# Presentation: 516415

## Characterizing Land Surfaces from  MISR Measurements

- Michel M. Verstraete1, with contributions from
- Bernard Pinty1, Nadine Gobron1,
- Jean-Luc Widlowski1 and David J. Diner2
- 1Institute for Environment and Sustainability (IES)
- Joint Research Centre, Ispra (VA), Italy
- 2NASA Jet Propulsion Laboratory, Pasadena, CA, USA
- ISSAOS course in L’Aquila
- Thursday August 29, 2002

## Outline

- MISR and multiangular ToA observations
- Surface anisotropy primer
- Multiangular reflectance nomenclature
- Interlude: The RPV GUI tool
- MISR standard surface products
- MISR non-standard surface products
- Case study: AirMISR and surface heterogeneity

## Overview of MISR

- 9 cameras at ±70.5, ±60, ±45.6, ±26.1, 0°
- Each camera at 446, 558, 672, and 866 nm
- Spatial resolution: 275 m (250 m nadir)
- Global mode: Full res. nadir and red, 1.1 km otherwise
- Local mode: Full resolution all cameras and all bands
- Swath: 360 km
- Coverage: global (9 days)

- Ref: http://www-misr.jpl.nasa.gov/mission/minst.html

## Slide 4

## ToA anisotropy from MISR (1)

- Ref: http://www-misr.jpl.nasa.gov/gallery/galhistory/2001_may_02.html

- Shikoku Island, Japan

- April 13, 2001

- Clouds

- RGB = R, G, B (26a)

- RGB = R, G, B (60f)

- Aerosols

- Vegetated land

- 285 km

## ToA anisotropy (2) over Zambia, Namibia and Botswana

**25 August 2000**

**Nadir Blue**
**Nadir Green**** **
**Nadir Red**

**70o Aft Red (forward scatter)**
**70o Fwd Red (backscatter)**
**Nadir Red**

**Nadir Green**
**Nadir Red**
**Nadir NIR**

- Credit: NASA/JPL MISR Team

**Notes:**
L1B2 TOA data

## ToA anisotropy from MISR (3)

- Saskatchewan
- and Manitoba

- April 17, 2001

- RGB = Nir, R, G

- 285 km

- RGB = R60a, Rn, R60f

- N

- Ref: http://www-misr.jpl.nasa.gov/gallery/galhistory/2001_may_30.html

- Snow

- Forests

- Agriculture

- Roads

## ToA anisotropy from MISR (4)

- Ref: http://www-misr.jpl.nasa.gov/gallery/galhistory/2002_apr_17.html

- 191 km

- South Florida

- January 16, 2002

- Lake
- Okeechobee

- Farmland

- RGB = R, G, B

- RGB = R46a, Rn, R46f

- Everglades
- National Park

- N

- Gulf of
- Mexico

## Credit: NASA/JPL MISR Team

**Notes:**
L1B2 TOA data

## An anisotropy primer (1)

- Solar illumination is highly directional, especially under clear skies
- All surfaces, natural or artificial, and in particular water, soils, vegetation, snow and ice, are anisotropic
- Surface anisotropy is controlled by the structure and optical properties of the observed media
- Reflectance of geophysical media is bidirectional
- Specular reflectance and hot spot, Lambertian panel
- Atmospheric constituents also interact anisotropically with the radiation fields (Rayleigh, Mie scattering)
- Anisotropy is itself a spectrally-dependent property

## An anisotropy primer (2)

- Imaging instruments with a small IFOV sample the reflectance of the surface-atmosphere system in the direction of the sensor, measure the hemispherical-conical reflectance of the geophysical system
- These measurements thus depend on the particular geometry of illumination and observation at the time of acquisition
- all sensors, including ‘nadir-looking’, are affected
- applications that do not exploit anisotropy must nevertheless account for these effects
- unique information on the observed media (e.g., structural characteristics) can be derived from observations of these angular variations

## Illumination and observation geometry

- Illumination direction:
- Ω0 = [θ0, φ0]

- Observation direction:
- Ω = [θ, φ]

- μ0 = cos θ0

- μ = cos θ

- Ref: Vogt and Verstraete (2002) RPV IDL tool

## Nomenclature (1)

- Ref: Nicodemus et al. (1977) NBS Monograph

- Incoming

- Outgoing

## Nomenclature (2)

- BRDF: Bidirectional Reflectance Distribution Function, Units: [sr -1], non-measurable

