Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter

Denis Dreano, Bani Mallick, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalSpatial Statistics
Volume13
DOIs
StatePublished - Apr 27 2015

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Statistics and Probability
  • Management, Monitoring, Policy and Law

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