A comparison of Multiple Non-linear regression and neural network techniques for sea surface salinity estimation in the tropical Atlantic ocean based on satellite data

Moussa, Hadger; Benallal, Mohamed Anis; Goyet, Catherine; Lefèvre, Nathalie; Jai, M. C. El; Guglielmi, Véronique; Touratier, Franck

doi:10.1051/proc/201549006

Cited by 11 publications

(4 citation statements)

References 4 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This toolbox performs multiple linear regressions between these three variables. This method provides good results for our purpose (Moussa et al 2015). We carried out the analysis for Part 1 of the database (2002–09), setting the degree of φ at 1 and the degree of SST at 2, resulting in the following function: where the optimized values of coefficients were a= 33.10000, b= -0.01257, c= 0.103600, d= 0.001929 and e= 0.008504.…”

Section: Methodsmentioning

confidence: 96%

Ocean salinity from satellite-derived temperature in the Antarctic Ocean

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 96%

Ocean salinity from satellite-derived temperature in the Antarctic Ocean

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…F r can be calculated using the following equation [33]: 55 10 exp(0.0861∆T) (20) where U 10 is the wind speed at a 10 m altitude, and ∆T = SST − T 0 is the sea air temperature difference.…”

Section: Removing the Effects Of Sea Surface Foam And Flat Sea Surfac...mentioning

confidence: 99%

The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

Wen,

Zhang,

Shu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.

show abstract

“…To improve the accuracy of satellite SSS measurements, several machine learning algorithms have been developed [28], [29]. These algorithms employ techniques such as random forests and support vector regressions to estimate SSS [30].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

2022

View full text Add to dashboard Cite

Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimatessuch as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI timeseries measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.

show abstract

A comparison of Multiple Non-linear regression and neural network techniques for sea surface salinity estimation in the tropical Atlantic ocean based on satellite data

Cited by 11 publications

References 4 publications

Ocean salinity from satellite-derived temperature in the Antarctic Ocean

Ocean salinity from satellite-derived temperature in the Antarctic Ocean

The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

Contact Info

Product

Resources

About