Centre for Geo-Information (CGI)

C e n t r e f o r G e o - I n f o r m a t i o n ( C G I )

Spectroradiometric Measurements From Physics to Applications

Michael Schaepman and Harm Bartholomeus

ISPRS – International Society for Photogrammetry and Remote Sensing

Mid-Term Symposium 2006 “Remote Sensing: From Pixels to Processes”

8-11 May 2006, ITC, Enschede (NL)

CGI Report 2006-01

1

Spectroradiometric Measurements

- From Physics to Applications

Instructors Michael Schaepman, Wageningen University, Centre for Geo-Information, NL Harm Bartholomeus, Wageningen University, Centre for Geo-Information, NL

Description

Field and laboratory measurements have become increasingly important to validate and calibrate remotely sensed data as well as supporting the systematic collection of spectral field data. The tutorial will be addressing on the one hand the physics of spectroradiometric measurements and on the other hand we will be demonstrating applications and perform a practical training with state-of-the-art instrumentation available during the tutorial. The physical part of the tutorial is devoted to the understanding of reflectance terminology, the calibration of field spectroradiometers, the measurement setup and plan, as well as discussing potential sources of uncertainty when performing radiometric measurements. The application part will focus on specific tasks to be performed when collecting data for various purposes, such as building a spectral database on leaf optical properties, vegetation canopy measurements, soil and mineral related applications, as well as calibration tasks for airborne and spaceborne instruments. The demonstration will include a session on how to perform measurements with available instrumentation, and discussing measurement strategies to minimize uncertainties of data acquisition. The presentation of the tutorial will be made available to the participants, including a list of papers, recommended reading, and web resources. The tutorial addresses all ISPRS Comm. VII Mid-Term Symposium participants, having a dedicated interest in field spectroradiometers. Participants may well have already built up a background on the use of such instruments but plan to expand their knowledge on either the physics or on the applications.

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Spectroradiometric MeasurementsFrom Physics to ApplicationsISPRS Mid-Term Symposium 2006Saturday, May 6th 2006, 13:30-17:00

Michael Schaepman, WUR (NL)Harm Bartholomeus, WUR (NL)

Schedule

Welcome 13:30 – 13:35Introduction 13:35 – 14:00Physics 14:00 – 14:40

Questions & Answers 14:40 – 15:00Break 15:00 – 15:20Applications 15:20 – 16:00

Questions & Answers 16:00 – 16:15Demonstrations 16:15 – 16:45

Questions & Answers 16:45 – 17:00Closure 17:00 – 17:00

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Outline

Welcome / ScheduleIntroduction

Spectroradiometric MeasurementsPhysics

Reflectance TerminologyCalibration / Measurement Plan / UncertaintySurface Anisotropy

ApplicationsInstrumentationVegetationSoils/MineralsCalibrationSpectral Databases

Demonstration

Introduction

Spectroradiometric MeasurementsBased on

• Kostkowski, H.J. (1997) Reliable Spectroradiometry, 1st ed. Spectroradiometry Consulting, La Plata, USA.

• Bartholomeus, H., and Schaepman, M.E. (2005) Lecture Notes, Wageningen University, NL

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SpectroVISZR

1957

Introduction

Source: Newton, I.: Opticks: or, a Treatise of the Reflexions, Refractions, Inflexions, and Colours of Light, Book I, Plate IV, Part I, Fig. 18, Sam Smith and Benj. Walford, St. Paul’s Church-yard, 1704 –Burndy Library

Sir Isaac Newton

(1642-1727)

Joseph von

Fraunhofer

(1787-1826)

Gustav Robert

Kirchhoff(1824-1887)

Robert Wilhelm

Bunsen(1811-1899)

Sir William Huggins

(1824-1910)

Spectraldispersion

Continuous spectrum,interrupted by dark lines

Explanation ofFraunhofer lines

Absorptionin gas

Composition ofastronomical objects

First handheldspectrometers

Introduction

Spectroscopy is the study of light as a function of wavelength that has been emitted, reflected or scattered from a solid, liquid, or gas.

The quantity measured is usually reflectance(expressed in %)

Spectroradiometry is the technology for measuring the power of optical radiations in narrow, contiguous wavelength intervals

The quantities measured are usually spectral irradiance and spectral radiance

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Introduction

Radiance (L) is the (energy) flux per unit projected area and per unit solid angle

L = (∂Φ) / (∂(A cos ø) ∂Ω) [W m-2 sr-1]

Where Φ is the flux (power, [W]), A the true, geometric area [m2], ø the angle from the surface normal at which the area is viewed, Ω the solid angle [sr]

Introduction

Spectroscopy is everywhereExobiology: in search for extraterrestrial lifeDesigning eye-friendly filters for new generation Xenon discharge lamp based headlightsRare earth elements doped Euro bills to prevent falsificationWeaving of silver strings into carpets to increase total reflectivity (to save illumination power)Unravel the composition of planets, moons, asteroids, and comets (as done on Mars, Mercury, Jupiter, Moon, Virtanen, etc.)Interaction measurement of polymeric surfaces with the environment

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Introduction

Spectroradiometric measurements are one of the least reliable of all physical measurements.

