# Presentation: 467441

## Anisotropy of the Earth reflectances: A bummer or an asset ?

- François-Marie BREON,
*Laboratoire des Sciences du Climat et de l’Environnement*
- and the POLDER-Parasol science team

- Fabienne
- Maignan

- Cedric
- Bacour

- Emilie
- Fedele

- Nicolas
- Henriot

## Yoram’s legacy

- Use glint and off-glint measurements to estimate aerosol absorption

## Glint analysis from POLDER measurements

- Wind Speed
- 4 m s-1

- Wind Speed
- 1 m s-1

- The glint appears as a bright spot on satellite imagery close to the specular direction.
- As the wind speed increases, the glint gets fainter but covers a wider range of directions

## Modeling

**

## Wave Slope distribution model

- Cox & Munk, 1954.  Based on airborne photographs of the sun glint

- Corrections to pure Gaussian distributions

## A revisit of Cox&Munk wave slope model

- A quantitative analysis of the glint is needed
- 	• for its impact on many remote-sensing methods (detection, correction)
- 	• to analyze the wave slope distribution (hydrodynamic, turbulence)
- POLDER is a great instrument for this purpose
- 	• A glint observation in each image acquisition => Global coverage and great statistic
- 	• Multi-directional observation, so that targets are observed on-glint and off-glint (needed to detect cloud contamination)
- 	• The NSCAT scatterometer, onboard the same platform, provides simultaneous measurements of wind speed and direction

- After atmospheric transmission correction, measured reflectance is directly related to the wave slope probability
- Compare to Cox&Munk model (function of wind speed and direction and 7 empirical parameters)

**Notes:**
We corrected the POLDER reflectance measurements for atmospheric transmittance (used 865 nm) which is less affected than other bands
Converted reflectance to slope probability
Assimilated these values into Cox and Munk model to make a new estimate of the model parameters (slope variances, skewness, peakedness)

## How bright is the glint ?

- Typical value ≈0.25
- Fainter than a typical cloud
- Typical reflectance very much a function of sun zenith angle, i.e. 0.6 for SZA in [50°, 60°]

- Modeling indicates that

- Sun angle normalization:

## Data processing

- Use concomitant measurements from POLDER and NSCAT
- POLDER reflectance measurements
- 	• Cloud detection
- 	• Correction for atmospheric transmission
- 	• Use 865 nm channel to minimize atmospheric effects and ocean column contribution
- 	• Reflectance measurements converted to slope probability
- Wind Speed and direction from NSCAT
- Invert the parameters of the wave slope model:
- 	• Mean slope (upwind and crosswind): ***up*, ***cr*
- 	• Skewness coefficients: c21, c03
- 	• Peakedness coefficients: c40, c04 , c22

## Measured slope distributions

- 1-2 m s-1

- 3-4 m s-1

- 5-6 m s-1

- 7-8 m s-1

- 9-10 m s-1

- 11-12 m s-1

- Upwind

- CrossWind

- 45° from wind

- Original Cox& Munk model

- Modified model

**Notes:**
The measured slope distributions agree rather well with the model.  Little difference between observed model and best fit model.

## Model fits both small and large prob.

## UpWind and CrossWind variances

- Original Cox&Munk model

- Modified model

- Each symbol is an independent estimate of the model parameter for a 0.5 ms-1 bin of wind speed
- Original model fits extremely well the observed variances
- Confirm that upwind variance larger than crosswind variance

- Wind Speed [m s-1]

- 0

- 15

**Notes:**
Our results provide an amazing confirmation of the accuracy of the Cox&Munk empirical estimate of wind slope variances, both for the upwind and the crosswind components

The Cox and Munk model describes the distribution of wave slope as a function of wind speed and direction.  The distribution is almost Gaussian.  sigma_up and sigma_cr quantifies the mean square slope upwind and crosswind.  Cox&Munk had shown that these are very linear function of wind speeds and our spaceborne measurement confirm this finding.  Note that the upwind/downwind mean slopes are larger than the crosswind ones.

