relations between soil color and landsat reflectance on semiarid rangelands

DIVISION S-7-FOREST & RANGE SOILS

Relations between Soil Color and Landsat Reflectance on Semiarid RangelandsDonald F. Post,* E. H. Horvath, W. M. Lucas, S. A. White, M J. Ehasz, and A. K. Batchily

ABSTRACTThe reflectance of radiant energy from the earth's surface in sparsely

vegetated arid rangelands is determined by the characteristics of thesoil and geologic material on the land's surface. This study measuredthe color characteristics of earth surface materials collected from asemiarid rangeland in southeastern Arizona and compared these colorsto digital numbers recorded by Landsat. Other parameters includingparticle size, slope, and vegetation were also evaluated, but the colorcharacteristics of the fine earth soil and rock fragments measuredwith a colorimeter were most strongly correlated to Landsat digitalnumbers. The numerical values of the color components (hue, value,and chroma) for three different soil and rock fragment size fractionswere related in a multiple linear regression equation to Landsat digitalreflectance numbers. The B? for Band 4 (0.5-0.6 um), Band 5 (0.6-0.7 urn), Band 6 (0.7-0.8 um), Band 7 (0.8-1.1 um), and the sum ofthe four bands were 0.85, 0.69, 0.71, 0.68, and 0.75, respectively.The color of earth surface features in sparsely vegetated land areasshould be precisely and accurately determined because of its verystrong correlation with remotely sensed spectral data. The use ofcolorimeters to quantify the color of earth surface features will signifi-cantly help in evaluating remotely sensed data, particularly for land-scapes in arid regions.

A[ INFINITE NUMBER of colors surround us in oureveryday lives. Color, as measured by the human

eye, is a matter of perception and subjective interpreta-tion. Our knowledge of color is usually not quantitative,as descriptive terms like yellowish brown or dusky redare often used. Precise and accurate color measurementsof earth surface features, such as soils and rock frag-ments, using colorimeters would help us to better under-stand reflected energy measured by remote sensing tech-niques.

Soil color is one of the most obvious attributes of thesoil and it is strongly correlated to selected soil properties

D.F. Post, W.M. Lucas, S.A. White, M.J. Ehasz, and A.K. Batchily,Dep. of Soil and Water Science, Room 429, Shantz Bldg., Univ. ofArizona, Tucson, AZ 85721; and E.H. Horvath, USDA-SCS, NationalCartographic Center, P.O. Box 6567, Fort Worth, TX 76115. Received20 Nov. 1992. *Corresponding author.

Published in Soil Sci. Soc. Am. J. 58:1809-1816 (1994).

and spectral reflectance. Soil color is most commonlydetermined by a human observer making a visual compar-ison between the sample color and color chips definedby the Munsell Color System (Munsell, 1988). TheMunsell color chips are arranged according to the hue,value, and chroma color components, and the procedurefor measuring the color of soils is explained in detail inSoil Survey Division Staff (1993).

The measurement of a material's color is the detectedor perceived interaction between energy and matter. Inthis study, the interaction is between sunlight, or simu-lated sunlight generated by a colorimeter, and the soilor rock fragment surfaces. Thus color is dependent onincident light, the human observer or instrument beingused, and the energy reflected from the object. Reflectedenergy is dependent on the characteristics of the soilor rock fragments, including the chemical composition,particle size, and the surface condition or microrelief.All of these factors determine the spectral reflectancecharacteristics of the materials, which is recorded as itscolor.

Melville and Atkinson (1985) discussed in detail themeasurement of soil colors and how they are designatedusing various models. Fernandez and Schulze (1987)calculated soil color from reflectance spectra and con-cluded that accuracy and precision are increased, thusmaking it possible to quantify small differences in soilcolor that are difficult to quantify using only visual colormatching. Post et al. (1993) reported results of the useof the Chroma Meter,1 (Minolta Corp., Ramsey, NJ)for making precise color measurements on soils. Theycompared colorimeter measurements with soil colors thathad been determined by many soil scientists as explainedin Soil Survey Division Staff (1993). Post et al. (1993)also evaluated color precision measurements made usingthe traditional field Munsell color book and matchingthe soil to the nearest chip. They concluded that soilscientists agree on the same color chip 52% of the time,

1 Trade names and company names are included for the benefit of thereader and do not constitute an endorsement by the authors.

1810 SOIL SCI. SOC. AM. J., VOL. 58, NOVEMBER-DECEMBER 1994

and there is 71% agreement when considering only asingle color component. These authors further concludedthat the Chroma Meter colorimeter has excellent potentialfor making quantitative color measurements, and it couldbe successfully used to quantify soil colors.