Henry Kostkowski, Reliable Spectroradiometry, 1997

Three major reasons for large errors in spectroradiometry are:The measurement is a multidimensional problem,The instability of measuring instruments and the standards used to calibrate these instruments are frequently 1% or more during the complete measurement process, andThe principles and techniques used for eliminating (or reducing) measurement errors due to this multidimensionality or instability have not been widely disseminated.

Introduction

Optical System

Background

Transmissions- medium Photons contributing

to the total signal

Object

esrsr

sr

sr

sr

ta

at

sr

i0 Exitance in Irradiance a Absorbed radiance sr Scattered/reflected radiance t Transmitted radiance e Emitted radiance

sr

sr

e

t

t

sr

tt

i0i0

i0

i1

i1

i2

i2

i2

i2

i2

i2

i3

i3

tt

a

srsr

sr

e

a

srsr

asr

sr

a sr

sr sr

sr sr

a

a

srsr

sr

a

t

sra

t

sr

sr

sr

sra

sr

sr

sr

a

e

e

e

e

e

sr

sr

srsr

a

Source

Contributing sources to a spectroradiometric measurement

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Introduction

Introduction

Source: HyEco’04 campaign, Wageningen and Millingerwaard, NL, 2004CGI course ‘Integration of GIS and Remote Sensing’, 2004MERCI programme, Bily Kriz, CZ, 2004

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Physics I

Geometrical Optical Reflectance NomenclatureBased on

• Nicodemus, F.E., J.C. Richmond, J.J. Hsia, I.W. Ginsberg, and T. Limperis, Geometrical Considerations and Nomenclature for Reflectance, pp. 52, National Bureau of Standards, US Department of Commerce, Washington, D.C., 1977.

• Martonchik, J.V., C.J. Bruegge, and A. Strahler, A review of reflectance nomenclature used in remote sensing, Remote Sensing Reviews, 19, 9-20, 2000.

• Bruegge, C.J., Schaepman, M., Strub, G., Beisl, U., Itten, K.I., Demircan, A., Geiger, B., Helmlinger, M.C., Martonchik, J., Abdou, W.A., Painter, T.H., Paden, B.E., & Dozier, J., Field Measurements of Bi-Directional Reflectance. In Reflection Properties of Vegetation and Soil with a BRDF Database (eds M. von Schoenermark, B. Geiger & H.-P. Roeser), pp. 195-224. Wissenschaft und Technik Verlag, Berlin, 2004.

• Schaepman-Strub, G., Schaepman, M., Painter, T., Dangel, S., and Martonchik, J., Reflectance Terminology in Optical Remote Sensing, Remote Sensing Environment, in print, 2006.

Radiometric NomenclatureBased on

• Palmer, J.M., Radiometry and Photometry FAQ, http://www.optics.arizona.edu/Palmer/rpfaq/rpfaq.pdf, Univ. Arizona, p. 14, 2003

Geometric-optical Reflectance Nomenclature

Spectrometer data and products are constantly improved in qualityOptimization is usually performed at

Radiance level (enhanced calibration concepts, vicarious calibration, etc.) with uncertainties approaching < 4%Reflectance level (atmospheric correction) with uncertainties approaching < 5%Product level (sophisticated integration of various sources, assimilation, etc.) uncertainties approaching < 10%

But rarely on terminology, where uncertainties can still be >> 10%

(‘we conclude that by measuring the BRDF, the directional error isminimized’ …)

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Underlying Assumptions

All remotely sensed data depend on sun and view geometry, as well as the opening angle of the illumination and the observation.

Geometric-optical nomenclature concentrates on the opening angle of the illumination.

All reflectance measurements under natural conditions include a diffuse fraction, being a function of atmospheric conditions, the topography, the surroundings, and the wavelength.

