NASAReferencePublication1139

1985

fU/ ANational Aeronauticsand Space Administration

Scientific and TechnicalInformation Branch

N85-30450

Spectral Reflectances of

Natural Targets for Use

in Remote Sensing Studies

David E. Bowker

and Richard E. Davis

Langley Research Center

Hampton, Virginia

David L. Myrick,

Kathryn Stacy,

and William T. Jones

Computer Sciences Corporation

Hampton, Virginia

i

1. Report No. I 2. Government Accession No. 3. Recipient's Catalog No.

NASA RP- 1139 i4. Title and Subtitle

Spectral Reflectances of Natural Targets for

Use in Remote Sensing Studies

7. Author(s)

David E. Bowker, Richard E. Davis, David L. Myriek,

Kathryn Stacy, and William T. Jones

9. Performing Organization Name and Address

NASA Langley Research CenterHampton, VA 23665

12. Sponsoring Agency Name and Address

National Aeronautics and Space AdministrationWashington, DC 20546

5. Report Date

June 1985

6. Performing Organization Code

619-12-30-03

8. Performing Organization Report No.

L-15920

10. Work Unit No.

II. Contract or Grant No.

13. Type of Report and Period Covered

Reference Publication

14. Sponsoring Agency Code

15. Supplementary Notes

David E. Bowker and Richard E. Davis: Langley Research Center, Hampton, Virginia.

David L. Myrick, Kathryn Stacy, and William T. Jones: Computer Sciences Corporation, Hampton,Virginia.

16. Abstract

A collection of spectral reflectances of 156 natural targets is presented in a uniform format. For each targetboth a graphical plot and a digital tabulation of reflectance is given. The data were taken from the literature

and include laboratory, field, and aircraft measurements. A discussion of the different measurements of

reflectance is given, along with the changes in apparent reflectance when targets are viewed through the

atmosphere. The salient features of the reflectance curves of common target types are presented anddiscussed.

17. Key Words (Suggested by Authors(s))

Spectral reflectancesReflectance measurements

Reflectances of natural targetsReflectances of Earth features

Radiative transfer

18. Distribution Statement

Unclassified--Unlimited

Subject Category 43

19. Security Classif.(of this report)

Unclassified 20. Security Classif.(of this page)Unclassified 21. No. of Pages 22. Price184 A09

For sale by the National Technical Information Service, Springfield, Virginia 22161

NASA-Langley, 1985

Contents

Introduction ................................ 1

Symbols and Units ............................. 1

Measurement of Reflectance ......................... 2

Laboratory Measurements ......................... 2

Field Measurements ........................... 2

Aircraft Measurements .......................... 3

General Features of Reflectance Curves .................... 4

Vegetation ................................ 4

Soil ................................... 5

Rocks and Minerals ............................ 5

Water, Snow, and Clouds ......................... 8

Selection and Formatting of the Spectral Reflectance Data ............ 8

Concluding Remarks ............................ 9

Appendix--Atmospheric Effects on Reflectance Profiles ............. 10

Factors Affecting Apparent Reflectance Determination ............ 13

Correction for Atmospheric Effects ..................... 16

References ................................. 18

Spectral Reflectance Data .......................... 21

Index of Spectral Reflectance Targets .................... 21

Reference Sources for Spectral Reflectance Data ............... 23

Graphs and Tables of Spectral Reflectances ................. 26

ORECEDING PAG'E BLANK NOT FCMI_

Precedingpageblank iii

Introduction

Remote sensing studies devoted to the develop-

ment of spacecraft sensors have need of a representa-

tive selection of spectral reflectances of natural tar-

gets in order to determine the optimum number andlocation of spectral bands and sensitivity require-

ments. For example, Schappell et al. (1976) uti-

lized reflectances of ground features in the design

of a video guidance, landing, and imaging system

for space missions; Begni (1982) selected the spec-

tral bands for the SPOT satellite by taking into ac-

count both the spectral signatures of ground objectsand the modifications introduced by the atmosphere;

and Huck et al. (1984) studied spacecraft sensor re-sponses and data processing algorithms for identify-

ing Earth features by using a selection of spectral

reflectances taken from the literature. Although sev-eral excellent sources of reflectance data are avail-

able, such as the agricultural data base from Pur-

due University Laboratory for Applications of Re-

mote Sensing (Biehl et al. 1982) and the geologic data

base from the Jet Propulsion Laboratory (Kahle et

al. 1981), these data bases are limited in target se-lection and are usually available only in computer

compatible format. Thus there is a need for a set

of reflectance data that is representative of natural

targets. The purpose of this report is to present a

collection of uniformly digitized spectral reflectances

of natural targets in a common format.

The spectral reflectance data were taken from the

literature and include laboratory, field, and aircraftmeasurements. Since the reflectance of most natu-

ral targets may be influenced by the measurement

technique, the techniques for the measurement of re-

flectance are discussed with emphasis on their majordifferences and sources of error. Most of the data

have been derived from laboratory or field measure-

ments. There is much interest, however, in the re-

mote sensing of natural targets from both airborne

and spaceborne platforms. Therefore, the appendixdiscusses the changes in apparent reflectance when a

target is viewed through the atmosphere.

The target reflectances have been divided into sixcategories: agriculture; trees; shrubs and grasses;

rocks and soils; water, snow, and clouds; and mis-cellaneous. The 156 reflectance curves included are a

representation of what is available in the literature;

they are not necessarily the most preferred sets of

data for a listing of this kind. There is a similarityamong reflectances of many of the targets, and thus

a representative reflectance curve for each of the ma-

jor types is presented along with a discussion of itssalient features.

All the data were digitized from copies of doc-

uments and archived on magnetic tape for further

processing. Each reflectance curve presented repre-

sents the data originally shown by the author. A test

of the data transcription method indicated an error

of less than 1 percent in the digitization process.

Symbols and Units

E d diffuse component of irradiance at the

Earth's surface, watts-m -2

Eo solar irradiance at the top of the atmo-

sphere, watts-m -2

Eo8 direct solar component of irradiance at

the Earth's surface, watts-m -2

E8 irradiance at the Earth's surface, watts-

m-2

FOV field-of-view, deg

H sensor altitude, km

IFOV instantaneous field-of-view, deg

L B beam radiance component of LT, watts-

m-2_sr-1

Lp path radiance component of LT, watts-m-2_sr-1

Ls surface radiance, watts-m-2-sr -1

L T total radiance measured at the instru-

ment, watts-m-2-sr -1

R bidirectional reflectance factor

Rr bidirectional reflectance factor ofreference

Rt bidirectional reflectance factor of target

T transmittance of atmosphere along

target-to-sensor path

TOA top of atmosphere

V atmospheric visual range, km

Vr instrument response when viewingreference

instrument response when viewing target

0i irradiance zenith angle, deg

Or reflected beam zenith angle, deg

A wavelength, #m

p total reflectance; used in the appendix to

represent reflectance measurement of theEarth's surface

PA

Pb

Pt

TA

Wo

apparent reflectance of a surface feature

when viewed from aloft through the

atmosphere

reflectance of background

reflectance of target

optical depth

relative azimuth angle, deg

irradiance azimuth angle, deg

reflected beam azimuth angle, deg

single-scattering albedo

Measurement of ReflectanCe

Reflectance of a target can be measured in three

ways: in the laboratory, in the field, or from an el-

evated platform such as an aircraft. These three

approaches provide different results for several rea-

sons. Illumination conditions are more easily con-

trolled in the laboratory, but then the content of

the field-of-view changes from laboratory to field to

aircraft (or spacecraft). In studying vegetation, forexample, a single leaf may be analyzed in the lab-

oratory, whereas in the field the footprint usually

becomes larger with altitude. Thus, depending on

its altitude, a narrow-field-of-view instrument may

"see" anything from several leaves to a field sev-eral hundred meters in diameter. As the footprint

becomes larger, the target becomes a composite ofleaves, stalks, soil, grasses, weeds, etc., and its re-

flectance properties are influenced by such factors as

wind condition, row geometry, solar zenith, target

slope, etc. Also, as altitude increases, atmospheric

effects become more important, and scattering and

absorption effects on radiance are enhanced. Target

radiance is also influenced by scattered radiance from

outside the instrument field-of-view. (These two ef-fects are discussed in the appendix.)

