Synergy between Ocean Variables: Remotely Sensed Surface Temperature and Chlorophyll Concentration Coherence

Special Issue Ten Years of Remote Sensing at Barcelona Expert Center.-- 13 pages, 5 figures, 1 table

[1]  J. E. Valdez-Holguin,et al.  Primary Productivity In The Gulf Of California Effects Of El Niño 1982-1983 Event , 1987 .

[2]  K. Denman,et al.  Time scales of pattern evolution from cross‐spectrum analysis of advanced very high resolution radiometer and coastal zone color scanner imagery , 1994 .

[3]  Janet W. Campbell,et al.  The lognormal distribution as a model for bio‐optical variability in the sea , 1995 .

[4]  Antonio Turiel,et al.  The Multifractal Structure of Contrast Changes in Natural Images: From Sharp Edges to Textures , 2000, Neural Computation.

[5]  M. Fuentes,et al.  Mesoscale variability of Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) satellite ocean color: Global patterns and spatial scales , 2003 .

[6]  Thomas M. Smith,et al.  Improved Extended Reconstruction of SST (1854–1997) , 2004 .

[7]  Antonio Turiel,et al.  Common turbulent signature in sea surface temperature and chlorophyll maps , 2007 .

[8]  J. Beckers,et al.  Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields , 2007 .

[9]  William J. Emery,et al.  Computing Coastal Ocean Surface Currents From Infrared and Ocean Color Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Antonio Turiel,et al.  Microcanonical multifractal formalism: Application to the estimation of ocean surface velocities , 2007 .

[11]  Antonio Turiel,et al.  Microcanonical multifractal formalism—a geometrical approach to multifractal systems: Part I. Singularity analysis , 2008 .

[12]  Antonio Turiel,et al.  The multifractal structure of satellite sea surface temperature maps can be used to obtain global maps of streamlines , 2009 .

[13]  Jean-Marie Beckers,et al.  Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology , 2011 .

[14]  Bruno Buongiorno Nardelli,et al.  A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity , 2012 .

[15]  Sylvie Thiria,et al.  Reconstruction of satellite chlorophyll images under heavy cloud coverage using a neural classification method , 2013 .

[16]  Joaquim Ballabrera-Poy,et al.  New blending algorithm to synergize ocean variables: The case of SMOS sea surface salinity maps , 2014 .

[17]  Joaquim Ballabrera-Poy,et al.  Improving time and space resolution of SMOS salinity maps using multifractal fusion , 2016 .

[18]  Redouane Lguensat,et al.  The Analog Data Assimilation , 2017 .

[19]  Joaquim Ballabrera-Poy,et al.  Singularity Power Spectra: A Method to Assess Geophysical Consistency of Gridded Products—Application to Sea-Surface Salinity Remote Sensing Maps , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Pierre-Henri Horrein,et al.  Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation , 2018, Remote. Sens..