Next Article in Journal
Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China
Previous Article in Journal
Local Azimuth Ambiguity-to-Signal Ratio Estimation Method Based on the Doppler Power Spectrum in SAR Images
Previous Article in Special Issue
Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis
Article Menu
Issue 7 (April-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(7), 858;

Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

IGE, Université Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, France
IMT Atlantique, Lab-STICC UMR CNRS 6285, UBL, 29200 Brest, France
Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin 300171, China
Department of Marine Information Technology, Ocean University of China, Qingdao 266100, China
Laboratoire d’Océanographie Physique et Spatiale, IFREMER, 29200 Brest, France
Author to whom correspondence should be addressed.
Received: 21 January 2019 / Revised: 4 April 2019 / Accepted: 4 April 2019 / Published: 9 April 2019
(This article belongs to the Special Issue Assimilation of Remote Sensing Data into Earth System Models)
PDF [5023 KB, uploaded 10 April 2019]


From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation. View Full-Text
Keywords: analog data assimilation; sea level anomaly; sea surface height; interpolation; data-driven methods analog data assimilation; sea level anomaly; sea surface height; interpolation; data-driven methods

Graphical abstract

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).

Share & Cite This Article

MDPI and ACS Style

Lguensat, R.; Viet, P.H.; Sun, M.; Chen, G.; Fenglin, T.; Chapron, B.; Fablet, R. Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation. Remote Sens. 2019, 11, 858.

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



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