RFI and Remote Sensing of the Earth From Space

Vine, David M. Le

doi:10.1142/s2251171719400014

Cited by 10 publications

(1 citation statement)

References 32 publications

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“…The time range of two SMOS products considered in our study is Jan. ,2011-Dec., 2018. The SMAP SSS V4 8-day running averages data on the 0.25° × 0.25° grids is chosen to compare the SMOS SSS data after correction [33], because SMAP has less landsea contamination and better RFI mitigation than SMOS [28], [34], [35].…”

Section: A Datamentioning

confidence: 99%

Correction of Satellite Sea Surface Salinity Products Using Ensemble Learning Method

et al. 2023

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Although salinity satellites can provide high-resolution global sea surface salinity (SSS) data, the satellite data still display large errors close to the coast. In this paper, a nonlinear empirical method based on random forest is proposed to correct two Soil Moisture and Ocean Salinity (SMOS) L3 products in the tropical Indian Ocean, including SMOS BEC and SMOS CATDS data. The agreement between in-situ data and the corrected SMOS data is better than that between in-situ data and the original satellite data. The rootmean-square deviation (RMSD) of the satellite SSS data decreased from 0.366 to 0.275 and from 0.367 to 0.255 for SMOS BEC and SMOS CATDS, respectively. The effect of the correction model was better in the Arabian Sea than in the Bay of Bengal. The RMSD of corrected BEC (CATDS) SSS was reduced from 0.44 (0.48) to 0.276 (0.269), and the correlation coefficient was increased to 0.915 from 0.741(0.801) in the Arabian Sea while the correlation coefficient improved less than 0.02 in the Bay of Bengal. The crossvalidation results highlight the robustness and effectiveness of the correction model. Additionally, the effects of different features on the correction model are discussed to demonstrate the vital role of geographical information in the correction of satellite SSS data. The proposed method outperformed other machinelearning methods with respect to the RMSD and correlation coefficient.

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Section: A Datamentioning

confidence: 99%

Correction of Satellite Sea Surface Salinity Products Using Ensemble Learning Method

et al. 2023

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Impact of river-discharged freshwater on surface ocean environments revealed by synergistic use of satellite measurements in the East China Sea

Kang,

Moon,

Kim

et al. 2024

Estuarine, Coastal and Shelf Science

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

Vine

Dinnat

2022

Radio Science

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Modern remote sensing radiometers in space at the long wavelength end of the microwave spectrum, such as flown on Soil Moisture and Ocean Salinity (SMOS) spacecraft (Kerr et al., 2010) and Soil Moisture Active Passive (SMAP) spacecraft (Entekhabi et al., 2010(Entekhabi et al., , 2014, operate in the narrow 27 MHz of spectrum at 1.413 GHz protected for passive use. Operating in protected spectrum is necessary to detect natural thermal emission from the surface and avoid being overwhelmed by interference from manmade sources of radiation. These radiometers have demonstrated remote sensing of sea surface salinity (SSS) (

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RFI and Remote Sensing of the Earth From Space

Cited by 10 publications

References 32 publications

Correction of Satellite Sea Surface Salinity Products Using Ensemble Learning Method

Correction of Satellite Sea Surface Salinity Products Using Ensemble Learning Method

Impact of river-discharged freshwater on surface ocean environments revealed by synergistic use of satellite measurements in the East China Sea

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

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