F ≠ F (Factor ≠ Function)

Terminology for Radiance and Reflectance

BihemisphericalHemispherical-conicalHemispherical-directionalHemispherical

Conical-hemisphericalBiconicalConical-directionalConical

Directional-hemisphericalDirectional-conicalBidirectionalDirectional

HemisphericalConicalDirectionalIncoming /Reflected

Measurable QuantitiesConceptual Quantities

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Derived ProductsMeasurement

Hemispherical-Conical

Reflectance

HDRFHemispherical-

DirectionalReflectance

Factor

BHRBihemispherical

Reflectance

BRFBidirectionalReflectance

Factor

BRDFBidirectionalReflectanceDistribution

Function

DHRDirectional-

HemisphericalReflectance

Data Processing of Reflectance Quantities

Measurable QuantitiesConceptual Quantities

Some Conversions …

HDRF = R(θi,φi,2π;θr,φr ) =dΦr (θi,φi,2π;θr,φr )dΦr

id (θi,φi,2π )=

cosθr sinθrLr (θi,φi,2π;θr,φr )dθrdφrdAcosθr sinθrLr

id (θi,φi,2π )dθrdφrdA

=Lr (θi,φi,2π;θr,φr )Lrid (θi,φi,2π )

=f r (θ i,φi;θr,φr )dΦi(θ i,φi)2π∫

(1/π )dΦi(θ i,φi)2π∫=

fr(θ i,φ i;θr,φr)cosθi sinθ iLi(θi,φi)dθidφi0

π / 2∫0

2π∫(1/π ) cosθi sinθiLi(θi,φi)dθidφi0

π / 2∫0

2π∫

),(),;,(),;,(

iiidr

rriirrrii d

dRBRFφθφθφθφθφθ

ΦΦ

== =cosθr sinθrdLr (θi,φi;θr,φr )dθrdφrdA

cosθr sinθrdLrid (θi,φi)dθrdφrdA

=dEi(θi,φi)dLr

id (θi,φi)⋅dLr (θi,φi;θr,φr)dEi(θi,φi)

=fr (θi,φi,θr,φr )f rid (θi,φi)

= π ⋅ f r(θi,φi;θr,φr)

R(θi,φi,ω i;θr,φr,ωr;λ)Hemispherical-

ConicalReflectance

HDRFHemispherical-

DirectionalReflectance

Factor

BRFBidirectionalReflectance

Factor

BRDFBidirectionalReflectanceDistribution

Function

BRDF = fr(θi,φi;θr,φr;λ) = dLr(θi,φi;θr,φr;λ)dEi(θi,φi;λ)

[sr−1]

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Black Spruce Forest CalculationsHDRF (PARABOLA measurements, Deering, 1995)

BRF

RPV fit parameters

HDRF, BRFBHR, DHR

Atmospheric correction (Atmospheric correction (TanreTanre, 1983), 1983)

RahmanRahman--PintyPinty--VerstraeteVerstraete (RPV) (RPV) BRDF modelBRDF model

RPV model run,RPV model run,irradiance scenarios (d=1.0 irradiance scenarios (d=1.0 --> d = 0.0)> d = 0.0)

BOREAS Information System (http://www-eosdis.ornl.gov/BOREAS)

Black Spruce Forest ResultHDRF versus BRF (650-670nm, solar zenith = 30deg)

60 40 20 0 20 40 600.01

0.015

0.02

0.025

0.03

0.035

backward <- view zenith -> forward

BRF d=1

HDRF d=0

8080

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Black Spruce Forest Result

BRF and HDRF Angular Distributions (650 - 670 nm, solar zenith = 30deg)

0.010

0.039

BRFBRFd = 1.0d = 1.0

HDRFHDRFd = 0.0d = 0.0





Conclusions

All remote sensing data depend on sun and view angle, as well as the opening angle of the illumination and the observation.All reflectance measurements under natural conditions include a diffuse fraction ( f (atm., top., sur.,wvl)), usually not accounted for in atmospheric corrections. -> Directional data measured outdoors are not (!) ‘BRDF’ data ...The influence of the diffuse component on reflectance depends on the scattering mechanisms of the surface. It highly affects single angle observations (incl. nadir observations), and leads to significant variations of the albedo. The basis of proper use of reflectance data is a standardized nomenclature.Standardization of nomenclature for reflectance quantities is urgent, including the definition of used quantities in every scientific publication.

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Physics IICalibration / Measurement Plan / Uncertainty

Based on• Schaepman, M. (1998) Calibration of a Field Spectroradiometer - Calibration and

Characterization of a Non-Imaging Field Spectroradiometer Supporting Imaging Spectrometer Validation and Hyperspectral Sensor Modelling, Univ. Zurich, Remote Sensing Series, Vol. 31, Zurich.

• Schaepman, M.E. & Dangel, S. (2000) Solid laboratory calibration of a nonimagingspectroradiometer. Applied Optics, 39, 3754-3764.

• Fox, N., Aiken, J., Barnett, J.J., Briottet, X., Haigh, J.D., Kieffer, H.H., Lean, J., Pollock, D.B., Schaepman, M., Shine, K.P., Thome, K.J., Zalewski, E., Carvell, R., Frohlich, C., Groom, S.B., Hagolle, O., Quinn, T., Sandford, M.C.W., Schmutz, W.K., Teillet, P.M., & Verstraete, M.M. (2002) Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS). In Sensors, Systems, and Next-Generation Satellites VI (eds H. Fuijsada, J.B. Lurie, M.L. Aten & K. Weber), Vol. 4881, pp. 395-406. SPIE.