- BRF: Bidirectional Reflectance Factor, BRDF normalized by equivalent reflectance of a Lambertian surface, non-dimensional, measurable in the laboratory as a biconical reflectance factor

- HCRF: Hemispherical Conical Reflectance Factor, Units: [N/D], common measurement

- Ref: Nicodemus et al. (1977) NBS Monograph

## Nomenclature (3)

- HDRF: Hemispherical Directional Reflectance Factor, single integral of BRDF on the incoming directions (i.e., direct + diffuse illumination)

- DHR: Directional Hemispherical Reflectance, single integral of BRDF on the outgoing directions (“black sky albedo”)

- BHR: Bi-Hemispherical Reflectance (also known as albedo or “white sky albedo”), double integral of BRDF

- Ref: Nicodemus et al. (1977) NBS Monograph

## Families of BRF models

- 3-D ray-tracing or radiosity models simulate the reflectance of realistic heterogeneous scenes (computationally expensive)
- 1-D turbid medium models simulate the reflectance of homogeneous scenes (computationally inexpensive)
- Parametric models represent the shape of the BRDF function without providing a physical explanation (computationally extremely fast)
- Appropriate inversion techniques should be selected (e.g., LUT for 3-D, non-linear iterative for 1-D, linear scheme for parametric)

## Anisotropy of heterogeneous systems

- 3-D radiation transfer models (e.g., Monte Carlo ray tracing) are required to simulate heterogeneous systems
- All plant elements are represented explicitly
- Input variables: size, shape, orientation and optical properties of each individual object

- Ref: Govaerts and Verstraete (1999) IEEE TGRS

## Anisotropy of homogeneous systems

- Plant canopies are represented as a ‘cloud’ of scatterers of finite dimension
- 1-D’ (vertical) radiation transfer models can simulate horizontally homogeneous systems, hot spot
- Input variables: number, size and orientation of leaves, leaf and soil optical properties, canopy height

- Ref: Gobron et al. (1997) JGR

## RPV parametric model

- Ref: Rahman et al. (1993) JGR

## Interlude...

- Play with the RPV-GUI tool

## MISR standard surface products

- Surface BRF, albedo (BHR and DHR), and DHR-based NDVI: Provisional since Aug. 2, 2002
- LAI/FPAR and DHR-PAR: Beta since Sep. 2002
- Martonchik et al. (1998) ‘Determination of land and ocean reflective, radiative, and biophysical properties using multiangle imaging’, IEEE TGARS, **36**, 1266-1281 (1998)
- Knyazikhin et al. (1998) ‘Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MISR data’, J. Geophys. Res., **103**, 32,239-32,256.
- Diner et al. (1999) ‘MISR Level 2 Surface Retrieval Algorithm Theoretical Basis’, JPL D-11401, Rev. D.

## Surface processing flowchart

- Ref: Martonchik et al. (1998) IEEE TGRS

- (Slope > 20°)

- (Assume RPV)

- (Angular integral)

- (Spectral integral)

- 16  16

- 1  1

- 16  16

- 1  1

- 1  1

- (Use CART and
- angular signature)

## MISR LEVEL 2 RETRIEVALS

**MISR LEVEL 2 RETRIEVALS**
**SUA PAN, BOTSWANA**
**August 27, 2000**

- Retrieved BRF
- Blue 46o aft BRF
- Green 46o aft BRF
- Red 46o aft BRF

- Retrieved BRF
- Nadir BRF
- 46o forward BRF (backscatter)
- 46o aftward BRF (forward scatter)

- Credit: NASA/JPL MISR Team

**Notes:**
L2 BRF retrievals

## MISR LEVEL 2 RETRIEVALS

**MISR LEVEL 2 RETRIEVALS**
**SUA PAN, BOTSWANA**
**August 27, 2000**

- Retrieved DHR
- Blue DHR
- Green DHR
- Red DHR

- Retrieved DHR
- Green DHR
- Red DHR
- NIR DHR

- Credit: NASA/JPL MISR Team

**Notes:**
L2 DHR retrievals

## DHR over Southern Africa, 6-week mosaic during SAFARI 2000

- (Paths: 165-183)

- RGB = NIR, Red, Green

- RGB = Red, Green, Blue

- Credit: NASA/JPL MISR Team

## MISR non-standard surface products

- VEGAS: FAPAR, canopy structure and heterogeneity
- Pinty et al. (2002) 'Uniqueness of Multiangular Measurements Part 1: An Indicator of Subpixel Surface Heterogeneity from MISR',  IEEE TGRS, MISR Special Issue, in print.
- Gobron et al. (2002) 'Uniqueness of Multiangular Measurements Part 2: Joint Retrieval of Vegetation Structure and Photosynthetic Activity from MISR',  IEEE TGRS, MISR Special Issue, in print.
- Widlowski et al. (2001)  'Characterization of Surface Heterogeneity Detected at the MISR/TERRA Subpixel Scale', GRL, 28, 4639-4642.