The use of orbiting land satellites (e.g., Landsat) toinventory and monitor natural resources has been investi-gated by many researchers. Lucas (1980), Horvath(1981), and Horvath et al. (1980, 1984) collected largequantities of data on earth surface features and vegetationcharacteristics of rangeland surfaces and related thesevariables to reflected energy recorded by Landsat. Theseresearchers concluded that the color and particle size ofthe soil material, coupled with its relationship to thegeologic parent material from which it was derived,were most strongly correlated with spectral reflectancemeasured by hand-held radiometers or Landsat. TheMunsell soil color components, especially Munsell value,and the size and percentage of coarse fragments wereidentified as being particularly important. This conclu-sion was most accurate when vegetative cover was low(<25% cover). As vegetative cover increased or slopesbecame very steep, the correlation of these two propertieswith Landsat spectral data decreased.

Many studies have been completed showing significantrelationships between soil properties and the spectralreflectance of soils in the visible and near-infrared por-tions of the electromagnetic spectrum (Stoner, 1979;Stoner and Baumgardner, 1981; Baumgardner et al.,1985; DaCosta, 1979; Shields et al., 1968; Condit,1970). These researchers emphasized how the soil com-ponents of organic C, Fe oxides, texture, water, andsalts aifect spectral reflectance. Correlations withMunsell hue, value, and chroma were also listed, butthe color measurements were made using the visualcomparison procedure (Soil Survey Division Staff,1993). Escadafal et al. (1988, 1989) investigated therelationships between Munsell soil colors and Landsatspectral response, especially on arid landscapes, andreported that the Munsell color parameters of hue, value,and chroma were significantly correlated with Landsatdata.

Our research evaluated a large number of field andlaboratory measurements of earth surface features andvegetation characteristics found on semiarid rangelandsin southeastern Arizona. We were particularly interestedin quantitatively evaluating the color of earth surfacefeatures (soil and geologic parent material), and thenrelating the color measurements to multispectral digitalcount reflectance data measured by orbiting satellites.This research has three objectives: (i) to measure accu-rately and quantitatively the color of earth surface materi-als found on a semiarid rangeland in southeastern Arizonausing the Chroma Meter colorimeter; (ii) to comparethe color of various particle sizes to determine if theyare different; and (iii) to determine the relationshipsbetween the color of earth surface features and the spec-tral characteristics of this rangeland as recorded by Lan-dsat.

MATERIALS AND METHODSStudy Area Description

This study was conducted on desert rangelands in the Basinand Range region of southeastern Arizona (Soil ConservationService, 1981). The study area covers 33000 ha consistingof gently undulating to steep-sloped hills that range in elevationfrom 600 to 1300 m above sea level. There are two majorgeologic subdivisions, the Quaternary-Tertiary alluvial depos-its that make up the foothills and the base of the mountains,and Precambrian schists and granites that form the mountainranges (Krieger, 1974a,b). The area is in two Major LandResource Areas, namely the Southeastern Arizona Basin andRange, Chihuahan Semidesert Grassland (D41-3), and theCentral Arizona Basin and Range, Upper Sonoran Desert Shrub(D40-1) (Soil Conservation Service, 1981). The northern andsouthern boundaries of the study area are at latitudes 33°OOWand 32° 5220", and the western and eastern boundaries of thestudy area are at longitudes 111°00'00" and 110°52'30". Inthis region, the annual precipitation ranges from 250 to 400 mmand the average monthly air temperatures are approximately 10to 15 °C in the coldest winter month and 25 to 30 °C in thewarmest summer month.

A wide range of soils can be found in the study area. Atthe family level (Soil Survey Staff, 1975), all soils have amixed mineralogy and a thermic temperature regime; however,the particle size classification is quite variable, with sandy-skeletal and loamy-skeletal being the most extensive. Typic,lithic, and ustollic subgroups are common in the area, andTorriorthents, Haplargids, Calciorthids, and Ustorthents arethe major great groups present in the area. The predominantsurface horizon particle size is a gravelly or cobbly sandyloam or loamy sand.

Description of Landsat DataLandsat Multispectral Scanner data collected on 7 June 1976

were used for this research. This was Landsat 2 digital data,which are identified by the National Aeronautics and SpaceAdministration (NASA) as Scene I.D. 8251217092500. At thetime the data were collected, the solar elevation above thehorizon was 59 °, and the solar azimuth angle 99°. The multi-spectral scanner sensors recorded data for four bands: Band4, 0.5 to 0.6 urn (green); Band 5, 0.6 to 0.7 urn (red); Band6, 0.7 to 0.8 jim, and Band 7, 0.8 to 1.1 um. Bands 6 and7 are both in the near-infrared portion of the electromagneticspectrum. Relative reflectances are recorded as digital numbersfor Bands 4, 5, and 6 are quantized across a range from 0 to127, where 0 is the lowest quantization level. Band 7 recordsvalues from 0 to 63, and the ground resolution (pixel size) isan 80 by 60 m rectangular shape, with the pixel being longestin the north-south direction. The 17 June data were selectedbecause they were acquired near the summer solstice whenthe sun was highest in the sky, thus minimizing shadow effects.Also no recent rains were recorded in the area, so the landsurface was dry and reflectance from photosynthesizing vegeta-tion was minimal.