• Schaepman, M., Dangel, S., Kneubuehler, M., Schlaepfer, D., Bojinski, S., Brazile, J., Koetz, B., Strub, G., Kohler, P., Popp, C., Schopfer, J., & Itten, K. (2002) Quantitative Field Spectroscopic Measurement Instrumentation and Techniques. In 1st EPFS Workshop on Field Spectrometry (ed E.J. Milton), CD-ROM. NERC, Southampton.

Measurement Plan

The measurement plan is the key cost and success factor for any calibration and contains the following elements:

Detailed description of the quantity to be measured including the accuracy desired

• Quantity to be measured• Wavelengths to be measured• Etc.

Identification of potential error sources and their estimation of their magnitude (can also be according to specifications or literature search)

• Noise to signal• Nonlinearity• Wavelength instability• Etc.

Selection of the radiance standardSelection of the spectroradiometerSelect the wavelength standard

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Measurement Plan (contd.)

Characterize the spectroradiometer for all potential errors• SNR, NER, and dark current• Wavelength characterization• Nonlinearity characterization• Etc.

Select and characterize the measurement set-upSelect the measurement designAcquire the data and calculate the quantity desiredPrepare the uncertainty report and report on all sources of uncertainty

• Errors of ‘Type A’ (Statistical analysis) or ‘Type B’ (‘Educated guesses’)• Degrees of freedom• Combined uncertainty• Expanded uncertainty• Un-identified sources of uncertainty

Calibration Using Irradiance Standards

Optronic Labs Integrating Sphere, ThermoOriel Power Supply,(NIST calibrated), Intensity Control System, Lamp ,Stabilized Power Supply Housing, Condenser,

FEL 1000 W Lamp

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Calibration to Radiances and WavelengthLabsphere Diffuse Labsphere UV/VIS Wavelength Reflectance Standards Calibration Standards(Spectralon) (Rare Earth)

Radiance Levels

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Calibration using Wavelength-Standards100

90

80

70

60

50

40

30

20

10

0

Ref

lect

ance

[%]

200018001600140012001000800600400

Wavelength [nm]

2.0

1.5

1.0

0.5

0.0

Relative A

bsorptance []

Dysprosium NIST Calibrated Primary Absorption Features

… and the corresponding Measurement

1.6

1.4

1.2

1.0

0.8

0.6

0.4

Rel

ativ

e A

bsor

ptan

ce [a

rbitr

ary

units

]

10501000950900850800750Wavelength [nm]

Reference Dysprosium Spectrum NIST Calibrated Absorption Peaks GER3700 Measurement ASD FieldSpec Measurement

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Spectral Resolution Terminology

Tuneable Dye Laser Calibration

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… and the Corresponding Measurement

1.0

0.8

0.6

0.4

0.2

0.0

Nor

mal

ized

Res

pons

e

596.5596.0595.5595.0594.5594.0593.5593.0592.5592.0591.5591.0

Wavelength [nm]

-0.2-0.10.00.10.2

Res

idua

ls

Dye Laser Gaussian Fit Residuals of Fit

FWHM 2.15 nm

593.6 nm

Spectral Changes of the Atmosphere

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Field Reflectance Standard (Spectralon)

Calibration uncertainty of reflectance

Additional reflectance factor uncertainty based on Spectralon non-lambertianbehaviour

Values, Errors, and UncertaintiesQuantity Uncorrected Uncorrected Correction Result Remaining Value of Values of Final

Observations arithmetic for all reco- of measure- error measurand measurand result ofmean of gnized sys- ment (unknow- (unknow- due to incom- measure-observations tematic able) able) plete defini- ment

effects tion (unknown)

Value(not to scale)

Variance(not to scale)

(singleobservation)

(arithmeticmean)

(does not include variancedue to incomplete definition

of measurand)

Incr

easi

ng v

alue

Ref. ISO: Guide to the Expression ofUncertainty in Measurement, 1995

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Uncertainty Report

Reporting of absolute radiance calibration including all remaining uncertainties

Reporting of traceability to a standard (NIST) in reflectance including the expanded uncertainties

1.70

1.60

1.50

1.40

1.30

1.20

Rad

ianc

e [W

/ (m

2 sr

nm)]

12001180116011401120110010801060104010201000980960940920900Wavelength [nm]

Sphere Data Si Detector PbS1 Detector (eq 6.10) PbS1 Detector (eq. 6.11)

28

26

24

22

20

18

16

Ref

lect

ance

[%]

220021802160214021202100Wavelength [nm]

230022802260224022202200

Y = y ± 4.85% Y = y ± 6.16%

Measurand

Physics III

Surface AnisotropyBased on

• Strub, G., Schaepman, M.E., Knyazikhin, Y., & Itten, K.I. (2003) Evaluation of Spectrodirectional Alfalfa Canopy Data Acquired During Daisex'99. IEEE Transactions on Geoscience and Remote Sensing, 41, 1034-1042.

