# Presentation: 196447

## MERIS US Workshop14 July 2008

- MERIS Level 2 processing
- Ludovic Bourg

## Presentation Outlook

- PART 1: MERIS L2 PROCESSING GENERAL OVERVIEW
- Common Processing
- Cloud Branch
- Land Branch
- Water Branch
- PART 2: WATER BRANCH ALGORITHMS,	A MORE DETAILED DESCRIPTION
- Atmosphere correction
- Ocean Color
- PART 3: TOWARD VICARIOUS ADJUSTMENT	OF MERIS TOA REFLECTANCE (at Level 2)

## MERIS LEVEL 2 PROCESSINGPART 1

- MERIS L2 PROCESSING GENERAL OVERVIEW

## Functionnal breakdown

- ATBDs available on the web at:
- _[http://envisat.esa.int/instruments/meris/atbd/](http://envisat.esa.int/instruments/meris/atbd/)_
*(Some under revision)*

- MERIS ESL:
- Institutes:
- FUB
- GKSS
- JRC
- LISE/ULCO
- LOV
- PML
- Companies:
- ACRI
- BC

## Pre-processing

- For ***all*** pixels:
- Interpolate all annotations at pixel
- Derive *surface* pressure from ECMWF *mean sea level *and pixel altitude
- For ***valid*** pixels only:
- convert L1b radiance **L** into TOA reflectance ****

## Pressure processing

- From
- measurement within the O2-A absorption line at 761 nm (band 11) and reference at 753 nm (band 10)
- accurate knowledge of the band 11 central wavelength
-  Cloud Top Pressure (neural net)
-  Surface Pressure (polynomials)
- Derived at every valid pixel (input to classification)
- Valid product only over relevant surface types (cloud and land pixels, respectively)

## Pixel identification

- Goal: sort valid pixels into Cloud, Water and Land
- Means:
- Cloud screening:
- spectrum values & slopes (on Rayleigh corrected reflectance)
- pressure tests
- Land/water discrimination:
*A priori* land/water classification
- radiometric (clear sky) land/water reclassification where *a priori* is questionable (on gas corrected reflectance)

## Smile Correction

- “Smile”: in-FOV variation of channels central wavelength
- Can affect Level 2 products if not accounted for
- Need for a simple and robust smile correction
- First order correction
- Restricted to Land and Water pixels
- Restricted to surface dependent subset of bands
- Based on surface dependent spectral slope estimates

## Cloud branch

- Compute the ***cloud albedo****, ****cloud optical thickness****, ****cloud type**** *products (in addition to already available ***cloud top pressure***)
- Albedo and optical thickness are derived from polynomials of LTOA(753) using geometry and surface albedo dependant coefficients. Polynomial coefficients are derived from radiative transfer simulations.
*Cloud type* product is an *index *computed from CTP and CA* (*defined by ISCCP climatology project)

## Total Column Water Vapour

- Determine column water vapour content above all surfaces from absorption at 900 nm
- Polynomials of T=LTOA(900)/LTOA(885) (from RT simulations)
- Surface dependent algorithms
- Above land and water with high glint:
- Correct for surface reflectance and its spectral dependency, estimated from LT at 753, 885, 900
- Account for surface pressure (vertical profile)
- Above water, no/low glint :
- Correct T for aerosol estimated from LT at 779, 865
- Above cloud :
- Account for cloud optical thickness (a MERIS cloud product)
- Account for underlying surface albedo

## Land branch

- MERISGlobal Vegetation Index

- Atmosphere Corrections over Land

- MERISTerrestrial Chlorophyll Index

## Land branch

- MERIS Global Vegetation Index

- Atmosphere Corrections over Land

- MERIS Terrestrial Chlorophyll Index

- Atmosphere & geometry insensitive vegetation index
- Remove unwanted pixels  flags
- Based on ***normalised*** TOA(B,R,IR) ( anisotropic)
- Computes rectified R for R & IR ( top of canopy at ref geometry)
- Computes MGVI(R(R), R(IR))( FAPAR)

## Land branch

- MERIS Global Vegetation Index

- Atmosphere Corrections over Land

- MERIS Terrestrial Chlorophyll Index

## Land branch

- MERIS Global Vegetation Index

- Atmosphere Corrections over Land

- MERIS Terrestrial Chlorophyll Index

## Water branch

## Water branch

- Water confidence checks

- Turbid water screening and atmosphere correction

- Clear water atmosphere correction

- Ocean color processing

## Water branch

- Water confidence checks

- Turbid water screening and atmosphere correction

- Clear water atmosphere correction

- Ocean color processing

## Water branch

- Water confidence checks

- Turbid water screening and atmosphere correction

- Clear water atmosphere correction

- Ocean color processing

## Water branch

- Water confidence checks

- Turbid water screening and atmosphere correction

- Clear water atmosphere correction

- Ocean color processing

## MERIS LEVEL 2 PROCESSINGPART 2

- A MORE DETAILED DESCRIPTION OF WATER BRANCH ALGORITHMS

## Water Processing: more details

- Input: top of atmosphere reflectance
- Mains steps:
- Atmosphere correction
- Ocean color processing