An unsupervised spectral map of the study area was preparedfrom a computer analysis of the multispectral scanner data.Experience had shown that Band 5 was the best band to usefor relating surface features of arid landscapes to satellite data,so we first created a gray-scale single channel map of thegeneral area of interest using Band 5 data. A geometricallycorrected line printer image at a scale of 1:24000 was pro-duced, and this product was compared with other available

POST ET AL.: SOIL COLOR AND LANDSAT REFLECTANCE 1811

reference data, especially U.S. Geological Survey topographicmaps, aerial photographs, and a general soil map of the studyarea. Based on our knowledge of the study area, we initiallydecided that 5 to 10 classes would satisfactorily describe thespectral variability of the area. The next step used a clusteringalgorithm on a representative training area, and the area wasclassified using all four bands. The clustering algorithm groupsthe data into natural groupings, using the multispectral scannerdata, on the basis of the inherent numerical properties withinthe data. The computer calculates statistical distances betweenall pairs of cluster classes to be used in making a judgmenton the differences between the classes. Our analysis identifiedseven spectral classes as being numerically different. The statis-tics of the training area's seven spectral classes were used ina pattern recognition algorithm to classify each data pointwithin the study area, and a 1:24000 line printer map wasgenerated. Data from all four Landsat bands were used tocreate the final map, which is referred to as an unsupervisedspectral map. This classification approach and its mathematicalbasis is presented in Schowengerdt (1983) and other remotesensing, image processing references.

Twenty-three sample sites were chosen, and each samplingarea was 240 m in length and 180 m in width (4.3 ha), whichcorresponds to a 3 by 3 pixel area on the line printer map.The number of sample sites within a particular spectral classwas selected, in part, to be proportional to the number ofpixels in that class, and also based on motor vehicle accessibilityto the sites. The center of each sampling site was located asaccurately as possible and four plot replications of field datawere collected. Plot size for the replications was chosen to bea circular area with a 5-m radius, and plot locations within asample site were selected using a random numbers table togenerate distances in paces from the center of the sample site;direction was selected using a spinner on a board.

Description of Sample Site Surface FeaturesThe earth surface features evaluated were: geologic soil

parent material, slope gradient, surface color, and surfacecover estimates. The surface cover estimates were separatedinto bare soil <2 mm in diameter, soil and geologic material>2 mm in diameter, trees and shrubs, and perennial grasses.There was very little perennial grass cover, so the trees, shrubs,and grass cover were combined and are referred to as vegetativecover. Annual grasses were not considered because they weresenescent and partially decomposed, thus making them difficultto evaluate. These surface covers were further characterizedas follows: the sand, silt, and clay content of the <2-mm soilfraction was estimated by field texturing; the >2-mm fractionwas also separated into gravel (2-76 mm), cobbles (76-254mm), stones (254 to =610 nm), and rock outcrop. All coverestimates were made using the ocular estimation method. Theocular method we used was a consensus estimation made bytwo or more observers on the percentage of the land surfacecovered by various soil particle sizes and different vegetationtypes. Extensive pre-field training had been completed on sitesof known cover percentages prior to data collection.

Table 1 summarizes the study site characteristics in termsof the seven spectral classes used in this research. There wasa total of 23 sample sites where four replicate sets of datawere collected and the mean computed from these four replica-tions was used for the data analyses completed on each site.The average surface cover conditions for these seven classesranged from 25 to 51% <2-mm soil, 43 to 59% >2-mm rockfragments, and 6 to 16% vegetative cover.

Table 1. Study site characteristics related to seven spectral classes.Spectral classcharacteristics_______1 2 3 4 5 6 7

Selected land characteristics of spectral classes10573 8764 8429 5398 10489 13434 11512

4 4 4 2 3 2 41207 909 848 1198 691 1115 996

10 3 6 7 3 19 24

Pixels, no.Sample sites, no.Mean elevation, mMean slope, %

<2-mm soil>2-mm rock fragmentsVegetation

Cover characteristics of spectral classes, %34 42 35 25 51 44 3556 47 50 59 43 43 5510 11 15 16 6 13 10

Landsat reflectance digital numberBand 4 (0.5-0.6 |im)Band 5 (0.6-0.7 (irn)Band 6 (0.7-0.8 |un)Band 7 (0.8-1.1 urn)Sum of four bands