• Bruegge, C.J., Schaepman, M., Strub, G., Beisl, U., Itten, K.I., Demircan, A., Geiger, B., Helmlinger, M.C., Martonchik, J., Abdou, W.A., Painter, T.H., Paden, B.E., & Dozier, J. (2004). Field Measurements of Bi-Directional Reflectance. In Reflection Properties of Vegetation and Soil with a BRDF Database (eds M. von Schoenermark, B. Geiger & H.-P. Roeser), pp. 195-224. Wissenschaft und Technik Verlag, Berlin.

• Rast, M., Baret, F., van de Hurk, B., Knorr, W., Mauser, W., Menenti, M., Miller, J., Moreno, J., Schaepman, M.E., & Verstraete, M. (2004). SPECTRA - Surface Processes and Ecosystem Changes Through Response Analysis. ESA Publications Division, ESA SP-1279(2), Noordwijk.

• Dangel, S., Verstraete, M., Schopfer, J., Kneubühler, M., Schaepman, M.E., & Itten, K.I. (2005) Toward a Direct Comparison of Field and Laboratory Goniometer Measurements. IEEE Transactions on Geoscience and Remote Sensing, 43, 2666-2675.

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Spectrodirectional Measurements at Pixel Level

Source: M. Rast, Ed., SPECTRA – Surface Processes and Ecosystem Changes Through Response Analysis, ESA SP-1279(2), 2004, pp. 66; Data: J. MorenoStrub, G., Schaepman, M.E., Knyazikhin, Y., & Itten, K.I. (2003) Evaluation of spectrodirectional Alfalfa canopy data acquired during DAISEX '99. Ieee TGRS, 41, 1034-1042.

λ

α

Polar Plot Anisotropy Factors of Alfalfa at 560 nm

SZ = 17°13:56

SZ = 44°17:10

FIGOS Measured SAIL Modelled

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Specular Surface Reflection

θ θ

Where does surface anisotropy comes from?

Shadows – geometrical-optical anisotropy

Volume Scattering

Sunglint

θ θ

Specular Reflectance

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Sunglint and Shadowing

Hot Spot in Spectrometer Data

Solar Azimuth 170/180° | Solar Zenith 17°

-30°

+30°

-17°

NadirScanningDirection

Scanning Direction

AirplaneMotion

Nadir

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0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

min 36 nadirmin 55 plus 55plus 36

NDVIdirectionoffnadirnadir

-1,2

-1,0

-0,8

-0,6

-0,4 PRIdirectionoffnadirnadir

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

min 36min 36 nadirnadirmin 55min 55 plus 55plus 55plus 36plus 36

ARI1directionoffnadirnadir

Normalized difference Vegetation index

Photochemical

reflectance index

Anthocyanin

Reflectance index

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

min 36min 36 nadirnadirmin 55min 55 plus 55plus 55plus 36plus 36

SRIdirectionoffnadirnadir

Simple Ratio index

Directional Sensitivity of Vegetation Indices

Influence of far viewing angles: Backscatter direction (+55°) values compared to nadir values:

Broadband NarrowbandNDVI: +8% PRI: -102% SRI: +34% ARI1:+ 66%

β-z

k

u

β

O

θ1

θ2

Directional Field/Laboratory Measurements

Source:Bruegge, C.J., Schaepman, M., Strub, G., Beisl, U., Itten, K.I., Demircan, A., Geiger, B., Helmlinger, M.C., Martonchik, J., Abdou, W.A., Painter, T.H., Paden, B.E., & Dozier, J. (2004). Field Measurements of Bi-Directional Reflectance. In Reflection Properties of Vegetation and Soil with a BRDF Database, Vol. 1, pp. 195-224. Wissenschaft und Technik Verlag, Berlin.

