HOANG CONG TINHue University of Sciences

VIETNAM

PRIMARY PRODUCTION IN THE SARGASSO SEA:An Integration of Time Series In-Situ Data and

Ocean Color Remote Sensing Observations

PP estimated using satellite is closely related with values measured in the field under overcast sky (Kahru et al., 2009).

INTRODUCTION

The theory to calculate the PP from ocean color satellite images or in-situ data was developed (Platt, 1986).

Understanding the methods to calculate PP from remote sensing and field data using Bermuda Atlantic Time Series Study (BATS) as a case study.

Primary productivity (PP) is an extremely important component in the Earth’s biogeochemical cycle and related to other factors (Field et al., 1998).

DATA AND METHODS

* SeaWiFS satellite images L3 data (2004)Available at http://oceancolor.gsfc.nasa.gov/

1. Data and Materials

* SeaWiFS derived satellite time series Chl-a data at GiovanniAvailable at http://reason.gsfc.nasa.gov/Giovanni/

* Chl-a, Primary Production in-situ data from BATS Available at http://bats.bios.edu/

* NOAA Pathfinder ver 5.4km (24 pixels/degree) SSThttp://www.nodc.noaa.gov/SatelliteData/pathfinder4km

2. Methods

* Calculate and statistically analyze satellite data by using R, Matlab, MS. Excel software.

* Using SeaDAS software to analyze and process SeaWiFS images.

DATA AND METHODS

Nonlinear regression model (Gaussian) equation used to parameterize Chl-a profiles at BATS station

3. Study siteDATA AND METHODS

The map of Bermuda island and BATS stationSource: BIOS’ website

Located 75km Southeast of Bermuda at 31o50’N, 64o10W

Monthly sampling

CHL, SST, PAR CHL.a SST

PARPrimaryProduction

DATA AND METHODS

DATA AND METHODS

Collect in-situ data from BATS: Chl-a, PP

Process and analyze satellite imagery using

SeaDASCalculate parameters for

light transmission underwater from Chl-a

biomass profile (R software)

Estimate photosynthetic parameters (Platt et al.

1980)

Calculate PP in the water column

Collect satellite imagery (SeaWiFS): SST, Chl, PARThe flow chart for

calculate primary production from satellite image

Chlorophyll a concentration in Sargasso Sea

RESULTSMonthly-averaged maps of Chl-a distribution in the Sargasso Sea from SeaWiFS image

Bermuda

Satellite-derived Chl-a variation by year

The correlation between in-situ and satellite Chl-a

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

JAN FEB MAR APR MAY JUN JUL AUG OCT SEP OCT DEC

Months

mgC

m-3

Sea Surface Temperature in Sargasso Sea

RESULTSMonthly-averaged maps of SST distribution in the Sargasso Sea from Pathfinder-5.0 image

Bermuda

PAR in the Sargasso Sea

RESULTSMonthly-averaged maps of PAR in the Sargasso Sea from SeaWiFS\NASA server

Bermuda

JAN 2004 FEB 2004 MAR 2004 APR 2004

MAY 2004 JUN2004 JUL 2004 AUG 2004

SEP 2004 OCT 2004 NOV 2004 DEC 2004

RESULTS Chl-a in 2004

JAN 2004 FEB 2004 MAR 2004 APR 2004

MAY 2004 JUN2004 JUL 2004 AUG 2004

SEP 2004 OCT 2004 NOV 2004 DEC 2004

RESULTS SST in 2004

Chl-

conc

entr

ation

(mgC

m-2

)

Time

Temporal variability in Chlorophyll-a derived from satellite at BATS

RESULTS

?* Data analyzing* Missing data

Nonlinear regression model (Gaussian) equation used as a standard profile and fitted to Chl-a BATS profiles .

RESULTSThe vertical of chlorophyll biomass can be represented by a shifted Gaussian curve for which the parameters vary widely with regions and seasons .

(Platt & Sathyendranath, 1988, 1989; Platt et al. 1991)

