Chlorophyll_a algorithms for MODIS and MERIS full resolution imagery: an analysis of Ligurian and North Tyrrhenian waters

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1 Chlorophyll_a algorithms for MODIS and MERIS full resolution imagery: an analysis of Ligurian and North Tyrrhenian waters Chiara Lapucci Gohin F., Ampolo Rella M., Brandini C., Gozzini B., Maselli F., Massi L., Nuccio C., Ortolani A., and Trees C. MELBA MILONGA

2 ENVIRONMENTAL CHARACTERIZATION Chlorophyll_a is a key element to assess the sea ecological status (WFD, MS descriptor 5 - eutrophication )

3 OCEANOGRAPHIC CAMPAIGNS True Color MODIS 17 March 2009 In situ HPLC Chlorophyll_a data: NURC Ifremer University of Florence Ecologia Vegetale

4 Campaign Period Number of stations (matchup) [Chl_a] Average (mg m -3 ) [Chl_a] Standard deviation (mg m -3 ) [Chl_a] minimum (mg m -3 ) [Chl_a] maximum (mg m -3 ) March July REP10 19 August - 3 September Main features and relevant [CHL] statistics of the three oceanographic campaigns

5 SATELLITES MODIS AQUA TERRA NASA MODerate Resolution Imaging Spectro-radiometer Spatial resolution: 1km (FUTURE: VIIRS NPP) EOS Terra MERIS Envisat ESA Medium Resolution Imaging Spectro-Radiometer Full resolution: 0.3m (FUTURE: OLCI Sentinel 3) ENVISAT European Space Agency ALGORITHMS Chlorophyll CDOM SPM Case 1 Case 2

6 ALGORITHMS OC3M Bio optical empirical MODIS global algorithm, Case 1 waters. O'Reilly, J.E., and 24 Coauthors, 2000: SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. NASA Tech. Memo , Vol. 11, S.B. Hooker e E.R. Firestone, Ed. NASA Goddard Space Flight Center, 49 pp. MedOC3 Bio optical empirical MODIS algorithm, OC3 regionally adapted on North Western Mediterranean, Case 1 waters. Santoleri R., Volpe G., Marullo S., Buongiorno Nardelli B., "Open waters optical remote sensing of the Mediterranean Seas, Remote sensing of the European Seas, , Springer Netherlands, OC5 Bio optical empirical MODIS (MERIS and SeaWIFS) algorithm, suited for Biscay Bay and the English Channel, Case 1 and Case 2 waters. Gohin F., Druon J. N., Lampert L., A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal watersint. J. Remote Sensing, 2002, vol. 23, no. 8, SAM-LT Bio optical semi analytical MODIS algorithm, locally tuned for Tyrrhenian - Ligurian sea, Case 2 waters. Maselli F., Massi L., Pieri M., and Santini C., Spectral Angle Minimization for the Retrieval of Optically Active Seawater Constituents from MODIS Data Photogrammetric Engineering & Remote Sensing, Vol. 75, No. 5, May 2009, pp

7 EMPIRICAL ALGORITHMS are based on direct regression of the ratio of reflectances at two wavelengths - BLUE/GREEN - to chlorophyll concentration. The remotesensing reflectance can be assumed as proportional to b b /a; so if the band ratio is taken, the influence of scattering is weakened. OC3M, MedOC3 OC5 MBR=(Rrs443>Rrs488)/Rrs547 LUT taking into consideration MBR, corrects for CDOM (Rrs412) and SPM (Rrs555). SEMI ANALYTICAL ALGORITHMS Radiative transfer theory provides a relationship between upweling radiance and the inherent optical properties of water (Sathyerdranath et al., 1997). Rrs is function of the inherent optical properties of sea water. Rrs=βb b (λ )/[a(λ)+b b (λ)] SAM_LT inversion algorithm which identifies simulated Rrs which best corresponds to measured spectral data.

