Global Distribution of the Random Uncertainty Associated With Satellite-Derived Chl a

The comparison of coincident daily records of the concentration of chlorophyll a (Chla) derived from the Sea-viewing Wide Field-of-view Sensor and the Moderate Resolution Imaging Spectroradiometer can provide the distribution of the standard deviation ¿ associated with the zero-mean random component of the uncertainty budget. The proposed approach does not quantify the bias characterizing the Chla satellite product, but it is spatially and temporally resolved and thus appears as a useful complement to validation exercises with in situ data. The global median of ¿ is equal to 0.074 for log-transformed Chla. The values of ¿ that are around or larger than 0.1 are found in a significant part of the ocean, including the centers of the subtropical gyres, some Arctic and Antarctic areas, or eastern boundary upwelling regions. In general, ¿ increases at the low and high ends of the Chla range, whereas it is lowest in the interval 0.1-0.3 mg ·m-3.

[1]  C. McClain,et al.  The calibration and validation of SeaWiFS data , 2000 .

[2]  Louis Legendre,et al.  The Importance of Being Digital , 1963 .

[3]  S. Franke,et al.  Method for statistical comparison of geophysical data by multiple instruments which have differing accuracies , 2001 .

[4]  Peter J. Minnett,et al.  An overview of MODIS capabilities for ocean science observations , 1998, IEEE Trans. Geosci. Remote. Sens..

[5]  F. Muller‐Karger,et al.  Bridging between SeaWiFS and MODIS for continuity of chlorophyll-a concentration assessments off Southeastern China , 2006 .

[6]  W. Gregg,et al.  Global and regional evaluation of the SeaWiFS chlorophyll data set , 2004 .

[7]  G. Zibordi,et al.  Optically based technique for producing merged spectra of water-leaving radiances from ocean color remote sensing. , 2007, Applied optics.

[8]  M. Darecki,et al.  SeaWiFS ocean colour chlorophyll algorithms for the southern Baltic Sea , 2005 .

[9]  A. Longhurst Ecological Geography of the Sea , 1998 .

[10]  B. Franz,et al.  Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry. , 2007, Applied optics.

[11]  Gilles Larnicol,et al.  Merging SeaWiFS and MODIS/Aqua Ocean Color Data in North and Equatorial Atlantic Using Weighted Averaging and Objective Analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  F. D’Ortenzio,et al.  The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms evaluation and implication for satellite chlorophyll estimates , 2007 .

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

[14]  Stanford B. Hooker,et al.  An overview of the SeaWiFS project and strategies for producing a climate research quality global ocean bio-optical time series , 2004 .

[15]  M. Toohey,et al.  Estimating biases and error variances through the comparison of coincident satellite measurements , 2007 .

[16]  S. Hooker An overview of SeaWiFS and ocean color , 1992 .

[17]  Giuseppe Zibordi,et al.  Assessment of satellite ocean color products at a coastal site , 2007 .

[18]  Lars Nerger,et al.  Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK filter , 2007 .

[19]  Janet W. Campbell,et al.  Ocean surface layer drift revealed by satellite data , 2002 .