Although the three measurement techniques yield

different results, each has its place in remote sensing

research. When modeling a vegetation canopy, thereflectance of the individual leaves is a required in-

put. The laboratory data in this report do not ade-

quately support canopy modeling; however, they do

show spectral variations important in remote sens-ing. When combining various ratios of vegetation

and bare soil to obtain an integrated reflectance, field

measurements are required. And lastly, when at-

tempting to correlate target reflectance with satel-

lite measurements, a field measurement with a large

footprint is desirable. Since target reflectance is in-

fluenced by the manner in which the measurement is

made, each of the three techniques will be discussed

separately.

Laboratory Measurements

Total reflectance p is the ratio of the reflected ra-

diant flux to the incident flux (Judd 1967). For agiven target this quantity can be determined in sev-

eral ways, but in the laboratory a small sample of

the target is usually analyzed using a spectropho-

tometer with an integrating sphere attachment. Two

methods of measuring reflectance with an integrat-

ing sphere are possible. In the substitution method,

sample and reference (an ideal Lambertian surface)

are placed in turn at the sample aperture and theratio of respective photocell readings is determined.

This technique has introduced systematic error ofup to 12 percent in the determination of reflectance

(Jacquez and Kuppenheim 1955). In the comparison

method, both sample and reference are placed in sep-

arate apertures, the illuminating beam is switched

from one to the other, and the ratio of the respective

photocell readings is determined (Vlcek 1972). For aperfect sphere, the error is zero; with a flat sample,

the error is about 1 percent. Most of the laboratory

data included in the appendix were generated with

spectrophotometers that use the comparison method.

Because of the transmittance of leaves, any re:flectance measurement of a single leaf is influenced

by the background on which the sample is supported

(Lillesaeter 1982). When leaves are stacked, it has

been found that no further change in reflectance

at near-infrared wavelengths occurs beyond a depth

of eight leaf layers or more (Allen and Richardson

1968). When comparing laboratory with field mea-

surements, Knipling (1970) found that the visible andnear-infrared reflectances from a nearly continuous

broad leaf canopy were typically about 40 and 70 per-cent, respectively, of the laboratory reflectance of a

single leaf.

Field Measurements

Spectral reflectance of natural surfaces can be

measured in the field by using a radiometer fittedwith an integrating sphere as the primary radiation

receiver. The aperture of the sphere is pointed atzenith to measure irradiance and then rotated 180 °

to nadir to measure the target radiance. Since thenadir field-of-view is nearly 180 °, a correction is usu-

ally applied to compensate for shading by the instru-

ment itself (Coulson and Reynolds 1971). When theintegrating sphere technique was used for measuring

hemispheric reflectance, Coulson and Reynolds found

that the time-varying irradiance field, particularly on

hazy days, was responsible for appreciable scatter in

2

the reflectance determinations because of the sequen-

tial nature of the measurements. Duggin and Cunia

(1983) compared simultaneous measurements of irra-diance and target radiance with sequential measure-ments and showed that the simultaneous approach

dramatically reduced the variation of the reflectance

measurements. Large cumulus clouds near the solardisk and thin cirrus clouds are two major causes of

varying irradiance (Robinson and Biehl 1979).

A cosine receptor, which usually employs a diffus-

ing optics element or an immersion lens for improved

performance, is often used to measure irradiance over

a 27r steradian field-of-view. The target radiancemeasurement at nadir is frequently restricted to a

smaller field-of-view, referred to as an "apertured"

reflectance measurement (Graetz and Gentle 1982).The more commonly measured parameter, however,

is the bidirectional reflectance factor, which requiresa reference standard for the irradiance determina-

tion. A bidirectional reflectance factor R is defined

as the ratio of the radiant flux reflected by the targetto that reflected into the same beam geometry by a

perfectly reflecting diffuser (Lambertian) identically

irradiated (Judd 1967).

The bidirectional nature of R(gi, ¢i; Or, Cr) is il-lustrated in figure 1 for incident and reflected beams

where (8i, ¢i) and (0r, Cr) are the zenith and azimuthangles of the incident and reflected beams. In the

field, R can be approximated by taking the ratio of

the instrument response when viewing the target Vtto the instrument response when viewing a level ref-

erence surface Vr such that

½Rt(O i, qSi; Or, Cr) = _rrRr(Oi, ¢i; Or, Cr)

(1)

where Rr (0i, el;Or, Cr) is the bidirectional reflectancefactor of the reference surface; this term corrects for

the nonideal reflectance properties of the reference

surface. This relation assumes that (1) the instru-

ment response is linear to entrant flux, (2) the dif-

fuse component of irradiance is negligible, (3) the ref-erence surface is irradiated and viewed in the same

manner as the target, and (4) the aperture is suffi-ciently distant from the target.

An attempt to correct for the diffuse skylight com-

ponent in the irradiance field by subtracting the spec-

tral responses of the shadowed target and shadowedreference introduces an uncertainty in the reflectance

determination that is greater than the diffuse sky-

light effect itself (Bauer et al. 1977). A simulation

study on the influence of sky radiance has found theerror induced in the estimation of bidirectional re-

flectance factors to be less than 5 percent for zenith

view and Sun zenith angles less than 55 ° (Kirchner et

Zenith

Radiance Irradiance

270

Figure 1. Viewing geometry for bidirectional reflectancefactor measurements.

hi. 1982). As long as the field-of-view is no greaterthan 15° to 20 °, the term bidirectional reflectancefactor is considered to describe the measurements ad-

equately (Bauer et al. 1979).When field illumination conditions are too vari-

able or the sky is frequently overcast, measurements

can be made using an artificial light source (De Boer

et al. 1974). In this procedure the target is covered

to block out natural light. The technique can also

be extended to the laboratory, though it is the usual

practice to view potted field plants and not individ-

ual leaves (McClellan et al. 1963).

Aircraft Measurements

The measurement of reflectance has thus far in-

volved only a ratio of two instrument readings and a

calibrated reference surface (when the reflectance fac-

tor is measured). With aircraft measurements, thereis the additional complication of atmospheric scatter-

ing and absorption effects. Atmospheric scattering is

apparent to anyone who has viewed the ground from

an aircraft on a hazy day; the scattering produces abluish turbidity superimposed over the backgroundscene. If a suitable reference surface is available or if

the spectral reflectance of selected targets has been

established as references using field or helicopter (i.e.,

low altitude) equipment, aircraft data can also be

calibrated (Bauer et al. 1979). The wide scan angleof most aircraft instruments is an additional prob-

lem, since the atmospheric path is variable acrossthe scan. Either additional calibrations should be

made at selected off-nadir scan angles or the targets

of interest should be restricted to nadir viewing.