40657229206

39616827195

37586426185

36536427180

36546024174

34495823164

32455221150

Determination of Soil and Rock Fragment ColorSamples of soil and rock fragments smaller than « 50 mm

were collected from each of the 23 sample sites from the 0-to 2-cm depth and brought to the laboratory. These sampleswere separated into the <2-, 2- to 5-, and >5-mm size fractionsfor further color characterization. Color was determined onthe <2-mm size fraction using the Munsell system and matchingthe soil color to the nearest color chip in the Munsell soilcolor book (Soil Survey Division Staff, 1993). Colorimetriccolor measurements using a Minolta Chroma Meter (ModelCR-200) were made on all three size fractions as follows: the<2- and 2- to 5-mm size fractions were evenly distributed ona flat surface to provide a thickness of 3 to 10 mm, and thenthe Chroma Meter color measuring probe was rested in avertical position on the surface of the soil or rock fragmentmaterials and color replicate readings were taken and a meancomputed. For the >5-mm size fraction, representative rockfragments larger than « 20 mm were selected and the probewas placed directly on the fragment surface and a readingtaken. From three to five readings were taken for the >5-mmsize fraction, and a mean color was computed. All colormeasurements were made on air-dry materials.

The Chroma Meter uses a pulsed xenon arc lamp thatprovides diffuse illumination with 90° viewing angle geometryto obtain a reading from an area « 8 mm in diameter. Sensorsmeasure the light reflected from the sample in the entire visibleportion of the spectrum. This reflected light is split by filters,and three photodetector cells read the amount of reflected lightin the blue, green, and red portions of the spectrum. Thespectral response of the photodetector cells duplicate the CIE1931 standard observer functions (Commission Internationalede 1'Eclairage, 1931). These color readings are translated bythe microprocessor in the instrument into four color systems,including the Munsell system. Instrument calibration is accom-plished using a standard white plate of known reflectance. Postet al. (1993) evaluated the precision of the Chroma Meter,compared with the Munsell standard color books, and reportedthe r2 values for a 1988 Munsell soil color book and theMunsell reference book were 0.98 or better. To evaluatestatistically the hue color component, the Munsell notation(such as 9.SYR, 7.SYR, or 5.6YR as recorded by the ChromaMeter) was assigned a numerical notation, as explained in Postet al. (1993). This system assigns the seven hues in the Munsellsoil color book a number from 1 to 7, with 10R = 1, 2.SYR= 2, SYR = 3, 7.5YR = 4, 10YR = 5, 2.5Y = 6, and 5Y


= 7. The value and chroma color notations were used asrecorded by the Chroma Meter, which records the color obser-vation to the nearest one-tenth. Statistical analyses were madeusing the SAS statistical package (SAS Institute, 1985).

RESULTS AND DISCUSSIONColor Measurements of Three Soil and

Rock Fragment Particle SizesSoil scientists routinely record the color of the fine

earth (<2-mm) soil fraction, but usually do not determinethe color of rock fragments hi the soil. In skeletal soilscommonly found on rangelands, the color of the rockfragments is quite variable and difficult to identify dueto variability of rock type, degree of weathering, andpresence of coatings on rock surfaces. Table 2 presentsthe mean hue, value, and chroma color components forthe three size fractions collected from each spectral class.An analysis of variance was used to determine if thecolors were significantly different, and a t test was madeto test which means were significantly different amongthe three particle-size categories.

There are significant differences in the color of thethree particle-size categories; however, the differencesare not consistent among the seven spectral classes. Themost consistent color difference is for the value colorcomponent, where in all cases the value of the >5-mmsize fraction is greater than the <2- or 2- to 5-mm sizefractions. This is very important because the value colorcomponent is usually the most important color componentaffecting the amount of energy reflected from any surface.The greater the Munsell value, the greater the percentageof energy reflected. The Munsell neutral scale (MunsellCo., Baltimore, MD) reports that an N 4/0 color chipwill reflect 12%, whereas N 5/0 and N 6/0 chips reflect20% and 30%, respectively, in the visible portion of theelectromagnetic spectrum. In the field, we noted that thegravel and cobble size particles had more rock fragments

with whitish quartz minerals present. This mineral com-position difference between the <2-mm fraction and therock fragments probably explains why the value colorcomponent was greater for the two larger particle sizes.

Hue and chroma color components have a lesser effecton reflectance percentage, but scientists hi the field arevery aware of slight chromaticity changes, which in-cludes that aspect of color related to the wavelength(hue) and color purity (chroma). The data for hue andchroma are not consistent for all spectral classes, butthe hue numbers are generally lowest for >5-mm sizefractions, which means that this fraction is generallyredder. The opposite is true for chroma, since the <2-mmfraction tends to have a lower chroma. The color mea-surement coefficient of variation is greater for the 2- to5- and >5-mm size categories than for the <2-mm mate-rial. This is probably because the >2-mm rock fragmentshave multiple color properties, and the large particleshave uneven particle surfaces that make the replicationof measurements more difficult.