- Chl 1 from band ratio (case 1)
- Chl 2, TSM, YS from Neural network (case 2)

## Water processing: atmosphere model

## Water processing: simplified signal model

## Water Processing: gas correction

- Gas corrections
- O3: transmittance tO3 from ECMWF ozone and optical thickness for each band
- H2O: transmittance at any band tH2O(b) estimated from measured tH2O(900)
- O2: transmittance at any band tO2(b) estimated from measured tO2(761)

## Water Processing: glint & bright pixels

- Pixels screening & glint flagging/correction:
- Glint reflectance estimates: Cox & Munk anisotropic model (revised with Ebushi & al parameters, and fed by ECMWF wind vectors)
- 2 glint thresholds and flags:
- high glint: to high for reliable correction (G≥.(865))
- medium glint: correction is required and possible(≤ G < .(865)) 		GC() = ng() – t. G
- “Bright pixels” screening: undetected clouds, haze, high aerosols and ice flagged (from a reflectance test at 412)
- Whitecaps: raise flag upon wind speed, no correction

## Water Processing: path reflectance

- Based on a classical “black ocean in the NIR” approach
- Using standard aerosol models closed to SeaWiFS ones
- With some specificities:
- Rayleigh/aerosol coupling accounted for through the use of path/R (ATBD 2.7)
- Extension to (moderately) sediment loaded waters through estimation of t.w in the NIR (ATBD 2.6)
- Additional aerosols with steep spectral dependency
- Detection of absorbing aerosols using “deficit of reflectance at 560 nm” (ATBD 2.7) and corresponding additional models (Moulin et al)

## Water Processing: turbid waters

- Purpose:
- To identify waters with significant backscatter in the NIR
- To provide corresponding reflectance estimates allowing atmosphere corrections

## Water Processing: turbid waters

- Simplified atmospheric model :single scattering approximation
- rc = GC-R so that rc-*t*w= a
- 	t = diffuse transmission up & down, Rayleigh only
- Find n and TSMthat explain RC at 709, 779 & 865
- Realized using a simplified w model
- Ends up with *t*.w estimates at 779, 865 + CASE2_S flag
*(ATBD 2.6 under revision)*

## Water Processing: aerosol identification

- Principle:
- Inputs: path=GC -*t*.w at 779 and 865 (=GC in case 1)
- Tabulated relationships:
-  path/R=f(,,,model),	(1)
-  =f -1(path/R,,,model) 	(2)
-  (,model) = (ref,model)	(3)
- For all aerosol models:
-  Measured path/R at 865 gives 865 using (2)
-  865 gives 779 using (3)
-  779 gives path/R at 779 using (1)
- Select pair of models most closely bracketing measured path/R at 779 and mixing ratio that fits.

## Water Processing: aerosol identification

- Illustration with a set of 4 maritime models

******path****/********R**

- θs = 40°, θv = 30°,
- Δφ = π/2.

## Water Processing: aerosol identification

- Global procedure:
- identification done over the whole set of non-absorbing aerosols (MAR, COA, RUR with various RH + 3 “blue”)
- best pair is selected, derive: 			at 510 nm
- If not CASE2_S then:
- Get w(510) using
- If w < (mean+)climatology repeat procedure with the absorbing aerosol families   new candidate pairs
- Select the one that minimizes |w - (mean+)climatology|
- Proceed to correction at all bands

## Water Processing: aerosol products

- Aerosol optical thickness at 865nm
- Aerosol Angstrom exponent (779-865)
- Atmosphere correction flags:
- validity flag for aerosol products
- OADB (out of aerosol database: limited representativity)
- ABSO_D: absorbing aerosols were selected
- CASE2_S: significant scattering by water in the NIR
- Medium glint
- High glint
- Ice/haze/high aerosol

## Water Processing: Ocean Color

- Ocean color products:
- Algal 1 index (~Chla concentration, mg.m-3): case 1
- Algal 2 index (~Chla concentration, mg.m-3): case 2
- Total Suspended matter (g.m-3): case 2
- Yellow Substance (absorption at 442, m-1): case 2
- Flags:
- Validity flags for each product
- CASE2_Anomalous: excess of reflectance at 560 as compared to derived Algal1  anomalous scattering

## Water Processing: Algal 1

- In Case 1 water,
- R() is a function of algal pigments
- R(442)/R(560) (or R(490)/R(560)) is directly related with Chl

- contribution of the
- air-water interface

- bi-directional distribution
- of the upwelling radiance
*(**depends on Chl**)*

*E*u : upwelling irradiance (integral of radiance) just below the surface
*E*d : downwelling irradiance

