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MODIS 500 m ocean colour data through exploiting spectral and spatial correlation Jamie Shutler, Peter Land, Tim Smyth, Steve Groom, Daniel Sanders and.

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Presentation on theme: "MODIS 500 m ocean colour data through exploiting spectral and spatial correlation Jamie Shutler, Peter Land, Tim Smyth, Steve Groom, Daniel Sanders and."— Presentation transcript:

1 MODIS 500 m ocean colour data through exploiting spectral and spatial correlation Jamie Shutler, Peter Land, Tim Smyth, Steve Groom, Daniel Sanders and Ralph Collett NERC Remote Sensing Data Analysis Service, Plymouth Marine Laboratory, UK 15 th June 2004 MODIS Terra “True Colour” Plymouth coccolithophore bloom

2 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

3 1) What is ocean colour? “A term that refers to the spectral dependence of the radiance leaving a water body” (NOAA glossary) Lord Rayleigh ( ): “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”

4 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) = a w (λ) + a ph (λ) + a p (λ) + a y (λ) b b (λ) = b bw (λ) + b bp (λ) reflectance (R) which can be detected using remote sensing: 1) What is ocean colour?

5 Phytoplankton – fine eddy structure SedimentCoccolithophoresCDOMbloom? Clear blue ocean

6 1) What is ocean colour? – the need for atmospheric correction cloud masking – less rigorous on sensors with no IR bands L w – only 5% of signal reaching satellite: rest due to L p L p components: molecular (Rayleigh) & aerosols Clouds

7 Dark pixel approximation over oceanic regions assume L w (765,865) = 0 any signal due to L p (765,885) remove Rayleigh and extrapolate aerosol to other wavelengths 1) What is ocean colour? – the need for atmospheric correction

8 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 and Irish Sea coastal observatories) see Shutler et al. poster

9 2) RSDAS – DB processing chain details Dundee Satellite Receiving Station NASA /NOAA Centres provide global/backup coverage RSDAS Users FTP Satellite link Scientists at sea/ In the field Level 2/3 data Sea-surface temperature Ocean colour properties Atmospheric properties Earth/terrestrial properties Atmospheric correction Navigation Near-real time Level 2 products ~0.5h AVHRR ~0.5h SeaWiFS ~1h MODIS Password protected Web site with simple Java Image analysis 10 Terabyte Image Database Level 0/1 data Received in Plymouth: ~26 passes/day =15GB/day Internet <100 Mbit/s Internet

10 2) RSDAS – DB processing chain details Passes split into 3 granules and processed in parallel on Linux Beowulf cluster 00:00 00:20 00:25 Data transfer Waiting 00:35 Level 0 – 1b Level 2 Granule stitching and mapping Web products 00:55 00:60

11 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 UTC + MODIS Terra + Aqua: 27 Jan 2004 MODIS Aqua: 27 Jan 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):

12 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

13 iii) Improved spatial resolution of features e.g. eddies, fronts 4) MODIS 500m data - Why do we need it? 11 July UTC Aqua nLw(469) Turbidity front Physics “mixing up” the biology

14 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 nmBand 3469 nm Band nmBand 4555 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:

15 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

16 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)

17 AC data: regress Lw 469 (1 km) against Lw 488 (1 km) Strongly correlated linear relationship R 2 = ) MODIS 500m data - methodology ii) Spectral correlation (cont)

18 iii) Spatial correlation 4) MODIS 500m data - methodology 500m 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)

19 4) MODIS 500 m data - results Lw 551 (1 km) Lw 555 (500 m) U.K. South West Approaches: 11 July :38 UTC Aqua Lw

20 mg m -3 4) MODIS 500m data - results U.K. South West Approaches: 11 July :38 UTC Aqua Chl 500 m 1 km Same broad-scale features low chlorophyll < 0.3 : lower at 500 m Information from estuaries Bloom fine-scale structure

21 Lw 555 (500 m) Lw 551 (1 km) 4) MODIS 500m data - results Plymouth Sound and Whitsand Bay Can see further into Plymouth Sound Residual problems with adjacency

22 Antarctic Peninsula: 6 th February Collaboration with BAS Lw 469 (500 m) chl-a (500 m) 4) MODIS 500m data - results

23 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 Environmental monitoring e.g. algal blooms Automatic spatial localisation of a phytoplankton bloom.

24 Apply same technique to 555 nm channel to extrapolate to 551 nm (R 2 = 0.99; m = 1.07 c = ) 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

25 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 m mapped products) Able to process both MODIS-Aqua and MODIS-Terra Early results look promising.

26 Extra slides

27 Results Iberian peninsula 25 August 2003 SeaWiFS 1 km MODIS 1 km MODIS 500 m

28 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) R 2 = 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.

29 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). BandWavelengthSNR (model) CZCS CZCS CZCS MODIS 3 (500m) MODIS 4 (500m) MODIS (1km)8 bands


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