Surface reflectances can be estimated fairly well

without ground support, however, provided that

absoluteradiancemeasurementsareobtained and a

suitable radiative transfer program is used to correct

for atmospheric effects (Bowker et al. 1983). Because

of the interest in airborne and spaceborne remote

sensing systems, the influence of the atmosphere on

radiance measurements from elevated platforms isdiscussed in the appendix. Two of the reflectance

curves presented in this report have been corrected

for atmospheric effects using the technique given in

the appendix.

General Features of Reflectance Curves

Many natural targets have common features in

their spectral reflectance curves, which make the tar-

gets difficult to identify or separate. All vegetation,for instance, has a similar reflectance profile, whether

it be agricultural crops, trees, shrubs, or grasses.

In addition to the Subtle differences in reflectances,

the reflectances vary with time, at least for Vegeta-

tion; this often leads to an identification or separa-

tion of targets. Several of the major categories ofreflectances w_l-be discussed in some detail in this

section to show the commonality of features and the

manner in which remote sensing may take advantage

of minor differences to separate targets. Vane et al.

(1982) have summarized the spectral bands useful forremote sensing applications.

Vegetation

Figure 2 is a typical reflectance curve for photo-

synthetically active vegetation. The spectrum can

be broken into three regions according to the ma-

jor factor responsible for the curve behavior. Below

0.7/tm, absorption is dominated by carotenoid pig-

ments (centered at 0.48 #m) and chlorophylls (cen-

tered at 0.68/_m). The green peak (centered at ap-

proximately 0.56 _um) is the region of the visible spec-

trum corresponding to weak absorption. The sharp

rise around 0.7 #m, (called the red edge) marks thechange from chlorophyll absorption to cellular re-flectance. The near-infrared reflectance from 0.7 to

1.3/_m is dominated by the cell-wall/airspace inter-

face and, to a lesser extent, by refractive index dis-

continuities of cellular constituents (Gausman 1974).

Beyond 1.3 /zm, reflectance is primarily controlled

by leaf water content. The suggested spectral bands

given in figure 2 have been successfully used by theresearchers; they mostly represent bands that wereavailable on various sensors and are not necessar-

ily optimum with respect to bandwidth or central

wavelength.

During the growth cycle of vegetation the re-

flectance decreases in the visible wavelength and in-

creases in the near-infrared wavelengths until max-

imum canopy development is reached. Then, with

senescence, the visible reflectance increases while

the near-infrared reflectance decreases, although rel-

atively less than the visible increases. Thus, veg-

etation reflectance usually progresses from a back-

ground, such as soil, to full greenness and then re-

turns to the background again.

By analyzing the reflectance spectrum of vege-

tation in discrete narrow bands, Verhoef and Bun-

nik (1974) identified about i2 spectral bands rele-

vant for assessing special features of crops. The se-

lection of a few bands and/or wide bands does not

give optimum results (Beers 1975). Generally, the se-lected bands should have low correlation. Using the

Landsat MSS bands, Kauth and Thomas (1976) de-veloped a linear transformation (called the "tasseled

cap") that defines two orthogonal components called

"brightness" and "greenness." The brightness estab-lishes the data space of soils, and the greenness is a

measure of green vegetation. The: temporal behavior

of the greenness can be used to separate some Crops

(Badhwar et al. 1982). Idso et al. (1980) used a re-flectance ratio involving Landsat MSS bands 5 (0.6-

0.7/zm) and 6 (0.7-0.8 #m) to estimate ga:ain yielcls

by remote sensing of crop senescence rates. Crops

that are stressed for water, which have lowest grainyields, had a longer period of senescence.

The broad absorption areas near 1.4 and 1.95

#m are also atmospheric water vapor bands andshould be avoided in remote sensing. However, the

1.6 and 2.2/_m regions are useful for distinguishing

succulent (average leaf water content of 92 percent)from nonsucculent (average leaf water content of

71 percent) plants (Gausman et al. 1978).

According to Collins (1978) the sharp spectral

reflectance rise between the chlorophyll absorption

maximum and the cellular reflectance maximum, the

red edge, can be very useful in detecting pheno-

logic changes and geochemical stress. In a study

of maturation changes of crop plants such as corn,wheat, and sorghum, Collins detected a red-shift

(of 0.007 to 0.010 /_m) of the red edge to longer

wavelengths (0.690 to 0.700/_m) associated with the

conversion from vegetative growth to reproductive

growth (heading and flowering). The red-shift was

useful in separating some crop types, particularly the

non-grain from the grain crops (the shift is not as

pronounced in the non-grain crops).When plants become stressed, a decrease in

chlorophyll Productivity causes a shift of the red edgetoward shorter wavelengths. This kind of blue-shift

has been detected in the reflectance spectrum froma forest canopy growing over copper-lead-zinc sulfide

mineralization (Collins et al. 1977).Just as the detection of the shift in the red

edge requires spectral measurements of 0.010-#m

4

resolution, other regions within the reflectance curve

of vegetation may also demand such resolution (Macket al. 1984). There is an emphasis toward more

bands with higher spectral resolution; however, Mack

advises using only those bands with an established

relevant biophysical and agronomic basis. Knowl-

edge of these spectral characteristics is essential for

minimizing data proce§sing costs and time.

Soil

Figure 3 displays five representative spectral re-

flectance curves for soils. Condit (1970) has clas-sified 160 soil samples from 36 states into three

general types according to the shape of their re-

flectance curves within the 0.4 to 1.0 #m region of

the spectrum. Type 1 curves have rather low re-

flectances with slightly increasing slope, which givesthem their characteristic concave form from 0.32 to

about 1.0 #m. Type 2 curves are characterized by

generally decreasing slope to about 0.6/_m followed

by a slight dip from 0.6 to 0.7 _tm, with continued

decreasing slope beyond 0.75 /_m. This results in

a typical convex shape from the visible to beyond

1.0 _m. Type 2 soils are better drained and lower

in organic matter than type 1 soils. Type 3 curves

have a slightly decreasing steep slope to about 0.6 #mfollowed by a slight dip from 0.62 to 0.74/_m, with

slope decreasing to near zero or becoming negative

from 0.76 to 0.88 #m. Beyond 0.88 #m (to 1.0/_m)the slope increases with wavelength. Type 3 soils

have moderately high iron content. Condit was able

to reproduce these curves (160 in all) with a high de-

gree of accuracy from measurements at five narrow

bandwidths (0.02 /_m) centered at 0.40, 0.54, 0.64,

0.74, and 0.92 #m; these wavelengths may not relateto specific physical phenomena. Stoner and Baum-

gardner (1980) established two more types of soil re-

flectance curves, similar to type 3, by extending the

data out to 1.3 ttm. The type 4 reflectance behavior

from.0.88 to 1.3 ttm was caused by high iron content

and organic material. In type 5, the negative slopefrom 0.75 to 1.3 /_m resulted from very high iron

and low organic concentrations. This was the only

type that did not show a strong absorption (water)at 1.45 #m.

Although reflectances in all spectral regions are

negatively correlated with organics, the region

around 0.57 #m (the green peak) is particularly use-

ful for monitoring organic matter in bare soils sinceit is free of other major disturbances. Stoner and

Baumgardner considered measurements at 0.7, 0.9,

and 1.0 #m to be essential for thorough classification

of background soil reflectance. Absorptions at 0.7

and 0.9 #m are produced by ferric iron compounds,

while that at 1.0 #m is caused by ferrous iron com-pounds.

The 0.4 to 1.0 #m region is not useful for moni-

toring soil moisture content (Reginato et al. 1977),

although the entire reflectance curve is generally sup-

pressed with increased moisture. The region cen-tered at 2.2 #m has the highest correlation with soil

moisture; this region was similarly important with

vegetation.