## An Optimized FAPAR algorithm (1)

- Traditional vegetation indices (e.g., NDVI) are subject to numerous perturbing influences, including atmospheric, soil and directional effects
- Elements exist to improve surface retrievals: canopy and atmospheric radiation models, bidirectional reflectance models, multispectral and multidirectional observations
- Exploit these tools to simultaneously rectify observations for aerosol scattering and directional effects, using MISR blue band and multiple cameras
- Estimate FAPAR from these rectified channels
- Similar algorithms for SeaWiFS, MERIS, GLI and VEGETATION (consistency and continuity)

- Credit: Gobron et al. (2002) IEEE TGRS

## An Optimized FAPAR algorithm (2)

- Construct large look-up table of simulated spectral and directional reflectances for a variety of surfaces and atmospheres (ToC, ToA and associated FAPAR training data sets)
- Express rectified red and nir channels as polynomials of (blue, red) and (blue, nir) measurements, and optimize coefficients so that rectified values match simulated ToC values
- Express FAPAR as polynomial of (rect. red, rect. nir) bands, and optimize coefficients so that values match simulations
- Apply polynomials to actual MISR data

- Credit: Gobron et al. (2002) IEEE TGRS

## Monitoring FAPAR with MISR (1)

- Denmark
- Composite for Sep. 2000 (2 acquisitions)
- Spatial resolution: ~300 m
- Input: blue, red and NIR at nadir
- Algorithm: VEGAS

- Credit: Gobron et al. (2002) IEEE TGRS

## Monitoring FAPAR with MISR (2)

- Australian east coast
- August 26, 2000 (1 acquisition)
- Spatial resolution: ~300 m
- Input: blue, red and NIR at nadir
- Algorithm: VEGAS

- Credit: Gobron et al. (2002) IEEE TGRS

## Characterizing heterogeneity

- Ref: Pinty et al. (2002) IEEE TGRS

## VEGAS: Using spectral and directional information

- Ref: Pinty et al. (2002) IEEE TGRS

## Saratov, Russia

- 31 May 2002 (top)
- 18 July 2002 (bottom)
- ‘True color’ MISR An (left)
- Red anisotropy (right):
- RGB = MISR Ca, An, Cf

- Source: http://www-misr.jpl.nasa.gov/gallery/galhistory.html

- Green: bell-shaped anisotropy
- controlled by soils and vertical plants

- Purple: bowl-shaped anisotropy
- controlled by bare soils

## Overview of AirMISR

- 1 camera pointable at ±70.5, ±60, ±45.6, ±26.1, 0°
- Spectral bands at 446, 558, 672, and 866 nm
- Spatial resolution: L1B2 data re-sampled at 27.5 m
- Image length: 9 – 26 km (0 –70°)
- Swath: 11 – 32 km (0 – 70°)
- Coverage: on request
- Data: LaRC DAAC

- Ref: http://www-misr.jpl.nasa.gov/mission/minst.html

## Atmospheric correction of AirMISR

## Mapping heterogeneity

- Ref: Pinty et al. (2002) IEEE TGRS

- 1.5

- 1.0

- 0.5

- Bell-shape

- Bowl-shape

- k

- AirMISR campaign, Konza Prairie, June 1999

## Field validation campaign

- Konza Prairie, June 2000
- A: Bare soil between trees
- B: Clearing between canopies
- C: Young corn field
- D: mixed vegetation
- E: Dry river bed
- F: Fence between two open fields
- G: Agriculture

- Ref: Pinty et al. (2002) IEEE TGRS

## Conclusion

- MISR’s Multidirectional spectral measurements offer new opportunities to document the state of geophysical systems and land surfaces in particular
- Surface and atmospheric products can be made more accurate because of the added directional constraint on inversion
- New products can be generated (e.g., to characterize heterogeneity), they could not be derived without directional measurements
- Future multidirectional sensors: MSG (launched yesterday), Polder on Adeos-II, MISR-2, SPECTRA