GlobColour CDR Meeting ESRIN July 2006 Merging Algorithm Sensitivity Analysis ACRI-ST/UoP

GlobColour CDR Meeting ESRIN July 2006 Content Review of the merging procedure –Averaging, weighted averaging procedure –Subjective analysis –Blended analysis –GSM01 algorithm –Optimal interpolation Example of merged images Method of the s ensitivity analysis Results Conclusion

GlobColour CDR Meeting ESRIN July 2006 Averaging, weight averaging procedure Advantages –Simple to implement –No source is considered better than another Disadvantage –Requires unbiased data sources If error bars of the data source can be characterized, a weight average can be implemented

GlobColour CDR Meeting ESRIN July 2006 Subjective analysis Information relevant to the quality of the sensors is used to develop a system weighting function, used during the merging Weighting functions represent variables that may determine the performance of a sensor: –Satellite zenith angle –Solar zenith angle –Sensor behaviour –Sun glint Advantage –Relies on scientific and engineering information Disadvantages –Difficult task that requires detailed information for each mission involved –Computationally demanding

GlobColour CDR Meeting ESRIN July 2006 Blended analysis Traditionally applied to merge satellite and in situ data Principle: –Assumes that in situ data are valid and uses these data to correct the final product Applied to merge multiple ocean colour data: –in situ data are replaced by data from one or more sensor established as superior (better characterisation, calibration, viewing conditions, …) Advantage: –can provide a bias correction –effective at eliminating biases if a "truth field" can be identified Disadvantage –the effectiveness of the bias-correction capability not well documented in satellite-satellite merging. –Can result in over correction

GlobColour CDR Meeting ESRIN July 2006 GSM01 algorithm A second order Gordon reflectance model (Gordon et. al., 1988) used with the optimized parameters (Maritorena et. al., 2002) In this equation, the absorption coefficient a( ) can be written as where a w ( ), a phyto ( ), a cdom ( ) are the spectral absorption coefficient of –pure water –phytoplankton cells –Colored dissolved organic material respectively Similarly, b b ( ) can be written as: where b bsw ( ), b bp ( ) are the –backscattering coefficient of pure seawater –backscattering coefficient of particulate matter

GlobColour CDR Meeting ESRIN July 2006 Among these five components: –a w ( ) and b bsw ( ) are known and constant – a phyto ( ), a cdom ( ) and b bp ( ) change as a function of Phytoplankton CDOM particulate matter They are modeled as: –a*phyto is the chlorophyll a specific absorption coefficient –[Chl] is the chlorophyll a concentration –acdom( 0 ) and bbp ( 0 ) are the CDOM absorption coefficient and particulate backscattering coefficient at the reference wavelength 0 –S is the spectral decay constant for CDOM absorption –  is the power law exponent for particulate backscattering coefficient GSM01 algorithm

GlobColour CDR Meeting ESRIN July 2006 Equation is therefore a function of three variables: –Chl a, a cdom ( 0 ), b bp ( 0 ). These three variables are retrieved by minimizing the mean square difference MSD: In this equation, Rrs_modelled refers to calculated remote sensing reflectance and Rrs_sat refers to the measured remote sensing reflectance. The MSD equation was solved using the nonlinear method. Chl a cdom ( 0 ) b bp ( 0 ) GSM01 algorithm

GlobColour CDR Meeting ESRIN July 2006 Advantage: –algorithm based on optical theory and not empirical relationships –Generate several products regardless of the number of data sources: Chl, a cdom ( 0 ), b bp ( 0 ) –Merging done implicitly during the inversion process –Completely different approach –When different sensors have the same set of spectral L wN ( ), data are used individually, without any averaging or other transformation Disadvantage –Errors associated with the parameterization and design of the model influence the quality of the merged product –Computationally demanding GSM01 algorithm

GlobColour CDR Meeting ESRIN July 2006 Optimal interpolation Principle: –weights are chosen to minimize the expected error variance of the analysed field –uses a statistical approach to define weights. –The weight matrix W represents the error correlations (error covariance matrix) Advantage –widespread use in data assimilation problems –objectivity in selecting the weights –Good at bias-correction Disadvantage –statistical interpretation of the merged data set, as opposed to a scientific evaluation. –computational complexity –very slow. –requires a good knowledge of data accuracy –shall be adapted from one region to the other (according to variogram that is the signature of the spatial correlation within each area) –dependent on a number of additional a priori information (e.g. as chlorophyll variability)

GlobColour CDR Meeting ESRIN July 2006 i j d Characterisation of the variance through semi-variogram (to quantify co-variability of information separated by a distance « d ») Spatial characterisation of natural variability: Elementary inputs for optimal interpolation and objective analysis

GlobColour CDR Meeting ESRIN July 2006

One orbit later

GlobColour CDR Meeting ESRIN July 2006 Large area – higher variability Small area – lower variability High fluctuations / regionalisation : use of sensitive a priori information

GlobColour CDR Meeting ESRIN July 2006 Indian ocean North sea Mediterranean Other illustrations

GlobColour CDR Meeting ESRIN July 2006 Results Global daily chlorophyll product from SeaWiFS, MODIS-A and MERIS % of sea pixels covered –11.20 % – 8.97 % – 4.82 % Initial daily images

GlobColour CDR Meeting ESRIN July 2006 Merged chlorophyll % of sea pixels covered –17.65%

GlobColour CDR Meeting ESRIN July 2006

Comparison between averaging and GSM01 algorithm

GlobColour CDR Meeting ESRIN July 2006 Comparison between averaging and GSM01 algorithm

GlobColour CDR Meeting ESRIN July 2006 Method of the s ensitivity analysis Sensitivity analysis on chlorophyll concentration retrieval for –GSM01 algorithm –averaging procedure based on global SeaWifs, MODISA and MERIS 9km standard map images results obtained on June 15 th 2003 as an example Adding noise to input parameters and evaluating the impact on the merged chlorophyll product Gaussian errors are introduced on the input parameters –on the nL w for the procedure using the GSM01 algorithm –on global chlorophyll products of individual sensors for the averaging technique Input products for the merging are used as available from each sensor: –no attempt was made to weight neither input chlorophyll nor input Normalized Water Leaving Radiances 10% 30% error when merging chlorophyll products 5 to 10% error with the GSM01algorithm + % error calculated by McClain + % error calculated in the characterisation section Presentation of the result for –30% error on Chl product –McClain and Characterisation error on nLw products

GlobColour CDR Meeting ESRIN July 2006 Sensitivity analysis averaging procedure

GlobColour CDR Meeting ESRIN July 2006 GSM01 algorithm McClain + Characterisation error

GlobColour CDR Meeting ESRIN July 2006 Sensitivity analysis GSM01 algorithm SeaWiFS Error MODISA Error MERIS Error All Errors

GlobColour CDR Meeting ESRIN July 2006 GSM01 algorithm Characterisation error

GlobColour CDR Meeting ESRIN July 2006 Sensitivity analysis GSM01 algorithm SeaWiFS Error MODISA Error MERIS Error All Errors

GlobColour CDR Meeting ESRIN July 2006 Conclusion The averaging procedure showed little sensitivity with up to 30% error The GSM01 algorithm showed little sensitivity to errors from McClain for SeaWiFS and MODIS-A. Despite the level of error introduced with the characterisation results, the chlorophyll output remained in good agreement with the initial calculations.

GlobColour CDR Meeting ESRIN July 2006