The two regions of highest soil reflectance, cen-

tered at approximately 1.27 and 1.65 #m, correlate

with many soil properties (Stoner and Baumgardner

1980). With sandy textured soils, a decrease in parti-cle size increased reflectance. However, with medium

to fine textured soils, a decrease in particle size de-creased reflectance.

Rocks and Minerals

Figure 4 shows spectral reflectance curves forshale and andesite. Rocks are similar to soils in

reflectance, which is not surprising since soils are

derived from weathered rocks. One major differencebetween the two is the organic matter present in soils,which tends to decrease reflectance.

With transparent rock particles, reflectance in-

creases with a decrease in particle size, but just the

opposite is the case with opaque particles (Salisbury

and Hunt 1968). This may explain the behavior ofthe fine grain soils discussed in the previous section.

The iron absorption bands are very prominent in

basic rocks (i.e., igneous rocks with minerals rich in

metallic bases). These absorption bands are evenprominent in red-stained beach sands.

The strong fundamental OH vibration at 2.74/_m

characterizes the behavior of hydroxyl-bearing min-

erals. Clays (hydrous aluminum silicates), in par-ticular, show decreasing spectral reflectance beyond

1.6 #m, and this broadband behavior can be used to

identify clay-rich areas associated with hydrothermal

alteration zones (Podwysocki et al. 1983). The ab-

sorption peaks at 2.17 and 2.20 #m can be used to

identify clay minerals (Goetz and Rowan 1981). Thereflectance spectrum of unaltered material is not as

complex, particularly in the 2.0 to 2.4 #m region, asin altered rocks.

The spectral absorption features at 1.4 and

1.9 t_m, as well as at 2.2 /_m, indicate hydration,

but these two regions are subject to atmosphericinterference.

The detection of vegetation cover and the anal-

ysis of the spectral properties of plants to identify

conditions present in the soil are also an important

area in geologic remote sensing. This subject was

mentioned in the vegetation section. A discussion of

• 8- . Suggestedspectral bands

,i,m==

• ;'--

• 6 _-_ :_Cell structure-l---_ Leafwater content_L_al I

pigmentsII Ceil-wail/airspace

I interface

- _i H20.2 Chlorophyll absorption H 0 absorption

P .4

0 I I

.3 .5 1.0 },, pm 1.5 2.0 2.

Figure 2. Typical vegetation reflectance curve showing dominant factors controlling leaf reflectance. Vane et at. (1982)attributed the three rows of suggested spectral bands to Wiersma and Landgrebe (top row), Tucker (middle row), andORI, Inc. (bottom row).

"'F6

Suggested spectral bands

Two

brightestregions MoistureIton absorption J. J, content

organic [---_ _ ___ "X_ /_ Curve

.22

145

0 I.3 .5 1.0 1.5 2.0 2.5

Lpm

Figure3. Typicalsoilreflectancecurvesforthe fivemajor typesofcurves.Types 1-3 proposedby Condit (1970)and types

4 and 5 by Stonerand Baumgardner (1980).Vane etal.(1982)attributedthe suggestedspectralbands to StonerandBaumgardner.

6

.8

.6

I

P .4

.2

Suggested spectral bandsm J _ ii nil mm

llmmB m m m m

Bound and unboundwater absorption

1l | OH-bear!ng

Iron absorption I _ I abs°rpti°n

SHALE/

-

0 I I I l l.3 .5 1.0 1.5 2.0 2.5

,X,pm

Figure 4. Typical reflectance curves for two rock formations. Vane et al. (1982) attributed the suggested spectral bandsto Goetz and Rowan except for the 0.40-0.42 /_m and 0.84-0.90 t_m bands (ORI, Inc.) and the 0.45-0.52 _m band(Billingsley).

.8-

.6

P

.4

.2

Suggested spectral band(for cloud-snow)

mR

Clouds

Suggested spectral bands( for water )

_-.___,,..II m

Fresh snow

Figure 5. Typical reflectance curves for water, snow, and clouds. Vane et al. (1982) attributed the suggested spectral bandsfor water to ORI, Inc., except for the 0.47-0.57 _m band (NASA-GSFC). The cloud-snow band is by Crane and Anderson(1984).

7

thetopichasbeenpresentedby Goetzet al. (1983).A 10-percentgrasscovermasksbeyondrecognitionspectralcharacteristicsof suchrocksasandesiteandlimestonethat havelowreflectances;dry vegetation,on theotherhand,hasa minimaleffect(SiegalandGoetz1977).Beyond1.4#m,thereflectanceof rocktendsto becomemoredominant.

Water, Snow, and Clouds

In the absence of glitter effects, water is easily dis-

tinguished from other targets by its low reflectance,

particularly in the near-infrared portion of the spec-trum. However, high concentrations of suspended

sediment, which often occur in shallow reservoirs,

can increase reflectance. Surface algal blooms can

also change the reflectance properties of water; these

are distinguished from high sediment loads by the

characteristic chlorophyll absorption in the red area

of the spectrum. Monitoring chlorophyll in ocean

waters, where reflectance is less than 0102, has been

discussed by Gower et al. (1984).

In figure 5, both snow and clouds are seen to have

high reflectance in the visible portion of the spec-

trum. Clouds are still highly reflective in the near-

infrared wavelengths, while snow becomes relatively

nonreflective beyond 1.4 #m, particularly in the 1.5

to 1.6 #m region (Crane and Anderson 1984).

Selection and Formatting of the SpectralReflectance Data

A literature search for spectral reflectance data

retrieved about 300 spectral curves. From these,

156 were selected based on the following criteria:

(1) the importance of the target, (2) the data collec-tion mode, and (3) the quality of the data. Priorities

for the remote sensing of agricultural crops have been

established by Bowker (1985). For the other areas,

however, selection was guided by availability and the

desire to present a variety of targets. Field measure-

ments made with a high-resolution scanning spec-

trophotometer were preferred. This type of data was

limited, so that laboratory spectral measurements of-ten had to be selected. It is important to note that

laboratory measurements are sometimes required be-

cause of vegetation cover of natural targets in the

field, for example, soils. Of the 156 data sets, 59 rep-

resent laboratory measurements. Most of these oc-cur in either the tree or the rocks and soils category.

(As previously mentioned, the laboratory and field

data are not compatible since they represent entirely

different environments.) Finally, the quality of thereflectance data, which was judged somewhat arbi-

trarily, was used to eliminate some of the data. The

8

preferred data were well documented with a discus-sion of error sources.

The targets were grouped into six major cate-

gories: agriculture; trees; shrubs and grasses; rocksand soils; water, snow, and clouds; and miscellaneous

targets. Each reflectance curve was presented by its

author as being representative of a given target; thisreport has simply standardized all of the data to acommon format.

This standardization involved digitizing the

curves from the published documents and interpo-

lating to obtain the desired format. The digitiza-

tion was performed in the following manner. First,

a photocopy was made of each spectral reflectancecurve chosen for inclusion in the data set. Then,

an X-Y digitizer was used to digitize the data fromeach profile. Each record was archived on magnetic

tape. In final processing the data were retrieved, the

reflectance curve was machine plotted, and a second-

order interpolation was performed to give the uni-

form spectral intervals and format shown; having a

common wavelength interval for each profile helps in-tercomparison of the data.