4 m

2 m

Target

Light source(Sun)

Sled withradiometerZenithal arc

Azimuthal arc

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Target S10: Bare Soil

18:17

≈9:58

13:40

17:12

Target V17: Alfalfa (Lucerne)

9:56

13:56

17:10

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Target V25: Non Irrigated Barley

9:55

14:10

10:39

≈ 16:50

Conclusions

A Lambertian reflector is a physical concept, and does almost not exist in the real world (e.g., integrating spheres are seen as being Lambertianillumination sources)One of the best approximations of a Lambertian reflector is a Spectralon(PTFE coating) panelThe ‘F’ in BRDF stands for ‘Function’ and a function can never be measuredAll natural and artificial surfaces exhibit little to significant anisotropyDirectionality in a remotely sensed signal is not only dependent from the illumination/observation angle constellation and the surface anisotropy, but also from the atmospheric composition!In a laboratory environment, issues are even more complex, due to the absence of diffuse light (=a situation not present in the real world)

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Applications

InstrumentationVegetationSoils/MineralsCalibrationSpectral Databases

Instrumentation

Field Spectrometer EquipmentBased on

• Analytical Spectral Devices• Ocean Optics• GER (Instaar Gaia Env. Research, was Geophysical & Env. Res. Corp)• Yankee Systems• Reagan Instruments• Li-Cor• Labsphere

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Field Spectroradiometers

Forest Measurements

1 Differential GPS for geocoding2 MFR-7 sunphotometer (spectral diffuse and direct irradiance)3 Spectral reflectance (‘endmember’ data collection)4 Forest downward spectral transmittance5 Understory spectral reflectance and Spectralon reflectance

1

2

3

4 5

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Irradiance Measurements

Goniometric Measurements

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Leaf Scale Measurements

Integrating SpherePlant Probe

Plot Scale Measurements

Predefined plots with predefined plant species composition measurement and controlled environment measurements.

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

400 900 1400 1900 2400

wavelength (nm)

refle

ctan

ce

plot 4plot 5plot 11plot14

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Canopy Scale Measurements

Stratified random sampling of a ‘homogeneous’target

Canopy Scale Measurements II

Droevendaal Experimental Farm, Wageningen (29.7.2004)

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Supporting Measurements

Fresh matter weightDry matter weightDry matter contentTotal N & P contentPosition of TreesWeather

Supporting Measurements II

Effective LAI = 2.2Sky/Soil = 21%Average Leaf Inclination Angle = 52 degClumping Factor = 0.51 (@ 30 deg)

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All together!

Measured Parameters IOptical Properties

• Leaf Reflectance• Leaf Transmittance• Soil Reflectance• Understory Reflectance• Canopy Reflectance• Canopy Transmittance• Solar Irradiance• Diffuse Irradiance

Chemical Properties• Chl a/b• Water content• CO2• Wet/dry Biomass

Measure Parameters IIStructural Properties

• Tree height• Crown height• Crown shape• Crown closure• Leaf Angle Distribution (LAD)• Leaf Area Index (LAI)• Tree density• Fraction of branches• Fraction of senescent wood• Fractional cover / gap fraction

1 2

3

4

5

6

All together!

Tree climber and branch cuttingCO2 gassing experimentNeedle, bark, and branch reflectance measurementNeedle transmission measurementHemispherical photo (Gap fraction)Hemispherical photo (fCover, LAI)

Photos: M. Schaepman/B.Koetz for SPREAD, 2002

3

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Applications

A Summer Wheat Field in Pictures

9.4.99 2.5.99 18.5.99

16.6.99 24.6.99 5.7.99

17.7.99 26.7.99 12.8.99

DC11 DC13 DC30

DC59 DC69 DC71-75

DC75 DC75-85 DC87

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… and it’s Spectroradiometric Expression60

50

40

30

20

10

0

Per

cent

Ref

lect

ance

[%]

200015001000500Wavelength [nm]

summer wheat 9.4.1999

60

50

40

30

20

10

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Decay of a Ficus benjamina L. Leaf

Source:Bartholomeus, H., and Schaepman M. (2004)Decay of Ficus benjamina L. in 10 minutes steps over 8 hrs, unpublished

Each time step is 10 mins., total duration 8 hrsMeasurement is reflectance plus reflected transmittance

Undisturbedleaf

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Biochemicals Present in Vegetation Spectra

Source: Schaepman, M., Koetz, B., Schaepman-Strub, G., Itten K., Spectrodirectional Remote Sensing for the Improved Estimation of Biophysical and –chemical Variables: Two Case Studies, JAG, accepted, 2004

Biochemical Compounds of Interest in Vegetation

Source: Ustin, S., Zarco-Tejada, P., Jacquemoud, S., Asner, G., Remote Sensing of the Environment: State of the Science and New Directions, in: Manual of Remote Sensing, 3rd ed.,Vol. 4, p. 696, 2004

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Vegetation

Vegetation BiochemistryBased on

• Malenovsky, Z., Cudlin, P., Albrechtova, J., Clevers, J.G.P.W., Schaepman, M., & Moravec, I. (2006 (in print)) Applicability of the PROSPECT model for Norway spruce needles. International Journal of Remote Sensing.

• Malenovsky, Z., Martin, E., Homolova, L., Pokorny, R., Schaepman, M.E., Gastellu-Etchegory, J.-P., Zurita Milla, R., Clevers, J.G.P.W., & Cudlin, P. (2005 (in revision)) Influence of forest canopy structure simulated using the Discrete Anisotropic Radiative Transfer (DART) model on the retrieval of spruce stand LAI. Remote Sensing of Environment.

