research. All of the geophysical retrievals relate to the air-sea boundary layer and are classified as essential climate variables by the Global Climate Observing System. In this paper, we give an overview of the microwave sensors, the inter-calibration methods, the retrieval algorithms, and the air-sea essential climate variable datasets housed at the Microwave Climate Data Center.

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Impact of microwave observations on the estimation of Arctic sea surface temperatures

Nielsen-Englyst,

Høyer,

Karagali

et al. 2024

Remote Sensing of Environment

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

Cited by 7 publications

References 29 publications

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

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

The Microwave Climate Data Center Repository

Impact of microwave observations on the estimation of Arctic sea surface temperatures

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