Digitizing the data has, of course, introduced

some error. All of the data were taken from copies of

the original documents. Fortunately, one of the re-

ports (Gausman et al. 1973) contained both graphi-cal and tabular data. This set of data represents the

worst case in digitizing since the original figures were

only approximately 20 by 45 mm in size. Compari-

son of reflectance values at 38 coincident wavelengths

(taken from two figures in the Gausman report) gave

an average error of only 0.0073 units of reflectance.This is an excellent agreement, and the data pre-

sented in this report may, therefore, be taken as re-liably reporting the original sources.

Spectral reflectances for the 156 selected targets

are presented in the common format in the back of

this report. The reflectance data for each target are

presented in two formats: (1) graphical, with a wave-length interval from 0.3 to 1.2 #m or from 0.3 to

2.5 #m, and (2) tabular, with a spectral resolution of

0.01 #m (0.3 to 1.2 #m) or 0.02 #m (0.3 to 2.5 #m ).The ordinate of the reflectance curves is labeled "re-

flectance" with a range from 0 to 1. Bidirectional re-

flectance factor would have been a more appropriateterm for most of the field data, but it was not always

clear that the assumptions required by equation (1)were valid (see Robinson and Biehl 1979). In severalinstances data have been included where the ordinate

was labeled "relative reflectance" or "albedo." The

magnitudes of most target reflectances are known to

vary over wide limits, even when the target descrip-

tions are identical. What is most important is the

variation of reflectance with wavelength.

Concluding Remarks

A collection of spectral reflectances for 156 natu-

ral targets has been presented in a uniform format.

Each target is described by both graphical and tab-ular data. The collection was chosen with some con-

sideration of the relative importance of the targets,

and the data presented are representative of what isavailable in the literature. While the data set was de-

veloped to support simulation studies in the develop-

ment of remote sensing instruments, it may find ap-

plication in other areas of remote sensing, such as al-

gorithm development and radiative transfer studies.

The data are presented here with a uniform 0.01-

or 0.02-#m spacing, even though the spectral reso-

lution of the source data varied widely. Therefore

these data are intended for the broad class of appli-

cations requiring moderate spectral resolution, and

not for those requiring high spectral resolution, such

as the detection of the vegetative red-shift; for thesehigh-resolution tasks, other data must be used.

NASA Langley Research CenterHampton, VA 23665February 20, 1985

9

Appendix

Atmospheric Effects on Reflectance Profiles

In the spectral region of interest for this report,

0.3 to 2.5 pm, the sensed energy is almost entirelyderived from solar radiation which transits the atmo-

sphere, is reflected by the surface, and is then trans-mitted to the sensor aloft. In this spectral region,

the thermal radiation from the atmosphere itself is

negligible in comparison with the solar component,so it will be ignored here. The solar irradiance Eo on

top of the atmosphere is shown in figure A1. After

passing through the atmosphere, the irradiance im-

pinging on the surface has been attenuated as shown

by the lower curve. Both curves here pertain to the

Sun at zenith. The atmospheric absorption featuresshaded on the curve are due to ozone, oxygen, water

vapor, and carbon dioxide, as indicated.The solar irradiance at the surface is composed

of both a direct and a diffuse component, as shown

in figure A2. For the example shown, the diffusecomponent amounts to more than 30 percent of the

total at the shortest wavelengths. (Slater (1980)

states that a diffuse component of 10 to 20 percent

is typical for the visual to near-infrared spectral

range.) The conditions assumed for figure A2 are an

atmospheric visual range of 31.4 km and a surfacereflectance of 0.4 at all wavelengths. (For this, and

subsequent curves in this appendix, the solar zenith

angle 0i is 20°; all figures here cover the wavelengthrange from 0.4 to 1.2 #m.) As will be seen later,

varying the visibility and surface reflectance affects

the magnitude of the diffuse irradiance.

From the foregoing, it can be seen that the spec-tral content of the solar irradiance has been modified

greatly by the atmosphere even before any reflection

takes place. The irradiance at the surface Es is made

up of a direct solar component Eos and a diffuse com-

ponent E d, so

Es = Eos + Ed (A1)

Upon reflection by the ground, a surface radiance Lsresults which is a function of Es and p, the surface

reflectance. If Lambertian (isotropic) reflectance can

be assumed (this assumption may not always be

justified; see Smith et al. 1980), then

Ls = Ss__fp (A2)

All quantities have a spectral dependence, which hasbeen omitted here for clarity of notation. The surface

radiance Ls is that radiance which would be mea-

sured by an observer at the surface. When the targetis viewed from aloft, the total radiance measured at

the instrument L T is composed of a beam radiance

L B and a path radiance Lp. Thus,

LT = LB + Lp (A3)

The beam radiance LB is that component of radiance

arising from radiation reflected from the Surface and

transmitted directly to the sensor without scattering_i.e_, LB = LsT. The path radiance Lp is scattered

radiation which enters the path between target and

sensor. In terms of the surface radiance Ls,

LT = LsT + Lp (A4)

where T is the transmittance of the atmosphere

along the target-to-sensor path. Since the surfacereflectance is defined as

lr Ls

p = _ (A5)

then the apparent reflectance aloft is

rL T _ 7r (LsT + Lp) (A6)PA= Es Es

which differs from the true reflectance p according to

the magnitudes of T and Lp for the altitude of the

sensor. Depending on their magnitudes, PA can be

either larger or smaller than the true value p.

Figure A3 shows the apparent reflectances of tar-gets with true reflectances of 0.1, 0.4, and 0.7 when

the targets are viewed from altitudes of 0.6 kin,3.0 kin, and the top of the atmosphere (TOA). In

general, viewing through the atmosphere increasesthe apparent reflectance for low-reflectance objects

(e.g., p = 0.1) and decreases the apparent reflectancefor high-reflectance objects (e.g., p = 0.7). For ob-

jects of intermediate reflectance (e.g., p = 0.4), the

effect is minimal and depends on wavelength; PA canbe either larger or smaller than the true reflectance

p.This distortion in PA is not surprising because

only photons in Ls carry information purely concern-

ing the target. Most photons making up Lp have had

no interaction with the target. Some of them are

derived from multiple-scattered radiation which hasnever reached the surface. Others are derived from

radiation which has been reflected from the surface

outside the target area and then, after one or more

atmospheric scatterings, has found its way into the

field-of-view of the sensor. A small number of pho-

tons in Lp have been reflected by the target, butscattered at least once on their way to the sensor

(and, thus, are not strictly part of the beam radi-

ance). As the path radiance increases relative to thebeam radiance, less information about the target is

included in the radiance signal.

10

250_-

Ir radiance,mW cm-2

pm-1

200-

150-

100-

Solar irradiance outside atmosphere

Solar irradiance at sealevel

H20

_z-O 2, H20

_-H20

l, rl __iii_=. H20' C02 H20' C02L_, , ,

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2},, IJm

Figure A1. Solar spectral irradiance outside the atmosphere and at the surface, for solar zenith angle of 0°. Features due toprincipal absorbers are identified.

Irradiance,mWcm-2

sr-1 iJm-1

5O

Bi: 20°, er= 0:, ""

V = 31.4km,p= 0.4,eo = 0.95

0 I I l I [ I I I.4 .5 .6 .9 1.0 1.1 1.2

Figure A2. An example of the direct and indirect (diffuse) components of irradiance on a surface with reflectance of 0.4.

11

.7

.9

PA

.... p= 0.7

Conditions:

0i= 20o,Or=0°,V = 31.4km, t_°= 0.95

Sensoraltitude,H

.... 3.0kin_._ 0.6km ::

: 0.Y

, •I.I 1.20.4 .....