## C03 and C21: Skewness of distribution

- Negative skewness indicates that most probable slope faces upwind
- Our measurements support a quadratic function of the wind speed rather than a linear function : c21 = a ws2 ; c03 = sigmoïd
- One may improve slightly the Cox&Munk model, but limited impact on the predicted reflectance
- Main result is a strong validation of the 50+ year old model !
- Uncertainty on glint estimate ≈35%.  Mostly due to WS error

*Bréon FM and N. Henriot, JGR, 2006*

**Notes:**
The skewness of the distribution quantifies the fact that the slope distribution is not symetric with respect to nadir.  The most probable slope faces slightly upwind.
Our results suggests that the skewness of the distributions do not follow a linear function of wind speed, but rather a quadratic one.
F. M. Bréon, and N. Henriot: Spaceborne observations of ocean glint reflectance and modeling of wave slope distributions.  J. Geophys Res - Ocean, in press 2006

Now, the distributions are not exactly Gaussian and CM have fitted the observed distribution using a Gram Charlier decomposition (something like harmonics).  5 terms of the decompositions appear sufficient to fit the data.
Two terms quantifiy the skewness of the distribution.  They are shown on graph 34.  Other terms quantify the peakedness of the distribution but are not shown in this presentation (small and very large slopes are more probable than for a Gaussian distribution

## Charles Cox and Walter Munk reaction:

- Cox & Munk model published in 1954

- Date: Sat, 5 Aug 2006
- From: Walter Munk <wmunk@ucsd.edu>
- Dear Francois-Marie:
- Chip Cox has kindly drawn my attention to the very nice paper by you and Henriot.  Of course I am delighted with the astonishing agreement with regard to the mean-square slopes (though I found the reference to work ".. done more half a century ago .."  somewhat disconcerting).
- I am particularly interested in your results on the skewness,  and the "saturation" at winds above10 m/s.
- I would much appreciate if you keep us appraised of any further work.
- Walter Munk

- Date: Tue, 6 Jun 2006
- From: Charles Cox <cscox@ucsd.edu>
- Subject: sun glitter and sea surface slopes
- Dear Walter
- Francois-Marie Breon has kindly sent me this paper on sea surface slopes derived from sun-glitter observed from a satellite. Their results are astonishingly similar to ours, even in the peakedness terms of the distribution where our measurements were very uncertain and scattered. The most interesting feature of the new data lies in the variation of skewness with wind speed.  Where we represented the skewness  as increasing linearly with wind speed, the new data show that the C03 term is "*S*" shaped and approaches a limit for winds above 10 m/s.  A dynamical explanation could be that ripples become like rounded saw teeth when running through the intense surface shear created by strong winds.  I am reminded of the appearance of ripples on a thin sheet of rainwater running down an inclined driveway, where they take this shape. This only occurs when the driveway is sufficiently steep, thus where there is a strong shear.
- Chip

## Now, switch to land surfaces

- Principal Plane

- Nadir

- Multispectral measurements acquired every 20 sec

- Phase
- angles

- View Angles

- Perpendicular Plane

- 13-16 Parasol views

## POLDER measures directional signatures

## Full BRDF measurements

- Multi-day measurements yield full coverage of the target directional signature
- 	• BRDF modeling
- 	• Albedo estimate
- 	• Surface characteristic

## Measurement and analysis of Hot-Spot

- First (and only) quantitative measurement of the Hot-Spot (amplitude and shape)
- Reflectance varies as (1+ /0)-1
- Half width 0 between 1° and 3°.
- Analytical model that relates HS width to the ratio of leaf size and density
- HS signature introduced is Ross-Li analytical model leads to better fit with observations.