Earth Surface Features Color and Landsat SpectralReflectance Relationship

Table 3 presents a matrix that shows the simple linearregression correlations between the Landsat digital num-bers data hi each spectral band and the color characteris-tics of each sample site. These data were first graphicallyplotted and the linear relationship among all variableswas verified. The <2-mm color relationships are forcolor determinations made matching the soil to the nearestMunsell soil color chip and Chroma Meter colors. Forthis size fraction, the correlations of color componentsand Landsat digital number were best with the two visiblebands, and both hue and value were significantly corre-lated (P < 0.05). These were positive relationships, butchroma showed a negative relationship; however, thechroma correlations were not significant.

Table 2. Mean hue, value, and chroma for the three particle-size categories within each spectral class using data from all sample sites.

Spectralclass

1(n =2(n =3(" =4(n =5(n =6(n =7(n =

14)

16)

15)

8)

12)

8)

16)All classes(n = 89)

<2 mm

4.56AJ(3.8)§4.32A

(1.9)4.44A

(2.5)4.27A

(3.2)4.48A

(2.3)4.13A

(3.9)4.12AB

(5.8)4.34A

(3.3)

Huet

2-5 mm

4.84A(4.4)4.66B

(4.1)4.29AB

(5.1)4.21A

(3.7)4.52A

(8.1)4.04A

(4.9)4.44A

(9.0)4.47A

(5.6)

>5 mm

4.76A(12.6)

4.25A(8.2)3.96B

(10.0)4.06A

(4.2)4.16A

(9.8)3.60A

(16.4)3.68B

(11.4)4.09B

(10.4)

<2 mm

4.69A(7.8)4.76A

(5.2)4.70A

(7.8)4.65A

(6.2)4.67AB

(4.0)4.04A

(6.9)4.23A

(7.6)4.56A

(6.5)

Value

2-5 mm4.56A

(H.3)4.64A

(8.1)4.64A

(7.4)4.53A

(13.3)4.44A

(10.6)3.91A

(7.6)3.70B

(9.6)4.36B

(9.7)

>5 mm5.86B

(6.6)5.45B

(9.9)5.47B

(17.5)5.35B

(8.3)4.85B

(10.5)4.58B

(8.3)4.40A

(6.6)5.16C

(9.4)

<2 mm

3.02A(12.6)

3.08A(5.9)2.87A

(10.9)3.17A

(10.9)2.87A

(6.1)3.09A

(13.5)2.98A

(16.2)3.00A

(10.9)

Chroma

2-5 mm

2.67A(18.1)

2.51B(15.7)

2.53AB(16.0)

2.94A(18.6)

2.43B(18.2)

2.84A(16.1)

2.11B(27.7)

2.53B(18.6)

>5 mm3.09A

(24.1)2.71B

(15.5)2.43B

(27.7)3.01A

(16.6)2.37B

(21.9)2.73A

(17.1)2.51B

(22.7)2.67B

(20.8)

t Hues ranked using a system where 10R = 1, 2.5YR = 2, SYR = 3, 7.5YR = 4, 10YR = 5, 2.5Y = 6, and 5Y = 7 (Post et al., 1993).^ Different letters indicate significantly different values (P £ 0.05). Only the hue, value, and chroma within each spectral class can be compared. Differences

between spectral classes were not computed.§ Coefficient of variation (%) given in parentheses.


Table 3. Simple linear regression correlations between the Landsat multispectral scanner digital numbers in the four spectral bands (4,5, 6, and 7) and the color characteristics of the 23 sample sites.

Surface featureHue by color book (<2 mm)Hue by Chroma Meter (<2 mm)Value by color book (<2 mm)Value by Chroma Meter (<2 mm)Chroma by color book (<2 mm)Chroma by Chroma Meter (<2 mm)Redness rating by Chroma Meter (<2 mm)Hue by Chroma Meter (weighted color)!Value by Chroma Meter (weighted color)Chroma by Chroma Meter (weighted color)

4(0.5-0.6 |im)

0.56**0.72**0.59**0.73**

-0.39-0.21-0.74**

0.73**0.80**

-0.02

5(0.6-0.7 (im)

0.54**0.66**0.51*0.63**

-0.37-0.13-0.66**

0.65**0.73**0.07

6(0.7-0.8 \un)

0.43*0.55**0.44*0.58**

-0.280.04

-0.53**0.58**0.72**0.23

7(0.8-1.1 urn)

0.330.45*0.300.47*

-0.160.14

-0.43*0.50*0.65**0.35

Sum of fourbands

0.49*0.62**0.48*0.62**

-0.31-0.04

0.61**0.64**0.74**0.16

*, ** Significant at P < 0.05 (r > 0.43) and 0.01 (r > 0.53), respectively.t Weighted color refers to the proportion of the specific color components within the <2- or >2-mm size ranges.