_,_m

Figure A3. Effect of sensor altitude on apparent surface reflectance.

45

3[

Conditions:

0i= 20o,Or= 0o,

V = 31.4,kin,Uo= 0.95

Radianc_,mW cm-_

st-1 _m-I

25

Sensor altitude, H

TOA--.-- 0.6 km

p= 0.7

.;,'-" "--'_'-_/-_T p = 0.1

'0_ LB . . o oII0 lli 1.2

4 5 0 J .o .- . "• " " X,pm

Figure h4. Beam ra_liance and total radiance at 0.6 km and TOA.

12

Figure A4 shows the relative strengths of L T

and LB for reflectances of 0.1 and 0.7, at altitudes

of 0.6 km and TOA. Thus, this figure shows the

radiance components for the PA plots in figure A3.

Note that the absorption features in the radiancecurves have been omitted, for clarity. The path

radiance is the difference between L T and L B. For

reflectance of 0.1, the 0.6-km curves lie betweenthe TOA curves. Then, for reflectance of 0.7, the0.6-km curves lie at or above both TOA curves. This

behavior shows that for p = 0.1, total radiance

increases with altitude, but for p = 0.7, it decreases

with altitude. For p = 0.4 (not shown), the total

radiance is nearly constant. Turner (1975) describes

in more detail the relative magnitudes of LT, LB,

and Lp under a variety of conditions.

Factors Affecting Apparent ReflectanceDetermination

Some introductory examples have just been givenof the influence of altitude and surface reflectance

on the derived reflectance. In the present section,

all the parameters affecting the determination of ap-

parent reflectance will be identified, and their effectsdescribed. The parameters are shown on figure A5;

they may be grouped as follows:

Viewing geometry:

Solar zenith angle, 0i

Viewing angle, 0r

Azimuthal angle, ¢i or Cr

Relative azimuthal angle, ¢,

where ¢ = Cr - ¢i + 180Altitude of sensor, H

Meteorological parameters:Relative humidityCloud cover

Surface pressure

Atmospheric optical parameters:

Optical thickness, r Aor

Atmospheric visual range, V

Aerosol type (phase function)

Single-scattering albedo, wo

Target and background parameters:

Target size

Target reflectance, Pt

Background reflectance, Pb

Instantaneous field-of-view, IFOV

The effect of variation in each of these parameters isnow discussed.

Viewing Geometry. As 0 i increases, less solar irra-

diance is incident on the surface, and less is reflected

Atmospheric opticalparameters

Visual range, VAerosol type (phase function)Single-scattering albedo

Background

reflectance, Pb

Zenith

Satellite, at altitude H

Meteorologicalparameters

Cloud coverSurface pressureRelative humidity

Sensor IFOV

Target reflectance, p t

Figure A5. Parameters affecting apparent reflectance.

13

to theobserver.Therefore,failureto accountfor anincreasein 0i would result in an underestimate of re-

flectance. (As noted earlier, 0 i should be kept below

55°.) Also, as Oi increases, there is a higher propor-tion of multiple scattering in the incident radiation.

Similar effects are noted for Or; as Or increases, the

path component of radiance increases, and the beam

component of radiance passes through a longer at-

mospheric path and suffers more attenuation through

absorption and scattering. Therefore, as Or increases,

the total radiance depends more heavily on atmo-

spheric influences and less on target characteristics.Thus, target contrast and modulation become re-

duced with increasing Or. In addition to the atmo-

spheric effects, most targets have bidirectional re-

flectance characteristics that are not isotropic (see,

e.g., Smith and Ranson 1979 and Kimes 1983). Thisbehavior needs to be considered in addition to the ef-

fects of changing Oi and Or (Ho!ben and Fraser 1984

and Barnsley 1984). A feature's reflectance may beconsidered to be isotropic only for small instanta-

neous fields-of-view (IFOV) and over ranges of Oiand Or each smaller than a few degrees (Slater 1980).

However, isotropic surface reflectance is assumed inall cases here. ....

Solar radiation is scattered by both the molecularand the aerosol component of the atmosphere. The

molecular component (mostly nitrogen) scatters in aRayleigh-like fashion with equal amounts of forward-

and back-scattering, and smaller amounts at right

angles to the incident beam. In a very clear atmo-

sphere, the scattering of radiation approaches this

condition. In an aerosol atmosphere, however, scat-

tering is much more anisotropic, with the prepon-derance of radiation scattered in the forward direc-

tion. In most conditions, the scattering phase func-

tion shape is a blend of the Rayleigh and aerosol

phase function shapes, with considerable departurefrom anisotropy. For this reason, the magnitude of

the radiance reaching the detector depends highly

on ¢ except when Or is zero (i.e., the nadir is being

viewed). For molecular scattering, the radiation is

scattered approximately as the inverse fourth power

of the wavelength. (This accounts for the predomi-nantly blue color of the sky.) For aerosol scattering,

the result is less marked, the exponent being on the

order of -1.3 (Kiang 1982). Thus, aerosol scatter-

ing results in a blue-white "milkiness," rather thana blue coloration. For both of these reasons, the ef-

fect of a change in ¢ is, again, always most marked

at the shortest wavelengths. Figure A6 shows the

effect of changing ¢ with 0i --- 20 ° when a surfacewith reflectance of 0.1 is viewed from satellite alti-

tude for Or = 5 °. For example, at _ = 0.4 #m, PA

increases by 0.030 for observations in the direction

14

of the Sun (relative azimuth angle ¢ = 0°) and de-

creases by 0.014 for observations in the direction op-

posite the Sun (¢ = 180°), compared with the nadir-

looking case, which is denoted by X's on the graph.At ), -- 0.7 #m, the increase and decrease are both

approximately equal to 0.002.

Conditions :

Bi=20':', Or=5'° p =0.1,

H = TOA, o° = 0.95

@= 0° V = 31.4 kmx- Pointsdenote

.2 ,\_/_$= 90° nadir vlew(e r = 0°)

.1

OT I I I I l I l ]

.4 .5 .6 .7 .8 .9 1.0 1.1 1.2h, JJm

Figure A6. Effect on apparent reflectance of changing

relative azimuthal angle when viewing a surface with: ........... O

reflectance of 0.1 at 5 from nadirl fromsatellite altitude.

Meteorologlcalparameters. Implicit in the forego-

ing discussion was the assumption of a cloud-free at-

mosphere and a nominal water vapor profile. Clouds

can drastically modulate the amount of energy reach-

ing the surface; they are not modeled here. (For a

good discussion of cloud effects, see Duggin et al.

1984.) Changes in the humidity profile change the

depth of the water vapor absorption features. Ahigher level of relative humidity also affects the type

of aerosol present, by favoring larger aerosol particles

(Shettle and Fenn 1979).Surface pressure and terrain altitude variability

have similar effects on the radiance level. The three-

sigma surface pressure variability worldwide is esti-

mated to be equivalent to a surface elevation range

from -0.73 to +0.78 km (Bowker et al. 1983). Eithera pressure or a surface elevation change modifies the

amount of molecular scattering. For most cases, thiseffect is small.

Atmospheric optical parameters. The amount of

aerosol in the atmosphere is usually parameterized

by the aerosol optical thickness TA where

T A = exp(--VA) (A7)

is the aerosol transmissivity in a vertical path. The

value of vA can be determined for a locality by view-

ing the Sun with a photometer over a range of solar

elevationangles(Flowerset al. 1969and Petersonet al. 1981).Thetotalattenuationismeasuredand,then,becausethemolecularscatteringandozoneop-tical thicknessesareknownand canbesubtracted,theaerosolopticalthicknesscanbedetermined.Theaerosoloptical thicknessis sometimesexpressedasa turbidity, oftentakenat or near0.55#m wave-length. The opticalthickness(or turbidity) at onewavelengthcanbe relatedto the optical thicknessat otherwavelengthsstatistically(Fraser1975andKaufmanandFraser1983)or analytically(Nicholls1984).

