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