• Ustin, S., Asner, G., Gamon, J., Huemmerich, K., Jacquemoud, S., Schaepman, M.E, & Zarco-Tejada, P. (2006) Retrieval of Quantitative and Qualitative Information about Plant Pigment Systems from High Resolution Spectroscopy. In IGARSS, IEEE, Denver (USA).

Case studyQuantitative remote sensing of Norway spruce crowns under

multiple stress

Centre for Geo-Information

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Acute stress - discolorations

Yellowing and rusting – water stress – ŠumavaMts. (summer 2003)

Chronic stress - crown transformation

b

a

c

Norway spruce functional crown parts:a/ juvenile partb/ production partc/ saturation part

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Spruce needle optical properties - method


JUVENILE Branch

PRODUCTION Branch

South

Only Primary structurePP (JP)

Primary and Secondary structure

PPS

Branch of the production crown part

Li-Cor integrating sphere Li 1800-12

Spruce needle optical properties - results


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PROSPECT – leaf radiative transfer model

PROSPECT calculates the leaf hemispherical reflectance and transmittance from 400 to 2500 nm. Scattering is described by the refractive index of leaf materials (n) and by a parameter characterizing the leaf mesophyll structure (N).Absorption is calculated from the concentrations of the biochemical compounds and the specific absorption corresponding coefficients.PROSPECT 3.01 inputs

N parameter of mesophyll layersCha+b concentration [μg/cm2 ]Water thickness Cw [cm] Dry matter content Cm [g/cm2]

PROSPECT – adjustment at the NIR plateauInversion of N (116 training samples): VIS = <550; 680> nm; NIR = <770; 900> nm

C

Verification of the adjusted PROSPECT (48 testing samples)

C+1

C+2

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Applications

Soils and MineralsBased on

• http://speclab.cr.usgs.gov -> http://speclab.cr.usgs.gov/spectral-lib.html

• Bartholomeus, H., Epema, G., & Schaepman, M.E. (2006 (accepted)) Iron content discrimination in soil. International Journal of Applied Earth Observation and Geoinformation.

Clark, R. N., Chapter 1: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, in Manual of Remote Sensing, Volume 3, Remote Sensing for the Earth Sciences, (A.N. Rencz, ed.) John Wiley and Sons, New York, p 3- 58, 1999.

Minerals

Dalton, J.B., T.V.V. King, D.J. Bove, R.F. Kokaly, R.N. Clark, J.S. Vance and G.A. Swayze, Distribution of Acid-Generating and Acid-Buffering Minerals in the Animas River Watershed as Determined by AVIRIS Spectroscopy Proceedings if the ICARD 2000 Meeting, May 21-24, 2000, Denver Colorado.

Distribution of Acid-Generating and Acid-Buffering Minerals

in the Animas River Watershed as Determined by AVIRIS Spectroscopy

Single-band AVIRIS image of Silverton scene with pyrite-weathering products superimposed in colors, as mapped by Tetracorder V3.4. Mineral assemblagesindicative of acid runoff superimposed

on a single-plane AVIRIS band image to highlight effects of acid-generating minerals on stream quality. Outcrops of pyrite-weathering sequence minerals do not fully correlate with poor water quality. Iron hydroxide precipitates are apparentlining stream edges in this image

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Soil SpectroscopySoil Spectroscopy

Ben-Dor and Heller, 2005: Optical Approach for Soil Survey MissionsFirst Joint Dutch-Israeli workshop on Spatial, Temporal & Spectral Scale in SVAE, Wageningen Sept 14-16 2005

Predication

Validation

Ben-Dor and Heller, 2005: Optical Approach for Soil Survey MissionsBen-Dor and Heller, 2005: Optical Approach for Soil Survey MissionsFirst Joint Dutch-Israeli workshop on Spatial, Temporal & Spectral Scale in SVAE, Wageningen Sept 14-16 2005

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Spectral Measurement on an Open profileSpectral Measurement on an Open profile

ASD field spectrometer

Saves lab time, needs a profile exposure

Problems: Need open profile. Spectral diversity


λ

R

Lamp

Fiber

Mirror

Spectrometer

OM = 2%

Solution:Solution: Building a spectral probe for underground sensingBuilding a spectral probe for underground sensing


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Practical Measurement Practical Measurement

λ

R

Drill

White Reference Ben-Dor and Heller, 2005: Optical Approach for Soil Survey MissionsBen-Dor and Heller, 2005: Optical Approach for Soil Survey MissionsFirst Joint Dutch-Israeli workshop on Spatial, Temporal & Spectral Scale in SVAE, Wageningen Sept 14-16 2005

