satellite ocean color remote sensing and its applications ......menghua wang, noaa/nesdis/star...

Menghua Wang, NOAA/NESDIS/STAR

Satellite Ocean Color Remote Sensing and Its Applications for Marine Air Quality Modeling

Menghua Wang1, Daniel Tong2, Xiaoming Liu1, & Kent Hughes1

1NOAA/NESDIS Center for Satellite Applications and Research (STAR) 5200 Auth Road, Camp Springs, MD 20746, USA

2NOAA Air Resources Laboratory (ARL) Silver Spring, MD 20910

The 3rd International Workshop on Air Quality Forecasting Research Bolger Center, Potomac, Maryland, November 29-December 1, 2011

QuickTime™ and aTIFF (Uncompressed) decompressor

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Menghua Wang, NOAA/NESDIS/STAR Chesapeake Bay Lake Taihu 2

Ocean Color Spectra


Sensor-Measured

Blue ocean

“Green” ocean

From H. Gordon

Atmospheric Correction (removing >90% sensor-measured signals) Calibration (0.5% error in TOA >>>> 5% in surface) 3

Ocean Color Remote Sensing


At satellite altitude ~90% of sensor-measured signal over ocean

comes from the atmosphere & surface!

• It is crucial to have accurate atmospheric correction and sensor calibrations.

• 0.5% error in atmospheric correction or calibration corresponds to possible of ~5% error in the derived ocean water-leaving radiance.

• We need ~0.1% sensor calibration accuracy.

4

SeaWiFS (1997-2010): Sea-Viewing Wide Field-of-View Sensor MODIS (1999-, 2002-present): Moderate Resolution Imaging Spectroradiometer


Ocean Color Remote Sensing: Derive the ocean water-leaving radiance spectra by accurately removing the atmospheric and surface effects.

Ocean properties: e.g., chlorophyll-a concentration, diffuse attenuation coefficient at 490 nm, can be derived from the ocean water-leaving radiance spectra.

5


nLw(443) Scale: 0.-3.0

(mW/cm2 µm sr)

Wang, M., S. Son, and W. Shi (2009), “Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data,” Remote Sens. Environ., 113, 635-644.

July, 2005 NIR-SWIR Data

Processing

July, 2005 NIR-SWIR Data

Processing

Chlorophyll-a 0.01-10 (mg/m3)

(Log scale)


SeaWiFS experiences demonstrate that the atmospheric correction works well in the open oceans.

0

1

2

3

4

0 1 2 3 4

412 nm443 nm490 nm510 nm555 nm

[Lw(λ

)] N (S

eaW

iFS

)

[Lw(λ)]

N (In situ Data)

[Lw(λ)]N Unit: (mW/cm2 µm sr)Slope = 0.9924, Intercept = 0.0536, R = 0.9478

555 nm

510 nm

7 Water-leaving Radiance


SeaWiFS Chlorophyll-a Concentration (October 1997-December 2003)

8


0.01

0.1

1

10

100

0.01 0.1 1 10 100

Chl

-a (S

eaW

iFS

)

Chl-a (In Situ Data)

Chlorophyll-a Unit: (mg/m 3)Slope = 0.9357, Intercept = 0.0129, R = 0.9225

SeaWiFS Chlorophyll-a Comparison

9


Global Kd(490) (Turbidity) Seasonal Maps (2005)

(Shi and Wang, 2009)


Validation Results for MODIS-derived Kd(490)

0.01

0.1

1

10

0.01 0.1 1 10

MO

DIS

Kd(

490)

(m-1

)

In Situ Kd(490) (m-1)

New ApproachNOMAD In Situ DataMODIS SWIR Derived R(667) for K d(490)

1:1 Mueller (2000) Model

Blended Model

New Model

(a)


0.01

0.1

1

10

0.01 0.1 1 10

MO

DIS

nLw

(λ)

(mW

cm-2

µm

-1 s

r-1)

In Situ nLw(λ) (mW cm-2 µm-1 sr-1)

Chesapeake Bay Regionλ = 412 nm

(a)Mean Ratio = 1.29

0.01

0.1

1

10

0.01 0.1 1 10



(b)Mean Ratio = 1.09

0.01

0.1

1

10

0.01 0.1 1 10

MO

DIS

nLw

(λ)

(mW

cm-2

µm

-1 s

r-1)



