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

Mathematics

• Statistical Model 100%
• Kalman Filtering 100%
• Covariance Model 100%
• Orthogonal Function 100%
• Prediction Error 50%
• Kriging 50%
• Positive Definite 50%
• Mean Square 50%
• Time Domain 50%
• Stationary Gaussian Process 50%

Earth and Planetary Sciences

• Kalman Filter 100%
• Covariance 100%
• Empirical Orthogonal Function Analysis 100%
• Interpolation 50%
• Kriging 50%

Keyphrases

• Space-time Covariance Model 100%
• Empirical Orthogonal Functions 50%
• Positive Definite 25%
• Mean Squared Prediction Error 25%
• Stationary Gaussian Process 25%
• Reduced-order Kalman Filter 25%
• Kriging 25%
• Multi-step Prediction 25%
• Data Interpolation 25%
• Root Mean Square 25%