Soil SpectroscopyProblem:

Quantitative retrieval of soil parameters in areas with fractional vegetation cover

Olive trees

Iron rich soil

ROSIS: RGB = 60, 40, 20Bartholomeus, H.M., Epema, G.F.,Schaepman, M.E. 2005:

Using imaging spectroscopy for the quantitative determination of

soil iron content in partially vegetated areas

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Iron Content Lab Measurements

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

350 850 1350 1850 2350Wavelength [nm]

Ref

lect

ance

[-]

7.114.519.5

Iron mapping: final result

0 20 Iron Content [mass %]

fCover < 5%: high reliabilityfCover 5-50%: medium reliability

fCover > 50%: no mapping

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Application

CalibrationBased on

• Strobl, P., Mueller, A., Schlaepfer, D., & Schaepman, M.E. (1997) Laboratory calibration and inflight validation of the Digital Airborne Imaging Spectrometer DAIS 7915 for the 1996 flight season. In Algorithms for Multispectral and Hyperspectral Imagery III (eds A.E. Iverson & S.S. Shen), Vol. 3071, pp. 225-236. SPIE, Orlando, FL.

• Kneubühler, M., Schaepman, M.E., Thome, K.J., & Schläpfer, D.R. (2003) MERIS/ENVISAT vicarious calibration over land. In Sensors, Systems, and Next-Generation Satellites VII (eds R. Meynart, S.P. Neeck, H. Shimoda, J.B. Lurie & M.L. Aten), Vol. 5234, pp. 614-623. SPIE, Barcelona.

• Zurita Milla, R., Clevers, J., Schaepman, M., & Kneubühler, M. (2006 (in print)) Effects of MERIS L1b radiometric calibration on regional land cover mapping and land products. International Journal of Remote Sensing.

Test Site Selection

Railroad Valley Playa, Nevada, USA38° 32' 31.2" N, 115° 43' 47.4" W, 1435 m asl.

Used for various calibration activitiesLandsat TM, ETM+, MODIS, ASTER, etc.

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Railroad Valley Playa MERIS Data Take

Date: 22 August 2002Time: 18:02 UTCRGB Color Composite(R:7, G:5, B:2)

MER_FR_1PNIPA20020822_180221_000000872008_00442_02500_0031.N1

Railroad Valley Atmosphere Characterization

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Oregon Wildfires Visible in MERIS DataMER_FR_1PNIPA20020812_11630_000000982008_00299_02357_0082.N1 Date: 12 August 2002

Time: 17:53 UTCData not used due to increased aerosol optical thickness

Vicarious Calibration Railroad Valley Playa

AA

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Standard Solar Spectrum Choice

900850800750700650600550500450400Wavelength [nm]

0.20

0.19

0.18

0.17

0.16

0.15

0.14

0.13

0.12

0.11

0.10

0.09

0.08

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0.06

TOA

at s

enso

r rad

ianc

e [W

/(m2 s

r nm

)]

1.41.21.00.80.6

Rat

io []

MERIS TOA bands (incl. FWHM) MERIS TOA radiance (± 1 stdev) ASD ground reflectance modelled to TOA radiance using MODTRAN sun ASD ground reflectance modelled to TOA radiance using Thuillier 2002 sun Ratio of MODTRAN sun to Thuillier 2002 sun

MERIS data:MER_FR__1PNIPA20020822_180221_000000872008_00442_02500_0031.N1

MODTRAN standard solar irradiance vs. Thuillier 2002 solar irradiance

Mean deviation (400 nm - 900 nm): 4.66%

Source:Kneubühler, M., Schaepman, M.E., Thome, K.J., & Schläpfer, D.R. (2003) MERIS/ENVISAT vicarious calibration over land. In Sensors, Systems, and Next-Generation Satellites VII, Vol. 5234, pp. 614-623. SPIE, Barcelona.

Application

Spectral Databases Free software to create your own database: http://www.cstars.ucdavis.edu/software/sams

MineralsUSGS (http://speclab.cr.usgs.gov/spectral-lib.html)

Caltech (http://minerals.caltech.edu/FILES/Index.htm)

Vegetationhttp://teledetection.ipgp.jussieu.fr/opticleaf/

Sensor specificASTER (http://speclib.jpl.nasa.gov/)

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USGS

SAMS

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LinksSpectroscopy

http://fsf.nerc.ac.uk/http://fieldspectroscopy.com/

SoftwareSAMS: http://www.cstars.ucdavis.edu/software/samsSpecPR: http://speclab.cr.usgs.gov/specpr.html

Supporting documents for this workshophttp://skgr0103.wur.nl/~barth001/fieldspectroscopy/

Thank you for your attention!

© Wageningen UR

Documents

Centre for Geo-Information (CGI)