(c)Mean Ratio = 1.00

0.01

0.1

1

10

0.01 0.1 1 10



(d)Mean Ratio = 0.95

Results from MODIS-Aqua Measurements

(Chesapeake Bay)

1

10

100

1 10 100

MO

DIS

TS

S (m

g l-1

)

In Situ TSS (mg l-1)

(b)Number of Data = 647

Mean Ratio = 1.06Median Ratio = 0.97STD = 0.48


Results from MODIS-Aqua Measurements (China East Coast)

10-3

10-2

10-1

100

400 500 600 700 800 500 600 700 800 900

In Situ DataMODIS-Aqua

Ref

lect

ance

[ρ

w(λ

)] N

Wavelength (nm)

September 23, 2003 (d)(122.2oE, 30.5oN) Time Diff: -0.4 hrs

(122.3oE, 31.5oN)Time Diff: 2.2 hrs

China East Coast

September 25, 2003

θ0 = 33.8o, θ = 54.7o

θ0 = 33.1o, θ = 43.8o

(c)

Lake Taihu

10-3

10-2

10-1

100

400 500 600 700 800 500 600 700 800 900

In Situ DataMODIS-Aqua

Ref

lect

ance

[ρ w

(λ)] N

Wavelength (nm)

April 5, 2003

θ0 = 31.2o, θ = 1.7o

(b)(123.0oE, 33.0oN) Time Diff: 2.5 hrs

(120.5oE, 36.0oN)Time Diff: 4.2 hrs

China East Coast

March 22, 2003

θ0 = 36.7o, θ = 35.5o

(a)


Blue-Green Algae (Microcystis) Bloom Crisis in Lake Taihu (Spring 2007)

14

Menghua Wang, NOAA/NESDIS/STAR (Wang and Shi, 2008)


0.1

1

March 21 April 10 April 30 May 20 June 9

Chl

orop

hyll-

a (m

g m

-3)

nLw(λ) (mW

/cm2 µm

sr)

50

8

3

10

Normalized Water-leaving Radiance at 443 nm

Chlorophyll-a

Date (2007)

Bloom Location

Central Lake

Time Series of Chlorophyll-a (index) and nLw(443) at Wuxi Station (bloom) and Central Lake (non-bloom)

16


Satellite-measured ocean color products have been used for various applications and studies. In particular, satellite-measured chlorophyll-a

concentration and diffuse attenuation coefficient at 490 nm (Kd(490)) data are used for a case study in producing Marine Isoprene Emission product.

17

Menghua Wang, NOAA/NESDIS/STAR Air Resources Laboratory 18

Why is Isoprene Important? Purpose of emission: combat abiotic stresses;

(Lerdau, Science, 2007)

Ozone formation:

Aerosol formation:

Cloud formation: Cloud Condensation Nuclei (CCN); Ozone, Aerosol, cloudiness all at the central stage

of climate change debate


Marine Isoprene: A Historic Review Pioneer studies on DMS (DiMthylSulfur) oxidation as source of cloud

condensation nuclei by Shaw (1983) and Charlson et al. (1984);

Isoprene emission estimation from land by Guenther et al. (1995);

Laboratory studies on isoprene emissions from ocean (phytoplankton and seaweed), with all species tested having this capacity (Bonsang et al., 1992; Ratte et a., 1998; Shaw et al., 2003; Broadgate et al., 2004);

Algorithm to distinguish phytoplankton species using global SeaWiFS imagery (Alvain et al., 2005).

Global marine isoprene fluxes quantified using MODIS chlorophyll data (Palmer and Shaw, 2005); (one emission factor for all);

Observed relationship between phytoplankton and cloudiness in the Southern Ocean (Meskhidze et al., 2006);

New emission factors and algorithm of marine isoprene based on redesigned lab culture results (Gantt et al., 2009).

