Next Article in Journal
Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity
Previous Article in Journal
Spectral Indices Accurately Quantify Changes in Seedling Physiology Following Fire: Towards Mechanistic Assessments of Post-Fire Carbon Cycling
Article Menu

Export Article

Open AccessTechnical Note
Remote Sens. 2016, 8(7), 576; doi:10.3390/rs8070576

A Conceptually Simple Modeling Approach for Jason-1 Sea State Bias Correction Based on 3 Parameters Exclusively Derived from Altimetric Information

1
Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, Porto 4169-007, Portugal
2
National Oceanography Centre, Natural Environment Research Council, Southampton SO14 3ZH, UK
3
European Organisation for the Exploitation of Meteorological Satellites, Darmstadt D-64295, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Raphael M. Kudela, Xiaofeng Li and Prasad Thenkabail
Received: 29 April 2016 / Revised: 26 June 2016 / Accepted: 4 July 2016 / Published: 8 July 2016
View Full-Text   |   Download PDF [2262 KB, uploaded 8 July 2016]   |  

Abstract

A conceptually simple formulation is proposed for a new empirical sea state bias (SSB) model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH) and wind speed (U10), a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA) variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions. View Full-Text
Keywords: satellite altimetry; sea state bias; mean wave period; nonparametric estimation; GAM satellite altimetry; sea state bias; mean wave period; nonparametric estimation; GAM
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pires, N.; Fernandes, M.J.; Gommenginger, C.; Scharroo, R. A Conceptually Simple Modeling Approach for Jason-1 Sea State Bias Correction Based on 3 Parameters Exclusively Derived from Altimetric Information. Remote Sens. 2016, 8, 576.

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