Anotherway of quantifying aerosol amount isthrough visual range in the horizontal at the sur-

face (Elterman 1970). The lower the visual range,

the more turbid the atmosphere. This approach has

appeal because visibility (which is proportional to vi-

sual range (Kneizys et al. 1980)) is a parameter mea-sured at all weather stations, whereas optical thick-

ness is measured at comparatively few sites. The

correspondence between optical thickness and visual

range is only a rough proportionality, however, be-

cause it is possible to have thick layers of aerosol ex-

isting aloft with a very clear atmosphere at the sur-

face, as indicated by a surface visibility measurement.For this reason, particularly in remote sensing mea-

surements, for which a target is viewed downward

through the atmosphere rather than along a near-

surface path, turbidity is a more reliable measure.

In summary, a decrease in visual range, or an

increase in optical thickness, increases the amount

of aerosol scattering. Figure A7 shows the effect

on apparent reflectance of changing the atmospheric

visual range from a very hazy condition (V -- 10.5

km) through an average condition (V = 31.4 km)

to a rather clear condition (V = 62.8 km). Thesolar zenith angle is 20 ° , and the nadir is viewed.Three different surface reflectances are simulated.

For the low reflectance (p = 0.1), the effect is an

increase in apparent reflectance at all wavelengths,

particularly at short wavelengths. Even for the

very clear atmosphere (V = 62.8 km), the apparent

reflectance at A = 0.4 #m for p = 0.1 is around0.24. At A --- 0.7 /_m, the increase in reflectance

is only approximately 0.02, even for a very hazyatmosphere. For p = 0.4, the effect of the atmosphere

can be either to decrease or to increase the apparent

reflectance, depending on the wavelength and visual

range. There is an increase only at wavelengths

smaller than 0.6 #m; at longer wavelengths, the

apparent reflectance decreases, by up to 0.04 for ahazy atmosphere. For p = 0.7, the effect is a decrease

in apparent reflectance for all wavelengths and visual

ranges. A more detailed discussion of the effects of

.8

.7

:_'..-=_-__--_-=-'C"- ---'--' ................

.6 _ P= 0.7Conditions:

Oi=20°, Or=0%

.5 (o = 0.95, H= TOAo

PA _-_.__-_'- -.-_-.:_ ........

Visual range:

m 10.5 km

31.4 km-- 62.8 km

T

I I I I I I

p= 0.4

0 I.4 .5 .6 .7 .8 .9 1.0 1.2

;_,pm

p= 0.I

I1.1

Figure A7. Effect of atmospheric visual range on apparent

reflectance, for three surface reflectances.

visual range at solar zenith angles other than 20 °

may be found in Bowker et al. (1983).

The type of aerosol affects the shape of the single-

scattering phase function. Also, the more absorptive

the aerosol, the more isotropic the scattering. Thesingle-scattering albedo wo determines the amount

of radiation scattered, rather than absorbed, at each

scattering. A higher wo means a higher total radi-

ance level. Remember that the shape of the actualphase function varies with wavelength and is a blend

of the Rayleigh and aerosol phase function shapes.Figure A8 shows the effect on PA of changes in the

aerosol single-scattering albedo wo assumed in thecalculations; the effect is shown for three surface re-

flectances. The apparent reflectance is always highestfor the highest value of wo and lowest for the lowest

value. The effect of a change in Wo is roughly propor-

tional to the surface reflectance. For darkest scenes,the effect is minimal; for the brightest scene simu-

lated (p = 0.7), the effect on PA is as much as 0.04at A=0.4#m.

Target and background parameters. If the re-flectance of the adjacent surface area differs from

that of the target, then light scattered from this sur-

rounding background has a different spectral content

from that of the target, and the perceived target re-

15

p= 0.7

PA

6tCondilJons-

.5F Bi = 20:, Sr = 0:,

_. V = 31.4kin, H= TOA

,J ................/ 0.4p=

I Single-scattering albedo:m. __ 1.00-- 0.95.... 0.90

.I

I I I I I.4 .5 .6 .7 .8 .9

A, IJm

p=O.l

I I Il.O 1.1 i.2

Figure A8. Effect of aerosol single-scattering albedo on

apparent reflectance, for three surface reflectances.

flectance will be in error. Figure A9 shows the effect

of viewing a target surrounded by a uniform, slightly

more reflective background. The figure shows cases

with target/background reflectance combinations of

0.1/0.2, 0.4/0.5, and 0.7/0.8. A comparison of these

results with those for the uniform-scene reflectances

of 0.1, 0.4, and 0.7 in figure A7 shows that in each

case the apparent reflectance is higher than that of

the target alone, because of additional photons scat-

tered into the path from the background. Even for

the slight reflectance differences (0.1) simulated here,

the effect is appreciable. Thus, background effects

need to be taken carefully into account.

The research area of modeling such "adjacency

effects" continues to be an active one. Some recent

references are those of Dave (1980), Kaufman and

Joseph (1982), Dana (1982), and Kaufman (1984). A

good introductory discussion may be found in Slater

(1980).

Correction for Atmospheric Effects

Because the factors named above all affect the

perceived reflectances of substances, it is of interest

.5Conditions:

Oi=20 °, Or=zO °'

_. H = TOA, u° = 0.95 Pt 0.4

.4 "_'_'_-" - Pb = 0.5

PA

.3" Visual range:m.__ 10.5 km

-- 31.4km

.... 62.8krn

.2 !_\.

.1 ....... 2 I " i____-_-_-_-_-_-_-_-_-.._._ p£ = 0.2

.4 .5 .6 .7 .8 .9 1.0 1.1 1.2_,,pm

Figure A9. Effect of background radiance on apparent

reflectance, when background reflectance is 0.1 higher

than target reflectance, for three target reflectances and

atmospheric visual ranges.

to ask whether such influences can be estimated well

enough to remove their effects and allow the true

reflectance profiles to be estimated. Bowker et al.

(1983) directly attacked this problem. In that re-

port, the effects of imprecision in the knowledge of

each of the quantities noted earlier on derived re-

flectance are discussed, and the results plotted. Also,

a method was developed for estimating spectral re-

flectance from total radiance values. Figure A10

(from Bowker et al. 1983) shows an example of an al-

falfa radiance profile converted to obtain a reflectance

profile. When the sky is free of clouds and relatively

stable atmospheric conditions prevail, it should be

possible to determine reflectance to an accuracy of

10 percent or better, by using local meteorological

data. It should be noted, however, that only 2 of the

156 reflectance curves presented in this report have

been corrected for atmospheric effects in this manner.

16

25 1.0

2O

LT, 15mW cm-2

sr-llJm-1 10

0.4

9 i = 27°

I I I I I I

.5 .6 .7 .8 .9 1.0;k, pm

.8

.4

2

0.4 .5 .6 .7 .8 .q i.0

L,IJm

Figure AIO. Example of alfalfa field radiance profile that has been converted to spectral reflectance.