Calculate B0, h, σ, zm from Chl-a in-situ vertical profile

Daily Production using a shifted Gaussian biomass profile

JAN 2004

CHLOROPHYLL VERTICAL PROFILE AT BATS

FEB 2004 APR 2004 MAY 2004

JUN 2004 JUL 2004 SEP 2004

B0 = 0h = 60.57 σ = 57.97zm = 53.51

B0 = 0h = 66.23 σ = 123.89zm = 18.68

JUN 2004 - FITTED JUL 2004 - FITTED AUG 2004 - FITTED

B0 = 0.04h = 14.58σ = 16.38zm = 93.29

AUG 2004

SEP 2004

CHLOROPHYLL VERTICAL PROFILE AT BATS

OCT 2004 NOV 2004 DEC 2004

DEC 2004 - FITTED

depth Lat Lon Day alphaB P_mB z_m B_0 h sigma Cloud Yelsub140 31 -61 30 0.016 1.53 53.5 0 60.57 57.97 70 0.03140 31 -61 60 0.016 1.53 18.7 0 66.23 123.9 50 0.03140 31 -61 90 0.016 1.53 18.7 0 66.23 123.9 40 0.03140 31 -61 120 0.016 1.53 40.4 0 54.32 86.14 20 0.03140 31 -61 150 0.016 1.53 41.7 0 34.45 51.26 30 0.03140 31 -61 180 0.016 1.53 93.3 0.05 14.58 16.38 40 0.03140 31 -61 210 0.016 1.53 17.4 0.14 15.03 20.43 55 0.03140 31 -61 240 0.016 1.53 20 0.03 15.48 31.12 50 0.03140 31 -61 270 0.016 1.53 97.4 0.04 16.38 21.47 60 0.03140 31 -61 300 0.016 1.53 26.5 0.08 16.05 7.03 75 0.03140 31 -61 330 0.016 1.53 78.4 0.02 15.72 56.39 70 0.03140 31 -61 360 0.016 1.53 83 0 12.46 45.8 70 0.03

αB & PmB after L. M. Lorenzo et al. (2004) for model inputs

In si

tu d

ata

calc

ulat

edm

gC/m

^3/d

ay

Days

Calculated PP from satellite and in-situ dataRESULTS

Using Lorenzo parameters

RESULTS

Platt, Trevor; Sathyendranath, Shubha; et al, Nutrient Control of Phytoplankton Photosynthesis in the Western North Atlantic. Nature; Mar 19, 1999; 6366; Research Library, pg.229

Prim

ary

Prod

uctio

n(m

gC m

-2 d

-1)

Months

Calculated In-situ

The comparison of primary production between remote sensing data and ship-board data at BATS

αB, PmB from L. M. Lorenzo et al. (2004)

αB =0.016Pm

B = 1.53

Prim

ary

Prod

uctio

n(m

gC m

-2 d

-1) Calculated

In-situ

Months

αB, PmB from Platt and Sathyendranath (1992)

αB = 0.087-0.136Pm

B =2.96 - 5.25

Prim

ary P

rodu

ction

(mgC

m-2

d-1

)

Months

(oC)

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12

SSTPAR

0.000

0.050

0.100

0.150

0.200

0.250

0.300

1 2 3 4 5 6 7 8 9 10 11 12

Chl_satChl_situ

Chl-a

conc

entr

ation

(m

gCm

-2)

Prim

ary p

rodu

ction

(mgC

m-2

d-1)

Months

Months1000

800

600

400

200

Phot

osyn

theti

callya

ctive

ra

diati

on a

nd SS

T (Em m-2 d-1)

(oC)

RESULTS Temporal variability in Chlorophyll-a derived from satellite and in-situ data at BATS

MAR 2004

MAR 2004

MAR 2004

Conclusions

• Surface chlorophyll-a in the Sargasso Sea shows distinct seasonal variation.

• Primary production in the Sargasso Sea exhibits seasonality: dominant feature is the Spring bloom.

• The model used to calculate PP needs to be refined and tested with additional field data.

+ Gained knowledge on ocean color remote sensing and primary production.

+ Used SeaDAS & R software to process satellite data

+ Applied methods to calculate Primary Production from remote sensing

* Analyzed satellite and in-situ data by R software * Running PP model using a vertical Chl-a profile

Lessons learned from the simultaneous analysis of field and satellite data

Acknowledgements

We would like to thanks Dr. Trevor Platt, Dr. Shubha Sathyendranath, Dr. George N. White, Dr. Heather , Dr. Li Zhai and Mr. Tom Jackson for their professional instructions.

Thank you

JAN 2004 NOV 2004

Prim

ary P

rodu

ction

(mgC

m-2

d-1

)

Months

(oC)

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12

SSTPAR

0.000

0.050

0.100

0.150

0.200

0.250

0.300

1 2 3 4 5 6 7 8 9 10 11 12

Chl_satChl_situ

Chl-a

con

cent

ratio

n (m

gC m

-2)

Prim

ary

prod

uctio

n(m

gC m

-2d-

1)

Months

Months1000

800

600

400

200

Phot

osyn

theti

cally

acti

ve

radi

ation

and

SST

(Em m-2 d-1)

(oC)

RESULTS The temporal variability of Chlorophyll-a derived from satellite and in situ data in BATS

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

1 2 3 4 5 6 7 8 9 10 11 12

SST

PAR

0.000

0.050

0.100

0.150

0.200

0.250

0.300

1 2 3 4 5 6 7 8 9 10 11 12

Chl_sat

Chl_situ

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

1 2 3 4 5 6 7 8 9 10 11 12

SST

PAR

0.000

0.050

0.100

0.150

0.200

0.250

0.300

1 2 3 4 5 6 7 8 9 10 11 12

Chl_sat

Chl_situ

Prim

ary P

rodu

ction

(mgC

m-2

d-1

)

Months

Months

Chl-a

con

cent

ratio

n (m

gC m

-2)

(oC)

Prim

ary

prod

uctio

n(m

gC m

-2d-

1)

Months

Months

(oC)

Phot

osyn

theti

cally

acti

ve

radi

ation

and

SST

Satellite Primary Production Model