The Chroma Meter hue and value correlation coeffi-cients were always greater than the color book colors,ranging from 0.12 to 0.16 higher in the two visiblebands. Hue and value were also significantly correlatedto the near-infrared bands, except for Band 7 for thecolor book color. The sum of the four bands sometimesis called soil brightness, and frequently remotely senseddata is related to this digital number rather than theindividual band numbers. These correlations were alsosignificant for both hue and value. We expect the visiblebands to have the higher r values because the spectralwavelengths are included in those measured by theChroma Meter, making these bands the most useful forevaluating relationships between soil characteristics andremotely sensed data.

The three Munsell color components were also con-verted into a redness rating according to the followingexpression by Torrent et al. (1980):

Redness rating = (10 - hue) x chromavalue

where the chroma and value are the numerical valuesof each, and the hue in the numerical number precedingthe YR in the Munsell hue. Redness ratings were com-puted for the <2-mm material from the 23 sample sitesusing the Chroma Meter color data. Table 3 includesthese data, and the r values are almost identical to thehue and value correlations. The advantage of using thisrating is that it combines the three color componentsinto a single number.

The rangeland surfaces in our study area had =50%coverage with rock fragments >2 mm (Table 1). Thedata in Table 2 show that the color characteristics weredifferent among the different size classes, and there werevariable amounts of these particle sizes on the 23 samplesite land surfaces. A pixel's reflectance, as recorded byLandsat, is an average of the entire pixel area. Pixelshave different amounts of rock fragments and fine earthon the land surface, which also are different colors.Therefore, a weighted color for hue, value, and chromafor each size fraction was computed to account for thedifferences in the color properties and the proportions ofdifferent-sized particles on the land surface. The weightedcolor for each of the 23 sample sites was calculated asfollows:

Cw =C(<2 mm [%]) + C(2 to 76 mm [%]) C(>76mm[%])

100 - vegetative cover (%)Cw = color weightedC = color component (hue, value, and chroma) un-

weighted

The colors reported in Table 2 were used to makethese calculations. The particle sizes for which colorswere determined in the lab and the methods of subdividingcover in the field are different. In the equation above,we assumed that the color of the 2- to 5-mm size fractionwas a representative color for the 2- to 76-mm sizefraction, and the >5-mm color was a representative colorfor the >76-mm size fraction. These assumptions arereasonable because many of the sample sites had a pre-dominance of pea-size gravel particles on the soil surface,which were « 2 to 5 mm in size. Furthermore, the colorsin Table 2 for the >5-mm size fraction were made onparticles >20 mm in size, as explained above. Also,visual observations of the land surfaces in the fieldshowed that particles >10 mm were basically the samecolor regardless of their size.

Table 3 presents the correlations relating weightedcolor components to the Landsat digital number. Thehue r value relationships between the Chroma Meter huefor the <2-mm size fraction and the hue weighted colorare mostly unchanged, but the correlation coefficientsfor the value color component increase from 0.73 to0.80 in Band 4 and from 0.63 to 0.73 in Band 5. Theincreases in Bands 6 and 7 are larger, being 0.14 and0.18, respectively. Using weighted colors improves thecorrelations and should be done when possible.

Multiple linear stepwise regression was used to predictLandsat digital numbers from soil color components forthe <2-mm soil fraction by visual estimation to the nearestMunsell color chip and by Chroma Meter, and forweighted color calculated using the Chroma Meter colors(Table 4). The partial coefficients of determination arelisted for each band and the sum of the four bands. TheR2 is much lower when colors are measured to the nearestchip than when colors are measured with the ChromaMeter. Post et al. (1993) stated that the precision ofcolor chip estimations are improved if "in-between" color


Table 4. Multiple linear stepwise regression to predict the Landsat multispectral digital numbers from the hue,components of earth surface features.

Dependentvariable

Band 4Band 5Band 6Band 7Sum

Band 4BandSBand 6Band?Sum

Band 4BandSBand 6Band?Sum

value, and chroma color

Partial R2

Equation for independent color variables! Xi

Color of <2-mm soil: Visual measurements to nearest Munsell color chip= -2.85 + 2.63 (value) + 5.13 (hue) 0.34= - 16.29 + 10.20 (hue) + 4.07 (value) 0.29= 36.76 + 5.37 (value) 0.19= 3.64 + 4.30 (hue) 0.11= - 12.46 + 26.46 (hue) + 11.79 (value) 0.24

Color of <2-mm soil: Chroma Meter color measurements= -50.92 + 4.11 (value) + 12.09 (hue) + 5.33 (chroma) 0.53= -122.72 + 26.16 (hue) + 13.84 (chroma) + 5.14 (value) 0.44= - 135.42 + 4.57 (value) + 27.77 (hue) + 19.03 (chroma) 0.34= 2.94 + 4.97 (value) 0.22= -384.04 + 14.80 (value) + 80.51 (hue) + 49.28 (chroma) 0.39

