MODIS 500 m ocean colour data through exploiting spectral and spatial correlation

Jamie Shutler, Peter Land, Tim Smyth, Steve Groom, Daniel Sanders and Ralph CollettNERC Remote Sensing Data Analysis Service, Plymouth Marine Laboratory, UK

15th June 2004 MODIS Terra “True Colour”

• Plymouth

coccolithophore bloom

Overview

1) What is ocean colour?– The need for atmospheric correction

2) The Remote Sensing Data Analysis Service (RSDAS)– DB processing chain details

3) Why use MODIS DB data?4) MODIS 500 m data:

– Why do we need it?– Methodology– Results– Application– Future developments

5) Conclusions

1) What is ocean colour?• “A term that refers to the spectral dependence of the radiance

leaving a water body” (NOAA glossary)• Lord Rayleigh (1842-1919): “The much-admired dark blue of the

deep sea has nothing to do with the colour of the water, but is simply the blue of the sky seen by reflection.”

• Raman (1922): “A voyage to Europe in the summer of 1921 gave me the first opportunity of observing the wonderful blue opalescence of the Mediterranean Sea. It seemed not unlikely that the phenomenon owed its origin to the scattering of sunlight by the molecules of the water”

• Mobley (1994): “Natural waters, both fresh and saline, are a witch’s brew of dissolved and particulate matter. These solutes and particles are both optically significant and highly variable in kind and concentration”

a(λ) = aw(λ) + aph(λ) + ap(λ) + ay(λ)

bb(λ) = bbw(λ) + bbp(λ)

• reflectance (R) which can be detected using remote sensing:

)()(

)()0,(

b

bE

ba

brR

1) What is ocean colour?

Phytoplankton – fine eddy structure

Sediment

Coccolithophores

CDOM

bloom?

Clear blue ocean

1) What is ocean colour? – the need for atmospheric correction

• cloud masking – less rigorous on sensors with no IR bands

• Lw – only 5% of signal reaching satellite: rest due to Lp

• Lp components: molecular (Rayleigh) & aerosols

Clouds

Clouds

Dark pixel approximation

• over oceanic regions assume Lw(765,865) = 0

• any signal due to Lp (765,885)

• remove Rayleigh and extrapolate aerosol to other wavelengths

1) What is ocean colour? – the need for atmospheric correction

2) The Remote Sensing Data Analysis Service (RSDAS)

• A NERC funded service provided by PML Remote Sensing Group• Provides Earth Observation data and information to underpin science

in the UK academic community– Currently funded primarily for marine science (~20% non marine)

– Complementarity – we don’t do what ESA or NASA does already

– Ease of use of data by specialists and non specialists alike

Guiding points include:

– Timeliness – DB data processing in near-real time• To guide research ships at sea• Increasing input to monitoring systems

(e.g. western English Channel andIrish Sea coastal observatories)

• see Shutler et al. poster

2) RSDAS – DB processing chain details

Dundee SatelliteReceiving Station

NASA /NOAACentres provide global/backup

coverage

RSDAS Users

FTP

Satellitelink

Scientists at sea/In the field

Level 2/3 data• Sea-surface temperature • Ocean colour properties• Atmospheric properties

• Earth/terrestrial properties

AtmosphericcorrectionNavigation

Near-real time Level 2 products

~0.5h AVHRR~0.5h SeaWiFS

~1h MODIS

Password protectedWeb site with

simple JavaImage analysis

10 TerabyteImage

Database

Level 0/1 dataReceived in Plymouth:

~26 passes/day=15GB/day

Internet<100 Mbit/s

Internet

2) RSDAS – DB processing chain details

Passes split into 3 granules and processed in parallel on Linux Beowulf cluster

00:00

00:2000:25

Data transfer

Waiting

00:35

Level 0 – 1b

Level 2

Granule stitching and mapping

Web products

00:55

00:60

3) Why use MODIS DB data?• DB data is crucially important to RSDAS – cruise support (285 d yr-1)• MODIS provides free-to-air DB ocean colour unlike:

– MERIS– SeaWiFS (licence + user agreement; now data encrypted)

• Two sensors (Aqua and Terra) - multiple daily passes– ameliorate cloud problems

MODIS Terra: 27 Jan 2004 1131 UTC

+

MODIS Terra + Aqua: 27 Jan 2004 MODIS Aqua: 27 Jan 2004 1310 UTC

=

Shutler JD, Smyth TJ, Land PE, Groom SB (2005) A near-real time automatic MODIS data processing system Int. J. Remote Sens. 26 (5): 1049-1055

4) MODIS 500m data - Why do we need it?

i) Coastal and large estuarine studies

1 km

500 m

ii) Water quality – e.g. Harmful Algal Blooms; Eutrophication; pollution

HAB

May 2000

detail available within estuaries – although still adjacency issues to resolve

iii) Improved spatial resolution of features e.g. eddies, fronts

4) MODIS 500m data - Why do we need it?

