Chlorophyll a algorithms for MODIS and MERIS full...
Transcript of Chlorophyll a algorithms for MODIS and MERIS full...
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
ENVIRONMENTAL CHARACTERIZATION
Chlorophyll_a is a key element to assess the sea ecological status
(WFD, MS descriptor 5 - eutrophication )
http://www.mo-mar.net/
OCEANOGRAPHIC CAMPAIGNS
NURC
Ifremer
University of Florence Ecologia Vegetale
True Color MODIS 17 March 2009
In situ HPLC Chlorophyll_a data:
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)
BP09 14 – 23
March 2009 33 1.011 0.712 0.396 2.837
MOMAR 19 – 22
July 2010 9 0.126 0.076 0.045 0.311
REP10
19 August -
3
September
2010
21 0.298 0.536 0.050 1.899
Main features and relevant [CHL] statistics of the three oceanographic
campaigns
MODIS AQUA – TERRA NASA MODerate Resolution
Imaging Spectro-radiometer
Spatial resolution: 1km
(FUTURE: VIIRS NPP)
MERIS Envisat ESA Medium Resolution Imaging Spectro-Radiometer
Full resolution: 0.3m
(FUTURE: OLCI Sentinel 3)
ENVISAT
European Space Agency
EOS Terra
Chlorophyll
CDOM
SPM ALGORITHMS
SATELLITES
Case 1 Case 2
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.
2000-206892, 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, 103-116, Springer Netherlands, 2008.
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, 1639–1661.
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. 595–605.
ALGORITHMS
OC5 LUT taking into consideration MBR, corrects for
CDOM (Rrs412) and SPM (Rrs555).
SAM_LT inversion algorithm which identifies simulated Rrs
which best corresponds to measured spectral data.
EMPIRICAL ALGORITHMS
are based on direct regression of the ratio of reflectances at two
wavelengths - BLUE/GREEN - to chlorophyll concentration. The remote-
sensing reflectance can be assumed as proportional to bb/a; so if the band
ratio is taken, the influence of scattering is weakened.
OC3M, MedOC3 MBR=(Rrs443>Rrs488)/Rrs547
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=βbb(λ )/[a(λ)+bb(λ)]
17 March 2009
MODIS AQUA Chlorophyll_a mg/m3
MODIS AQUA
OC3M
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed
[C
HL]
(m
g m
-3)
BP09
MOMAR
Rep10
R2 = 0.7671
RMSE = 1.22 mg/m3
MBE = 0.36 mg/m3
MedOC3
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed
[C
HL]
(m
g m
-3)
BP09
MOMAR
Rep10
R2 = 0.795
RMSE = 2.91 mg/m3
MBE = 0.91 mg/m3
SAM_LT
0,01
0,10
1,00
10,00
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed
[C
HL]
(m
g m
-3)
BP09
MOMAR
Rep10
R2 = 0.4333
RMSE = 0.75 mg/m3 MBE = -0.22
63 matchups
OC5
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed
[C
HL]
(m
g m
-3)
BP09MOMARRep10
R2 = 0.7414
RMSE = 0.74 mg/m3
MBE = 0.08 mg/m3
Lee and Hu (2006) Case 1
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
HPLC OC3M MedOC3 OC5 SAM
[CH
L] (
mg
m-3
)
Lee and Hu (2006) Case 2
0,00
0,50
1,00
1,50
2,00
2,50
3,00
3,50
HPLC OC3M MedOC3 OC5 SAM
[CH
L] (
mg
m-3
)
Lee Z P, Hu C (2006) Global distribution of Case-1 waters: an analysis from SeaWiFS
measurements. Remote Sensing of Environment 101 270 – 276.
Optical relations typical of Case 1
waters
Chl_a - CDOM
Chla_a - SPM
15 Case 1
31 Case 2
MERIS FR and MODIS AQUA Chlorophyll_a mg/m3
17 March 2009
22 matchups
MODIS AQUA vs MERIS OC5
0,01
0,10
1,00
10,00
0,01 0,10 1,00 10,00
MERIS [CHL] (mg m-3
)
MO
DIS
[C
HL
] (m
g m
-3)
R2 = 0.7708
22 matchups
MERIS FR MODIS
OC5
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed
[C
HL]
(m
g m
-3)
BP09
MOMAR
Rep10
R2 = 0.5826
RMSE = 1.23 mg/m3
MBE = 0.32 mg/m3
OC5
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed [C
HL]
(mg
m-3
)
BP09MOMARRep10
R2 = 0.7639
RMSE = 0.95 mg/m3
MBE = 0.23 mg/m3
MERIS OC5
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Est
ima
ted
[C
HL]
(m
g m
-3)
BP09
MOMAR
Rep10
R2 = 0.7695
RMSE = 0.50 mg/m3
MBE = -0.10 mg/m3
Arno river plume strong
Chl_a oversetimation by
OC5
MODIS OC5
0,01
0,1
1
10
0,01 0,1 1 10Measured [CHL] (mg m-3)
Esti
mat
ed
[C
HL]
(m
g m
-3)
BP09MOMARRep10
R2 = 0.7869
RMSE = 0.38 mg/m3
MBE = -0.09 mg/m3if we get rid of those stations
OC5 MODIS chlorophyll_a
8-day average
http://www.lamma.rete.toscana.it/en/node/3431
• 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.
Conclusions
Thank you