Color weighted proportional to particle sizes: Chroma Meter color= - 14.03 + 6.31 (value) + 5.06 (hue) 0.64= -31.37 + 11.07 (value) + 8.62 (hue) 0.53= -48.04 + 8.59 (value) + 11.52 (hue) + 8.16 (chroma) 0.52= - 30.73 + 3.02 (value) + 6.07 (hue) + 5.92 (chroma) 0.42= - 139.29 + 27.08 (value) + 33.72 (hue) + 18.80 (chroma) 0.55

X2

0.470.39

0.33

0.710.650.44

0.52

0.850.690.620.490.70

X3 P>F

0.00170.00670.03530.12140.0176

0.78 0.00010.69 0.00010.66 0.0001

0.02310.70 0.0001

0.00010.0001

0.71 0.00010.68 0.00010.75 0.0001

t All variables entered into the model are significant at the 0.15 level.

chip estimations are made, and the /?2 would probablybe improved if this had been done.

The Band 4 digital numbers are most strongly corre-lated with the soil color components, having an R2 of0.78 and 0.85 with the <2-mm and weighted colors(determined by Chroma Meter), respectively. Althoughthe /?2 between Band 4 and the nearest color-chip colorcomponents was only 0.47, this is a very significantrelationship, as P > F is >0.0017. The simple linearregression helps to clarify relationships; however, themultiple linear regression is better because it combinesthe color components into a single model. It is recom-mended that this model be used in studies relating thesoil color components to Landsat digital numbers, andthat a weighting of colors be done if the land surfacehas rock fragments present.

Other Earth Surface Features and Landsat SpectralReflectance Relationship

We also investigated how other land and soil character-istics correlated to Landsat digital numbers. Relevantdata are presented in Table 5. The proportions of the

following surface features were not significantly corre-lated (P > 0.05): soil <2 mm, soil >2 mm, trees andshrubs, perennial grasses, total vegetative cover, gravel,and rock outcrop. The proportions of the following fea-tures were significantly correlated to the Landsat digitalnumber: slope, sand, silt, clay, and cobbles and stones.We calculated both a nonweighted and a weighted corre-lation coefficient for the various particle-size categoriesas follows. The sum of the <2-mm fraction, rock frag-ments >2 mm, and vegetation equals 100%. However,the two mineral soil fractions were further characterizedas to the proportion of different sized particles found onthe earth's surface in each of these particle size rangesby recalculating the percentages as follows:Particle size, weighted (%) =(<2 mm on land surface [%]) (particle size, unweighted[%])

100%For example, a landscape devoid of vegetation mighthave 35% of its area covered by particles <2 mm and65% covered by >2-mm sized particles. Assuming the<2-mm fine earth fraction had 75% sand and 15% clay,

Table 5. Simple linear regression correlations between the Landsat multispectral scanner digital numbers in the four spectral bands (4,5, 6, and 7) and the land surface features of the 23 sample sites.

Surface feature

Soil >2 mmSoa<2mmTrees and shrubsPerennial grassesTotal vegetative coverSlopeSand (in <2 mm and weighted average)!Silt (in <2 mm and weighted average)Clay (in <2 mm and weighted average)Gravel (in >2 mm and weighted average)Cobble and stones (in >2 mm and weighted average)Rock outcrop (in >2 mm and weighted average)

4(0.5-0.6 um)

0.010.02

-0.02-0.39-0.02-0.67**

0.78**, 0.26-0.51*, -0.30

-0.80**, -0.64**0.28, 0.15

-0.48*, -0.52*0.20, 0.19

5(0.6-0.7 um)

-0.020.010.02

-0.310.02

-0.67**0.75**, 0.23

-0.56**, -0.32-0.74**, -0.58**

0.37, 0.19-0.50*. -0.53**

0.08, 0.07

6(0.7-0.8 um)

-0.110.090.14

-0.280.14

- 0.55**0.64**, 0.10

-0.51*, -0.37-0.63**, -0.54**

0.30, 0.25-0.44*, 0.47*

0.10, 0.09

7(0.8-1.1 um)

-0.100.050.210.190.21

-0.49*0.55**, 0.08

-0.46*, -0.33-0.52*, -0.44*

0.33, 0.23-0.43*, -0.47*

0.04, 0.03

Sum of four bands

-0.050.040.10

-0.230.09

-0.62**0.70**, 0.17

-0.53**, -0.35-0.69**, -0.57**

0.32, 0.22-0.48*, -0.51*

0.10, 0.10

*, ** Significant at P < 0.05 (r > 0.43) and 0.01 (r > 0.53), respectively.t Weighted average refers to the proportion of the specific component within the <2- or >2-mm particle-size fractions.


then the weighted sand value would equal 26%, andthe weighted clay value would equal 5%. A similarcalculation was made for the components of the >2-mmfraction. The reason for making these calculations is toapproximate the percentage of the various particle sizesexposed on the surface that would reflect energy fromthe land surface and be recorded by Landsat.

