Next Article in Journal
A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat
Previous Article in Journal
An Assessment of SEVIRI Imagery at Various Temporal Resolutions and the Effect on Accurate Dust Emission Mapping
Previous Article in Special Issue
A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery
Article Menu
Issue 8 (April-2) cover image

Export Article

Open AccessArticle

Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

1
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2
Key Laboratory of Software Engineering for Complex Systems, Changsha 410073, China
3
State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing 100101, China
4
The Hainan Key Laboratory for Earth Observation of Hainan Province, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 919; https://doi.org/10.3390/rs11080919
Received: 25 February 2019 / Revised: 27 March 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
  |  
PDF [8699 KB, uploaded 16 April 2019]
  |  

Abstract

Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation. View Full-Text
Keywords: SMOS; SSS preprocessing; Generalized Regression Neural Network (GRNN); data assimilation; subsurface salinity SMOS; SSS preprocessing; Generalized Regression Neural Network (GRNN); data assimilation; subsurface salinity
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mu, Z.; Zhang, W.; Wang, P.; Wang, H.; Yang, X. Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea. Remote Sens. 2019, 11, 919.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top