*Bréon FM et al., JGR, 2002*
*Grant I et al, IJRS, 2003*

**Notes:**
Image shows POLDER observation over Amazonia.  Red lines show the phase angle by step of 10°.  Green line is principal plane.  There is a clear increase of reflectance in the last 10 degrees.  There is an even larger increase within 2 degrees of backscattering but specific processing is required to quantify it. 
Grant IF, Breon FM, Leroy MM  First observation of the hot spot from space at sub-degree angular resolution using POLDER data   Int J. Rem. Sens., 24 (5): 1103-1110 (2003)
Bréon, F.-M., F. Maignan, M. Leroy, and I. Grant.  Analysis of hot spot directional signatures measured from space.  J. Geophys. Res., 107, 4282-4296 (2002)
There is a modeling of the width as a functino of width size and vertical scale in the original JGR paper (equation 15) :
xi0=D L /(6.4 H) 
where D is leaf diameter, L is LAI and H is canopy depth.  Basically, you have an horizonal scale (D) and a vertical scale H/L which is the depth of the canopy that contains an LAI of 1.  xi0 is related to the ratio of the horizontal and vertical scale.

## Validate and rank BRDF models

- A corrected Ross-Li model, with three free parameters allows the best fit to the observed BRDFs
- Other tested models are so-called Roujean, Ross-Li, RPV, Engelsen, and Walthall
- A statistical analysis lead to typical directional signatures for each IGBP biomes

*Maignan et al., RSE, 2004*
*Bacour and Bréon, RSE, 2005*

- Ross-Li-HotSpot
- Roujean-HotSpot
- Rahman-Pinty-Verstraete
- Ross-Li
- Engelsen
- Roujean
- Walthall

**Notes:**
The cumulative histogram quantifies the ability of various models to fit the observed directional signatures.  This figure indicates that the Ross-Li model, corrected to account for the Hot-Spot effect (3 free parameter model) permits the best fit to the observations.  Note that the RMS error results from both the inadequacy of the model and the noise in the measurements.  The noise is expected to has the same effect for all models.
Best targets are those with a small RMS difference (bottom left of figure).  The difference between the various models appears more clearly for these targets.
Bacour, C. and F.-M. Bréon   Variability of land Surface BRDFs.  Remote Sensing Environment. 98, 80–95 (2005).
Maignan, F., F.M. Bréon, R. Lacaze: Bidirectional reflectance of Earth targets: Analytical modeling and validation against a large data set of satellite observations.  Rem. Sens. Env., 90, 210-220 (2004).

## Modeling per biome

- The Bidirectional Signature is
- Within a given biome, the variability of the directional signature is rather small
- We defined “typical” directional signature (i.e. k1/k0 and k2/k0) for each biome.

- Ici, une figure qui montre quelques signatures dans le PP

## Correction for directional effects

- Puts the reflectance measurements in a standard observation geometry
- Makes use of the directional signatures derived from POLDER observations (sun at 40°, satellite at nadir)
- The normalized time series show much less high-frequency variability than the original measurements:

- DVI (R865-R670)
- MODIS measurements
- Corrected for atmosheric effects

- After directional signature correction.  Uses “a-priori” BRDF shape

## Application: Vegetation onset

- Most vegetated areas show a strong annual cycle of vegetation growth
- The dynamic is controlled by temperature, water availability or both.
- For a given region, the total Carbon uptake is strongly linked to the date of vegetation onset (earliermore uptake)
- From our DVI time series, we estimate the vegetation onset as the date when the *smooth curve* crosses the *trend* upward.
- A small fraction of sites have a seasonal cycle not suitable for such procedure (double crosses)

- Illustration ?

## Correlation with NAO over Europe

- NAO is the difference of surface pressure between Iceland and Azores.  It controls the flow from the Atlantic to Western Europe
- There is a strong correlation of winter NAO with Onset Date

- Carte des corrélations

*Maignan et al., RSE, 2007*

## Conclusions

- We now have an accurate, validated, model of Ocean glint.  Can be used to test Yoram’s idea for absorption estimate
- POLDER/Parasol provide global measurements of BRDFs at coarse resolution.  Variability of these BRDFs smaller than expected
- 	• No succesfull attempt to quantitatively retrieve surface parameters from the directional signature
- 	• To the first order, the reflectance variability due to the directional signature can be corrected with a simple procedure
- Such correction has been applied to both AVHRR and MODIS time series with amazing results

- Wait, there is more: I have a free offer ! (two slides)

## A BRDF database from Parasol

- Choose target through a simple click

- With an analysis tool

## Target BRDF analysis

- Database and tool available on request