The clay and sand content in the <2-mm fine earthfraction and the slope were very significantly (P > 0.01)correlated with the Landsat bands, with sand showing apositive relationship, while clay and slope were negative.The weighted particle size r values were much lowerthan the unweighted. Cobbles and stones and silt contentswere also significantly correlated to the digital numbers,but the r values were =0.2 to 0.3 lower. Consistently,the strongest correlations were again in Bands 4 and 5,and Band 7 was the least correlated to these surfacefeatures.

DISCUSSIONMany soil characteristics have been shown to be sig-

nificantly correlated to landsat digital number; howeverthese properties have a strong covariance relationshipwith soil color. The objective is to select those soilproperties that are the most strongly related to the re-flected energy recorded by Landsat, then these propertiesshould be determined as precisely and as accurately aspossible, and used to interpret Landsat data. In this studyMunsell hue and value and the sand and clay content inthe <2-mm soil fraction were most strongly correlatedto reflectance measured by Landsat. We also found therewas a strong covariance between the color, texture, andslope for our sample site. This suggests it may not alwaysbe necessary to measure all of these characteristics. TheR2 relating the three Chroma Meter color componentsto Band 4 is 0.78 for the <2-mm soil fraction, and 0.85when a weighted color is computed. This increase inthe R2 values shows that it is important to preciselyand accurately measure the color of both soil and rockfragments on arid rangelands where remotely sensed dataare being used.

Tueller (1987) stated that it is important to identifyand measure via remote sensing a minimum of ecosystemcharacteristics that strongly, consistently, and mechanis-tically correlate to complex phenomena that occur withinthe arid land ecosystem. These characteristics, whenselected for an arid land ecosystem, enables one to useremotely sensed data to study various complex biologicaland physical processes. Tueller (1987) further stated thatthere is great spectral variation on rangelands, and careshould be taken not to complicate too much the interpreta-tion made of spectral maps produced for these lands.

Researchers have pointed out that the color of theearth's surface features on nonvegetated or sparsely vege-tated landscapes is clearly very strongly correlated toenergy reflected from these land surfaces. Escadafal etal. (1989) reported that determining soil color using theMunsell color matching technique is not very precise;however Munsell color was still very strongly correlatedto Landsat data. Ringrove et al. (1989) also reported a

high degree of correlation between Landsat and theFrench Systeme Probatoire de'Observation de la Terresatellite system (SPOT) responses and the Munsell valuecolor component for rangelands in Botswana, Africa.They also pointed out that the CaCo3 content of thesoil was important, because it causes the value colorcomponent to increase. Price (1990) studied the high-resolution (0.01-n.m) reflectance spectra for the fine earthfractions of >500 moist soils, and identified four broad-band spectral measurements as sufficient to describe thespectra of soils. He further concluded that the semiquanti-tative Munsell color matching procedure explained inSoil Survey Division Staff, 1993 is apparently capableof discriminating the major spectral variability of U.S.soils. The importance of soil color and how it correlatesto spectral reflectance was also reported by Baumgardneret al. (1985), Horvath (1981), Horvath et al. (1984,1980), and Stoner (1979).

Ideally, the intent in rangeland ecosystems is to identifya minimum number of variables that are strongly corre-lated to the reflected energy measured by Landsat. Thecolor of the soil and rock fragments has the greatestpotential, but a major drawback to using color data inthe past has been the lack of precision in color measure-ments. Soil colors can now be precisely measured, asexplained in Fernandez and Schulze (1987), from re-flectance spectral curves; however, the procedure is notadapted to use under field conditions. The portableChroma Meter has this potential, and precise color mea-surements of soil and rock materials are possible.

This research shows that the color of earth surfacefeatures are strongly correlated to the amount of energyreflected from sparsely vegetated arid landscapes wherethe slopes are less than «25 %. Steeper sites may givedifferent results caused by sun angle or shadow effects,which were not studied here. The use of colorimetersallows acquisition of the needed precise color measure-ments. It must be remembered that reflected energy perse is dependent on the chemical composition, particlesize, surface conditions, and other features of land sur-faces. These factors influence the color of a material,which we precisely measured in this study. If colorime-ters are not available, Post et al. (1993) has shown thatsoil scientists can measure "in-between" the Munsell colorbook chips, and the precision of the color measurement isimproved. The value color component is usually themost important color parameter affecting reflectance ofenergy from land surfaces; however, hue and chromaare also important. This research further showed thatdifferent particle-size fractions often have different col-ors, so color measurements of botii the <2-mm soilfraction and the >2-mm rock fragments should be mea-sured.

relations between soil color and landsat reflectance on semiarid rangelands

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