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_0

Spectral Reflectance Data

Spectral reflectance data are presented for 156targets. The targets are grouped into six major cat-

egories: agriculture; trees; shrubs and grasses; rocks

and soils; water, snow, and clouds; and miscella-

neous. Within each category the targets are arranged

alphabetically with appropriate adjectives that helpto describe the state of the target. Of the 156 data

sets, 59 represent laboratory measurements. Most ofthese occur in either the tree or the rocks and soils

category. The laboratory data are identified by thewords sample, leaf, or needles. As previously men-

tioned, laboratory and field data are not compatible,

since they represent entirely different environments.

The reflectance data for each target are presented

in both a graphical and a tabular format. On each

graph the source reference number is given alongwith the date of measurement and target location,

where available, and any other pertinent information

concerning the target condition or viewing geometry.The supporting information provided here has been

limited to the more commonly measured items, and

the reader may refer to the original source when more

specific data are needed. Each reflectance curve was

presented by its author as being representative of a

given target; this report has simply standardized allof the data to a common format.

An index of targets and a numbered list of refer-

ence sources precede the spectral reflectance data.

Index of Spectral Reflectance Targets

Agriculture

No. Target Ref.

1 Alfalfa .............. 9

2 Mature Alfalfa ........... 6

3 Dry Alfalfa Hay .......... 37

4 Barley .............. 385 Barley .............. 50

6 Stem Extension Barley ....... 3

7 Ripe Barley ............ 6

8 Ripe Barley ............ 39 Bean Leaf ............. 26

10 Dehydrated Bean Leaf ....... 2611 Beans ............... 50

12 Beets ............... 46

13 Cabbage ............. 46

14 Cantaloupe Leaf .......... 2015 Tall Green Corn .......... 27

16 Silage Corn ............ 2717 Yellow Corn ............ 27

18 Cotton Leaf ............ 15

19 Dehydrated Cotton Leaf ...... 1520 Fallow Field ............ 40

21 Flax ............... 33

22 Oats ............... 33

23 Oats ............... 50

24 Oats ............... 38

25 Peanuts .............. 3326 Potatoes ............. 46

27 Potatoes ............. 50

28 Rapeseed ............. 38

29 Sorghum ............. 7

30 Soybeans ............. 2931 Soybeans ............. 38

32 Sugar Beets ............ 50

33 Sugar Beets ............ 33

34 Sugarcane Leaf ........... 14

35 Sugarcane Leaf ........... 19

36 Sugarcane ............. 1937 Sunflower ............. 23

38 Tobacco .............. 26

39 Tomatoes ............. 46

40 Tomatoes ............. 33

41 Watermelon Leaf .......... 14

42 Wheat ............... 50

43 Seedling Wheat .......... 3

44 Young Wheat ........... 1145 Booted Wheat ........... 6

46 Mature Wheat ........... 11

47 Mature Wheat ........... 3

48 Wheat Stubble ........... 40

Trees

No. Target Ref.

49 Trembling Aspen .......... 5150 Birch Leaves ............ 30

51 Redblush Citrus .......... 17

52 American Elm Leaf ......... 2453 Balsam Fir ............ 57

54 Silver Maple Leaf ......... 24

55 Sugar Maple Leaf ......... 5256 Burr Oak Leaf ........... 36

57 Live Oak ............. 39

58 Orange Leaf ............ 5659 Peach Leaf ............ 56

60 Dead Ponderosa Pine Needles .... 22

61 Ponderosa Pine Needles ....... 2262 Ponderosa Pine Needles ....... 55

63 Red Pine Needles ......... 36

64 White Pine ............ 51

65 Red Spruce ............ 57

66 Sycamore Leaf ........... 49

67 Dehydrated Sycamore Leaf ..... 49

68 Tulip Tree Leaf .......... 24

21

ShrubsandGrasses

No. Target Ref.

69 Cenizo .............. 39

70 Average Desert Leaves ....... 471 Grass ............... 40

72 Grass ............... 44

73 Kentucky Blue Grass ........ 5074 Red Fescue Grass ......... 50

75 Blue Grama Grass ......... 48

76 Perennial Rye Grass ........ 50

77 Dry Lichen Sample ......... 1378 Lichen Mat ............ 12

79 Manzanita ............. 44

80 Mesquite ............. 39

81 Honey Mesquite .......... 16

82 Prickly Pear ............ 19

83 Dry Sage ............. 44

84 Average Subalpine Slope Leaves . . 485 Silverleaf Sunflower ......... 39

86 Burned Forest Surface ....... 12

Rocks and Soils

No. Target Ref.

87 Arkose .............. 21

88 Basalt Sample ........... 4189 Red Cinder Basalt Sample ...... 1

90 Gray Basalt Sample ........ 191 Breccia .............. 32

92 Dry Red Clay Sample ........ 47

93 Wet Red Clay Sample ....... 47

94 Quartz Diorite ........... 595 Granite .............. 54

96 Granite .............. 5

97 Biotite Granite Sample ....... 4198 Gravel .............. 40

99 Glaciofluvial Sand and Gravel .... 45

100 Limestone ............. 21

101 Limestone Sample ......... 47102 Monzonite ............. 5

103 Quartz Monzonite 5

104 Obsidian Sample .......... 1105 Unaltered Rocks .......... 42

106 Altered Rocks ........... 42

107 Rhyolite Sample .......... 41108 Beach Sand Sample ......... 25

109 Carbonate Beach Sand Sample .... 10

110 Quartz Beach Sand Sample ..... 10

111 Quartz Beach Sand Sample ..... 10

112 Dry Sand ............. 8

113 Gypsum Sand Sample ........ 25114 Silica Sand Sample ......... 25

115 Shale ............... 21

116 Dry Silt Sample .......... 47

117 Wet Silt Sample .......... 47118 Dry Silt .............. 8

119 Dry Lacustrine Silt and Clay .... 45120 Soil Sample ............ 43

121 Soil Sample ............ 43

122 Soil Sample ............ 25123 Disked Bare Soil .......... 18

124 Dry Pedocal-Type Soil ....... 8

125 Dry Chernozem-Type Soil ...... 8

126 Dry Laterite-Type Soil ....... 8

127 Dry Clay Soil Sample ........ 24

128 Wet Clay Soil Sample ........ 24129 Dry Lake Soil Sample ........ 25

130 Chilean Nitrate Soil Sample ..... 25

131 Salt Pool Soil Sample ........ 25

132 Dry Sandy Soil Sample ....... 24

133 Wet Sandy Soil Sample . . . .... 24

134 Syenite .............. 5135 Dry Glacial Till .......... 45

136 Rhyolite Tuff Sample ........ 1

Water, Snow, and Clouds

No. Target Ref.

137 Altocumulus Clouds ........ 28

138 Stratus Clouds ........... 28

139 Cirrostratus Clouds ......... 28

140 Middle Layer Clouds ........ 34

141 Dense Ice Cloud Sample ....... 58

142 Hoarfrost Sample .......... 58143 Snow Sample ........... 53

144 Typical Snow Sample ........ 35

145 Fresh Snow Sample ......... 35146 Two Day Old Snow Sample ..... 35

147 Dry Fresh Snow .......... 23148 Wet Snow ............. 23

149 Water .............. 40150 Clear Lake Water ......... 2

151 Turbid River Water ........ 2

Miscellaneous

No. Target Ref.

152 Asphalt .............. 40

153 Blacktop ............. 9154 Concrete ............. 40

155 Shingles .............. 40156 Artificial Turf ........... 31

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Lillesand, Thomas M.; and Kiefer, Ralph W.: Remote

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Marts, Ronald W.: Application of Remote-Sensing Tech-

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Novosel'tsev, Ye. P.: Spectral Reflectivity of Clouds.

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25

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R X R X

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181