Menghua Wang, NOAA/NESDIS/STAR 12/21/2011 Air Resources Laboratory 20 More details at http://www.emc.ncep.noaa.gov/mmb/aq/AQChangelogOE.html

Anthropogenic Emission: Area, mobile, and point emissions based on EPA NEI 2005

Natural Emission: Biogenic emissions by BEIS 3.14 and USGS LULC data; Sea-salt emissions along coast lines;

A Case Study: Hawaii Air Quality Forecast


Approaches for Estimating Marine Isoprene Emissions

Air Resources Laboratory 21

Shaw et al. (2003):

EFVaChlEiso **][ −=

Palmer & Shaw (2005):

)*(* AWASiso CHCKE −=

0)/*( =−++− MIXMLASbioXiiW LZkkCkCP

Gantt et al. (2009):

∫−=max

0max **][**H

isoiso PdhFaChlHSAE

Eiso - Isoprene emission;

[Chl-a] - Chlorophyll-a;

V – euphotic water volume;

EF – Emission factor;

kAS – exchange coeff.;

CW – isop. conc. in water

H – Henry’s law constant;

CA – isop. conc. in the air

P – isoprene production;

Hmax – euphotic zone height;

ZML – mixing layer height;

ki – chemical reaction rate for oxidant i; kbio – bacterial loss rate; LMIX – loss due to downward mixing;

CXi – concentration of oxidants Xi;


Estimating Marine Isoprene Emissions


)*(* AWASiso CHCKE −=

Overall emission flux into the atmosphere (Palmer and Shaw, 2005):

WASiso CKE *=

Determine CW:

∑ ++−

=MLASBIOLXii

MIXW ZkkCk

LPC/ ∑

∫++

−=

max

max

0

2

/

)ln(*

HkkCk

LdhPAREFC

ASBIOLXii

MIX

H

W

(Palmer and Shaw, 2005) (Revised based on Gantt et al. (2009)

Derive Hmax: )/)5.2ln((max 4900

KI

H −= (Gantt et al. 2009)

I0 – ground radiatioin; K490 – diffuse attenuation coefficient in water

Menghua Wang, NOAA/NESDIS/STAR 23

Chlorophyll-a and Kd(490) Sensor/Satellite: Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua Data Processing Levels (NOAA CoastWatch http://coastwatch.noaa.gov): - Level 1: NOAA obtains data from NASA GSFC in 5-minute granules, and process to

geolocated, calibrated radiances - Level 2: Processed to derived MODIS data products (Chl-a, Kd(490), nLw, etc.) - Level 3: Products are mapped to the CoastWatch geographic regions Algorithms:

– Chlorophyll-a concentration: OC3 Algorithm (an empirical algorithm) – Diffuse attenuation coefficient at 490 nm (Kd(490)): NASA-OBPG KD2 algorithm (http://oceancolor.gsfc.nasa.gov/ANALYSIS/kdv4/)

Chlorophyll-a Kd(490)


Terrestrial vs. Marine Isoprene Emissions


Land Emission

Marine Emission

(Preliminary Results)

Monthly [Chl-a] and K490;

K490 cut-off (= 0.016);

Hourly ground radiation (I0);


Satellite-measured ocean color products can provide global ocean water optical, biological, and biogeochemical properties.

Both SeaWiFS and MODIS have been providing high quality ocean color products in the global open oceans.

A new method in the data processing using the shortwave infrared (SWIR) bands has been developed for providing good quality water property data for coastal and inland turbid waters.

Preliminary results from satellite-measure Marine Isoprene Emission product show quite promising and provide an effective tool to monitoring marine isoprene emissions in global oceans.

Future work needs to focus on validating this marine isoprene emission product, thus provide reliable satellite marine emission product.

25

Conclusions


Thank You!

26


Sensitivity to Kd(490) Retrieval Algorithm

∫−=max

0

2)ln(**][max**H

dhIEFaChlHSAP

)/)5.2ln((max 4900

KI

H −=

Some existing Kd(490) algorithms: 1. NASA previous operational algorithm (Mueller, 2000); 2. Revised Murller algorithm (J. Werdell, online 2005); 3. NASA current operational algorithm (Morel et al., 2007) http://oceancolor.gsfc.nasa.gov/ANALYSIS/kdv4); 4. Wang’s new Kd(490) algorithms: (Wang et al., 2009).

Isoprene emissions very sensitive to Kd(490) (K490):

The emission algorithm weights heavily on the lower tail of Kd(490) values.


Sensitivity to input parameter: [Chl-a]

Daily [Chl-a] and Kd(490) Monthly [Chl-a] and Kd(490)

Using monthly, instead of daily, average [Chl-a] and K490 reveals larger source area for marine isoprene emissions.

satellite ocean color remote sensing and its applications ......menghua wang, noaa/nesdis/star...

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