Next Article in Journal
Detecting Neolithic Burial Mounds from LiDAR-Derived Elevation Data Using a Multi-Scale Approach and Machine Learning Techniques
Next Article in Special Issue
Optimal Estimation of Sea Surface Temperature from AMSR-E
Previous Article in Journal
Validation of Carbon Monoxide Total Column Retrievals from SCIAMACHY Observations with NDACC/TCCON Ground-Based Measurements
Previous Article in Special Issue
The Accuracies of Himawari-8 and MTSAT-2 Sea-Surface Temperatures in the Tropical Western Pacific Ocean
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessFeature PaperArticle

Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures

Météo-France/Centre de Météorologie Spatiale, Avenue de Lorraine, B.P. 50747, 22307 Lannion CEDEX, France
IMT Atlantique, Lab-STICC, UBL, 29238 Brest, France
Ifremer, Laboratoire d’Océanographie Physique et Spatiale, ZI Pointe du Diable CS 10070, 29280 Plouzané, France
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 224;
Received: 20 November 2017 / Revised: 19 January 2018 / Accepted: 27 January 2018 / Published: 1 February 2018
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
PDF [2815 KB, uploaded 1 February 2018]


Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for which a large number of collocations is available, results show that the random forest model is the best model to predict the systematic errors and it is computationally fast, making it a good candidate for operational processing. It is able to explain nearly 31% of the total variance of the bias (in comparison to about 24% for the multi-linear regression model). View Full-Text
Keywords: machine learning; systematic error; sea surface temperature; random forest machine learning; systematic error; sea surface temperature; random forest

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).
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

Saux Picart, S.; Tandeo, P.; Autret, E.; Gausset, B. Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures. Remote Sens. 2018, 10, 224.

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