- From MERIS case 1 model:

## Water Processing: Algal 1

- Reflectance correction for bi-directionality  requires knowledge of Chl  iterative method:
- Select highest  ratio among bi{443,490,520}, bref=560
- Normalization of w  ratio using tabulated f/Q (Chl dependent)
*log *Chl1 = polynomial (band_ratio)

- Iterative
- process

## Water Processing: Algal 1

## Water Processing: Case 2 Neural Network

- Case 2 waters:
- Described by *optical components*:
- Phytoplankton pigment absorption apig
- Absorption of all other substances ayp(dissolved Gelbstoff y and non-algal particles p)
- Scattering of all particles bp
- Global inversion is done using a Neural Network (NN), provides apig(442), ayp(442), bp(442) from reflectance in 8 bands
- NN is trained offline over a large data set produced by Radiative Transfer model
- Internal consistency verification with a forward model NN
- Components converted to concentrations using reversible relationships

## Water Processing: Case 2 Neural Network

- *: 412 to 709, excluding 681

## Water Processing: Case 2 Neural Network

- Neural Net principle and training conditions are described in a recent publication:
- Doerffer, R. and Schiller H. (2007) ’The MERIS Case 2 water algorithm’, Internal Journal of Remote Sensing, 28:3, 517-535
- As well as:
- MERIS ATBD 2.12 *(under revision)*
- MERIS Reference Model Document *(under revision)*

## MERIS LEVEL 2 PROCESSINGPART 3

- TOWARD VICARIOUS ADJUSTMENT OF MERIS TOA REFLECTANCE

## Vicarious Adjustment: Principle

- Based on the work currently led at NASA for SeaWiFS and MODIS vicarious calibration, see Franz et al. 2007, Bailey et al. 2007.
- Consist in computing averaged multiplicative gain factors to correct the “TOA signal”, thanks to a DB of reference in-situ tw
- “TOA signal” = Level 2 reflectance pre-corrected for smile, gaseous absorption, glint, i.e. at input of the Water AC algorithm
-  gcnew() = gc()*G() for  in the VIS and NIR
- Two-step approach separating the NIR and VIS channels, avoiding iterative procedure within the AC algorithm
- First adjust one of the two NIR bands used in aerosol retrieval (the other one assumed perfect)  G(779) for MERIS AC
- AC being now assumed “perfect”, adjust all other bands  G()
- The methodology fully imbricates the sensor response and the processing:
- The gains need to be updated each time a change occur in the processing (LUT, algorithm, L1b calibration, etc...)
- A strong effort of traceability in the gain computation, with respect to all other processing parameters, should be maintained.

## Vicarious Adjustment: Principle

- Computation starts from the decomposition:  gc() = path() + td() . w()
- Knowing the true (or targeted) signal through in-situ measurements, _individual gains_ are computed matchup per matchup by
- gi() = [tpath() + ttd() . tw()] / [path() + td() . w()]
- _Average gains_ are finally derived: G() = mean(gi(
- To calibrate the NIR, one requires:
- the most NIR band (865) is perfectly calibrated *(**** assumption)*,
-  w(NIR) is truly negligible, (*** **oligotrophic sites)*
- aerosol spectral dependency is known ( specific sites).
- Thus one has
-  tgc(865) = gc(865) = path(865)
-  tgc(779) = gi(779).gc(779) = tpath(779)
- Using provides tpath(779) **hence g****i****(779)**
- Once NIR is calibrated, tpath()=path() and ttd()=td() at all bands, **hence ****g****i****(********)**

## Vicarious Adjustment: implementation

- NIR calibration sites: **South Pacific Gyre and South Indian Ocean**, selected model MAR 90% (including continental background in the free troposphere and H2SO4 in the stratosphere of fixed 550 = 0.025 and 0.005 resp.)
- In-situ data: AAOT, BOUSSOLE and MOBY (foreseen : NOMAD)

- Data selection:
- no flags within 25x25 px
- Results in:
- NIR (2003 & 2007 only):
- 14 at SPG
- 34 at SIO
- VIS:
- 28 at MOBY
- 29 at BOUSOLE
- 3 at AAOT

## Vicarious Adjustment: Preliminary Results

- SeaWiFS figure from **Bailey S.W., S.B. Hooker, D. Antoine, B.A. Franz, P.J. Werdell, « Sources and assumptions for the vicarious calibration of ocean color satellite observations », Applied Optics, 47(12), 2035-2045.**

- SeaWiFS

- MERIS

## INTER-SENSOR COMPARISONS: CHLa

- MERIS:no vicarious adjustment

- Source GlobColour,
- Courtesy:
- SeaWiFS: NASA (v5.2)
- MODIS: NASA (v1.1)
- MERIS: ESA (v2.0Q)