11 July 2005 1338UTC Aqua

nLw(469)

Turbidity front

Physics “mixing up” the biology

4) MODIS 500m data - methodology

• To begin with we will settle for 488 nm and 555 nm at 500 m

• Need to atmospherically, spectrally and spatially correct these

bands at 500 m …

1 km band (nm) 500 m band (nm)

Band 10 488 nm Band 3 469 nm

Band 12 551 nm Band 4 555 nm

Aim: Atmospherically corrected 500 m chlorophyll product

• simple (Carder 2003) Chl band ratio algorithm 488/551 (1 km)

• ideally want 488 and 551 nm at 500 m resolution:

Use AC at 1 km to correct 500 m data

Alternative approach

i) Atmospheric correction (AC)

Advantages:• Uses sophisticated ocean colour AC• Pixel by pixel correction (1 km resolution)• Allows for aerosol variability and atmospheric transmission

4) MODIS 500m data - methodology

Only 4 bands at 500 m: necessitates a simple “dark pixel” approach.

Assumes uniform aerosol of known type across entire scene

Susceptible to noise and outliers

Ignores atmospheric transmission

Optimal spectral interpolation of parameters to 500 m wavelengths

Spatial interpolation to 500 m

ii) Spectral correlation

• Strong correlation between spectrally close bands• Interested in 469 nm (500 m) and 488 nm (1 km)

Modelled chl reflectance spectra• Good linear approximation between 469 nm and 488 nm

4) MODIS 500m data - methodology

Morel and Maritorena (2001)

• AC data: regress Lw469 (1 km) against Lw488 (1 km)• Strongly correlated linear relationship R2 = 0.99

4) MODIS 500m data - methodology

ii) Spectral correlation (cont)

iii) Spatial correlation4) MODIS 500m data - methodology

500m500m

500m500m

500m500m

500m500m

1 km

Alignment of 500 m pixels with 1 km pixel

Overcome alignment problem:

• 469 nm is strongly correlated with 488 nm

• weightings (intra-variation) within 500 m group same at 469 as at 488 nm

• use weightings at 469 nm (500 m) to refine 488 nm (500 m)

4) MODIS 500 m data - results

Lw551 (1 km)

Lw555 (500 m)

U.K. South West Approaches: 11 July 2005 13:38 UTC Aqua

Lw

mg m-3

4) MODIS 500m data - results

U.K. South West Approaches: 11 July 2005 13:38 UTC Aqua Chl

500 m1 km

Same broad-scale features

low chlorophyll < 0.3 : lower at 500 m

Information from estuaries

Bloom fine-scale structure

Lw555 (500 m)

Lw551 (1 km)

4) MODIS 500m data - results

Plymouth Sound and Whitsand Bay

• Can see further into Plymouth Sound

• Residual problems with adjacency

Antarctic Peninsula: 6th February 2004. Collaboration with BAS

Lw469 (500 m)

chl-a (500 m)

4) MODIS 500m data - results

4) MODIS 500 m data - application

Towards spatial localisation of harmful algal blooms; Statistics-based Spatial anomaly detection, J. D. Shutler, M. G. Grant, P. I. Miller, SPIE Remote Sensing Europe 2005 (Image and Signal processing for remote sensing XI), Belgium, September 2005.

• Environmental monitoring e.g. algal blooms

Automatic spatial localisation of a phytoplankton bloom.

• Apply same technique to 555 nm channel to extrapolate to 551 nm

(R2 = 0.99; m = 1.07 c = 0.00069)• In-situ chlorophyll comparisons.• Atmospheric correction development:

– Case 2 waters?• Land/sea adjacency affect.• Issues relating to the point spread function?• Spectral regression will break down for scenes with large absolute

differences between chlorophyll concentrations.– Spatially sub-divide the scene?– Multiple single linear-regressions based on confidences?– Caveat: regional chlorophyll algorithm.

4) MODIS 500 m data – future developments

5) Conclusions

• RSDAS have developed a processing scheme for DB MODIS data.• Illustrated a method for atmospherically correcting MODIS 250 m

and 500 m land channels when viewing the ocean.• Developed a simple method of exploiting MODIS 500 m channels for

chlorophyll estimation without the need to determine a new chl-a relationship.

• Processing is automatic (from level 1b to mapped level 2 500 m mapped products)

• Able to process both MODIS-Aqua and MODIS-Terra• Early results look promising.

Extra slides

Results

Iberian peninsula25 August 2003

SeaWiFS 1 km

MODIS 1 km

MODIS 500 m

500m Chlorophyll estimates• Comparing 488/555 (1 km) with 488/551 (1 km).• The Ideal case is a 1:1 agreement (slope = 1; intercept = 0.00)• R2 = 0.86; slope = 1.04; intercept = 0.07• Justifies using 555 channel• However, result compounds noise in 555 nm (500 m) channel

and the difference in response between 551 nm and 555 nm.

Performance• The MODIS 500m channels have lower S/N ratios than most of the

1km channels.• MODIS 500m channels have wider bandwidths.• S/N ratios for 500m 469 nm and 555 nm are still greater than those

of CZCS.• Applicable to Case 1 waters (atmospheric correction and chl-a).

Band Wavelength SNR (model)

CZCS 1 443 211

CZCS 2 520 180

CZCS 3 550 208

MODIS 3 (500m) 469 328

MODIS 4 (500m) 555 240

MODIS (1km) 8 bands 717-1300