Characterizing Land Surfaces from MISR Measurements

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’AquilaThursday August 29, 2002

OutlineOutline

• 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 MISROverview 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

Credit: NASA/GSFC/LaRC/JPL MISR Team

Multiangle animation

Emigrant Gap Fire,California

13 August 2001

79 km

ToA anisotropy from MISR (1)ToA anisotropy from MISR (1)

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

Shikoku Island, JapanApril 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 Botswana25 August 2000

Nadir BlueNadir Green Nadir Red

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

Nadir GreenNadir RedNadir NIR

Credit: NASA/JPL MISR Team

ToA anisotropy from MISR (3)ToA anisotropy from MISR (3)

Saskatchewanand 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)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

LakeOkeechobee

Farmland

RGB = R, G, B RGB = R46a, Rn, R46f

EvergladesNational Park

N

Gulf ofMexico

nadirtrue color

nadir near-infraredfalse color

multi-anglefalse colorfwd scatter, nadir, backscatter

Gulf coast wetlands along thePascagoula, Mobile-Tensaw,and Escambia Rivers arespectrally similar to surroundingvegetation but have a distinctiveangular signature.

Mississippi,Alabama, FL15 October 2001

Differentiatingsurface vegetationvia angular signatures

Credit: NASA/JPL MISR Team

An anisotropy primer (1)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)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 geometryIllumination 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)Nomenclature (1)

Ref: Nicodemus et al. (1977) NBS Monograph

Incoming Outgoing

Nomenclature (2)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)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 modelsFamilies 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 systemsAnisotropy 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 systemsAnisotropy 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 modelRPV parametric model

Ref: Rahman et al. (1993) JGR

),,,,( 00 λλλλλ ρρρ cHG

v kF ΘΩΩ=

),,(),,(),,(),,,,( 0302010 λλλλλλ ρθθρ cvHG

vvcHG

v ffkfkF ΩΩΘΩΩ=ΘΩΩ

λ

λ

θθθθθθ λ k

v

kv

v kf −

−

+= 1

0

10

01 )cos(cos

)cos(cos),,(

2/32

2

02 ])(cos21[

)(1),,(

HGHG

HGHG

v gf

λλ

λλ Θ+Θ+

Θ−=ΘΩΩ

Gf c

cv +−

+=ΩΩ11

1),,( 03λ

λρρ

Interlude…Interlude…

• Play with the RPV-GUI tool

MISR standard surface productsMISR 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 flowchartSurface 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 andangular signature)

MISR LEVEL 2 RETRIEVALSSUA PAN, BOTSWANAAugust 27, 2000

Retrieved BRF

Blue 46º aft BRFGreen 46º aft BRFRed 46º aft BRF

Retrieved BRF

Nadir BRF46º forward BRF (backscatter)46º aftward BRF (forward scatter)

Credit: NASA/JPL MISR Team

MISR LEVEL 2 RETRIEVALSSUA PAN, BOTSWANAAugust 27, 2000

Retrieved DHR

Blue DHRGreen DHRRed DHR

Retrieved DHR

Green DHRRed DHRNIR DHR

Credit: NASA/JPL MISR Team

DHR over Southern Africa, 6-week mosaic during SAFARI 2000(Paths: 165-183)

RGB = NIR, Red, GreenRGB = Red, Green, Blue

Credit: NASA/JPL MISR Team

MISR non-standard surface productsMISR 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)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)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)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)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 heterogeneityCharacterizing heterogeneity

Ref: Pinty et al. (2002) IEEE TGRS

3-D

Bell-shape

k=1.18

IPA

Bowl-shape

k=0.65

VEGAS: Using spectral and directional information

VEGAS: Using spectral and directional information

Ref: Pinty et al. (2002) IEEE TGRS

Saratov, Russia31 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 anisotropycontrolled by soils and vertical plants

Purple: bowl-shaped anisotropycontrolled by bare soils

Overview of AirMISROverview 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 AirMISRAtmospheric correction of AirMISR

SALINA, KSJuly 1999

Top-of-atmosphereImage (70º)

Rayleigh-corrected

Rayleigh + aerosolcorrected

Mapping heterogeneityMapping heterogeneity

Ref: Pinty et al. (2002) IEEE TGRS

1.5

1.0

0.5

Be

ll-sh

ap

eB

ow

l-sh

apek

AirMISR campaign, Konza Prairie, June 1999

Field validation campaignField 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

ConclusionConclusion

• 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