8 MODIS AQUA Chlorophyll_a mg/m 3 17 March 2009

9 Estimated [CHL] (mg m -3 ) Estimated [CHL] (mg m -3 ) Estimated [CHL] (mg m -3 ) Estimated [CHL] (mg m -3 ) MODIS AQUA 10 Rep10 OC3M 10 Rep10 MedOC ,1 R 2 = RMSE = 1.22 mg/m 3 MBE = 0.36 mg/m 3 0,1 R 2 = RMSE = 2.91 mg/m 3 MBE = 0.91 mg/m 3 0, Measured [CHL] (mg m -3 ) 0, Measured [CHL] (mg m -3 ) 10 Rep10 OC5 10,00 Rep10 SAM_LT 1 1,00 0,1 R 2 = RMSE = 0.74 mg/m 3 MBE = 0.08 mg/m 3 0, Measured [CHL] (mg m -3 ) 0,10 R 2 = RMSE = 0.75 mg/m 3 MBE = , Measured [CHL] (mg m -3 ) 63 matchups

10 [CHL] (mg m -3 ) [CHL] (mg m -3 ) 0,70 Lee and Hu (2006) Case 1 0,60 15 Case 1 31 Case 2 0,50 0,40 0,30 0,20 0,10 0,00 HPLC OC3M MedOC3 OC5 SAM 3,50 Lee and Hu (2006) Case 2 3,00 2,50 2,00 1,50 1,00 0,50 Optical relations typical of Case 1 waters Chl_a - CDOM Chla_a - SPM 0,00 HPLC OC3M MedOC3 OC5 SAM Lee Z P, Hu C (2006) Global distribution of Case-1 waters: an analysis from SeaWiFS measurements. Remote Sensing of Environment

11 MERIS FR and MODIS AQUA Chlorophyll_a mg/m 3 17 March matchups

12 MODIS [CHL] (mg m -3 ) MODIS AQUA vs MERIS OC5 10,00 1,00 0,10 R 2 = ,10 1,00 10,00 MERIS [CHL] (mg m -3 ) 22 matchups

13 Estimated [CHL] (mg m -3 ) Estimated [CHL] (mg m -3 ) MERIS FR MODIS 10 Rep10 OC5 10 Rep10 OC ,1 0,1 R 2 = RMSE = 1.23 mg/m 3 MBE = 0.32 mg/m 3 R 2 = RMSE = 0.95 mg/m 3 MBE = 0.23 mg/m 3 0, Measured [CHL] (mg m -3 ) 0, Measured [CHL] (mg m -3 )

Estimated [CHL] (mg m -3 ) Estimated [CHL] (mg m -3 ) 10 Rep10 MERIS OC5 1 Arno river plume strong Chl_a oversetimation by OC5 0,1 R 2 = 0.7695 RMSE = 0.50 mg/m 3 MBE = -0.

14 Estimated [CHL] (mg m -3 ) Estimated [CHL] (mg m -3 ) 10 Rep10 MERIS OC5 1 Arno river plume strong Chl_a oversetimation by OC5 0,1 R 2 = RMSE = 0.50 mg/m 3 MBE = mg/m 3 0, Measured [CHL] (mg m -3 ) 10 Rep10 MODIS OC5 1 0,1 if we get rid of those stations R 2 = RMSE = 0.38 mg/m 3 MBE = mg/m 3 0, Measured [CHL] (mg m -3 )

15 OC5 MODIS chlorophyll_a 8-day average

16 Conclusions The current analysis confirms the overestimation of conventional MODIS algorithms (OC3M and MedOC3) SAM_LT is poorly sensitive to low chlorophyll concentrations, while it performs better than the others on critical pixels, such as on river plumes OC5 overall shows the better performances, but has a tendency to the overestimation A larger dataset of in situ and satellite observations is needed.

17 Thank you

Robert Arnone, Hubert Loisel, Kendall Carder, Emmanuel Boss, Stephane Maritorena and ZhongPing Lee

Chapter 13 Examples of IOP Applications Robert Arnone, Hubert Loisel, Kendall Carder, Emmanuel Boss, Stephane Maritorena and ZhongPing Lee The IOPs retrieved from ocean colour provide innovative tools