Evaluation of MODIS chlorophyll algorithms in

Evaluation of MODIS chlorophyll algorithms in Australian continental shelf waters: The IMOS match up data base Schroeder T, Lovell J,, Clementson L, King E, Brando V 9 July 2014 Australian Marine Science Association Conference, Canberra, Australia

Motivation No consistent national validation approach

Motivation No coherent algorithm validation on continental scale Global algorithms are biased towards northern hemisphere data

The Australian bio optical data desert

IMOS data updates to NASA SeaBASS (since Jan 2014) http://seabass.gsfc.nasa.gov/wiki

In situ HPLC chlorophyll IMOS data base N=1992 http://imos.aodn.org.au/webportal/ p// / p /

Motivation No coherent algorithm validation on continental scale Global algorithms are biased towards northern hemisphere data IMOS Ocean Colour Sub facility is addressing data gap Compilation of a national bio optical data base Production and provision of Ocean Colour data

L2 re mapped d swath h https://rs.nci.org.au/u83/public/data/modis/l2.oc.70/aqua/ L2 continental scale mosaics http://thredds0.nci.org.au/thredds/catalog/u83/modis/oc.mosaics.70/catalog.html In future also via http://imos.aodn.org.au/webportal/

Which chlorophyll product to choose? IMOS MODIS A repository based on SeaDAS v7.0 processing Empirical algorithms OC3 (MODIS standard algorithm) Clark Semi analytical algorithms GSM (Garver Siegel Maritorena model) Carder

IMOS in situ HPLC data coverage Time constrain to satellite overpasses ±1 day All HPLC data (N=1992) ±1 day (N=262)

IMOS in situ HPLC data coverage Time constrain to satellite overpasses p ±2 hours All HPLC data (N=1992) ±1 day (N=262) ±2 hours (N=34)

Data distribution NOMAD vs IMOS

Match up Methodology For each in situ Chl a data from IMOS data base identify matching satellite images within ±2 hours upto ±1 day Extract satellite derived CHL observations at in situ locations within a 5 km radius for open ocean waters, 3 km for coastal waters, calculate median and standard deviation Apply quality control flags Land, cloud, glint, Sun zenith < 60, view zenith < 60,... Calculate statistics (RMSE, % error, bias) of match up pairs Classify match up pairs according to Optical Water Type (OWT) 8 classes (Moore et al. 2009) Extra class for cocolithophore bloom

Optical Water Type (OWT) classification Moore et al. 2009

OC3 MODIS A) Satellite CHL (M In situ CHL

N=60 N=264 ±3 h ±1 day

Match up Statistics ±1 Day OC3 Clark Carder GSM N 262 262 193 262 RMSE 0.25 0.25 0.34 0.31 MAPE 50 50 42 79 Bias 0.03 003 0.02 002 0.23 023 008 0.08 ±3 hr OC3 Clark Carder GSM N 57 57 40 57 RMSE 0.20 0.20 0.35 0.25 MAPE 29 32 46 56 Bias 0.08 0.05 0.28 0.06

Conclusions IMOS OC sub facility provides a useful platform for nationally consistent evaluation of ocean colour products All data freely available through the IMOS portal Ongoing effort (currently secured until 06/2015) Optical Water Types vital to constrain match up analysis exclude out of range conditions larger deviations OWT 7,8,9 In situ HPLC does not confirm presence of cocolithophore (OWT9) Reducing time window for match ups improves statistics (except Carder) Empirical algorithms perform better than semi analytical Performance ranking: OC3, Clark, Carder, GSM

The IMOS bio optical data base needs your data! The IMOS bio optical data base needs your data! It is a community effort.

Thank you question? Dr Thomas Schroeder, Brisbane Thomas.Schroeder@csiro.au Acknowledgements: SeaDAS Development Group and the OPBG at NASA GSFC for development, support and distribution of the SeaDAS software