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DIVISION S-6-SOIL & WATER MANAGEMENT & CONSERVATION

Predicting Soil Albedo from Soil Color and Spectral Reflectance Data

Dep. of Soil, Water and Environmental Science, Shantz Building 38, Rm. 429, P.O. Box 210038, 1200 E. South Campus Drive, Univ. of Arizona, Tucson, AZ 85721-0038 USA

postdf{at}ag.arizona.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
The albedo of earth surface features, such as soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two soil properties, soil color and soil moisture, on soil albedo. Two soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the soil and no water glistens on the soil surface. The albedos for 26 U.S. soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 µm. Soil colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 µm (measured in 0.1-µm increments) were determined with a multispectral radiometer. All measurements were made on <2-mm smooth soil surfaces, and the dry and wet data were combined for statistical analyses. Soil albedos were significantly correlated with Munsell soil color value , blue , green , red , near infrared (NIR), , and sum of the four bands ; however, the slopes and intercepts for these relationships were different. The 52 spectral curves yielded nine cluster groups, which mostly related to the Munsell soil color value and soil albedo soil characteristics. The 0.3- to 2.8-µm albedos of smoothed soils can be accurately estimated using the regression relationship: soil albedo (0.3–2.8 µm) = 0.069 (color value) - 0.114. Using the regression equations presented here, spectral reflectance data in selected visible and NIR bands can also be used to predict albedo.

Abbreviations: K, reflected radiation from soil surface • K, incoming radiation from the sun • NIR, near infrared • , spectral wavelength

	INTRODUCTION

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
SOLAR RADIATION is the main source of energy for the global climate system. After interacting with the atmosphere (being both partly absorbed and scattered by it), a fraction of this incoming solar radiation is absorbed at the earth's surface. The remainder is reflected back to the atmosphere, and the ratio of this reflected energy to the incoming radiation is called the albedo of that surface. Reflective optical radiation is defined as propagating electromagnetic energy with characteristic wavelengths between 0.4 and 3.0 µm (Arya, 1988). Radiation beyond 3.0 µm is mostly emitted long-wave radiation (sometimes referred to as terrestrial radiation) from the earth's surface and will not be considered here. Reflective radiation energy controls most processes at the surface of the earth, including the exchanges of heat, moisture, and C, as well as all biological activity.

When discussing albedo, the spectral wavelength () between the 0.4- to 3.0-µm range must be noted. Albedo, for example, is sometimes defined for < 0.7 µm, which is basically the visible portion of the spectrum, for > 0.7 µm, or for wavelengths that include both ranges. An Eppley pyranometer is commonly used in climatological studies and integrates the amounts of reflected energy between 0.3 to 2.8 µm. As a general observation, soil albedos for > 0.7 µm are about twice those for < 0.7 µm (Dickinson et al., 1993).

The National Center for Atmospheric Research (Dickinson et al., 1993) developed a generalized table of soil properties used in circulation models of land-surface processes, and soil color and moisture are the two properties used to predict soil albedo. It has long been recognized that bare soil albedo is dependent on the moisture status of the soil (Angstrom, 1925; Brooks, 1952; Bowers and Hanks, 1965; Planet, 1970; Graser and Van Bavel, 1982). The observed darkening of wet soil is due to the optical effects of a thin liquid layer on the soil surface. Idso et al. (1975) completed a detailed study to evaluate the bare soil albedo for the Avondale loam soil (fine-loamy, mixed, superactive, calcareous, hyperthermic Typic Torrifluvent) and directly related it to the moisture content of the soil.

Soil color, particularly the Munsell color value component, has been identified in many studies affecting the amount of energy reflected from soil surfaces (Baumgardner et al., 1985; Escadafal et al., 1989; Stoner and Baumgardner, 1981; Condit, 1970; Post et al., 1994). The Soil Survey Staff (1993) describes in detail how soil color is determined using the Munsell soil color charts, and Post et al. (1993) studied the consistency among soil scientists in determining soil color. Post et al. (1993) also evaluated using a Chroma Meter to measure soil colors and concluded that commercial colorimeters have great potential as tools to precisely measure soil colors. These authors further discuss how color changes from the dry to a wet condition, and the greatest change is for the Munsell color value component, which is also strongly correlated with soil albedo.

Other researchers have evaluated the relationship between soil color (soil reflectance) and the organic matter and Fe content of soils. With respect to color and organic matter, relationships are poor when soils are collected from an extensive geographic area, and the soils have a wide range of soil properties. If studies focused on a limited geographic area and the soils have similar morphologic properties, the relationships are much better (Schulze et al., 1993; Alexander, 1969; Fernandez et al., 1988). Schwertmann (1993) reported how various Fe oxides can be recognized by their mineral specific colors; however, Bigham et al. (1978) pointed out a common misconception among soil scientists, namely, that soil color redness is primarily determined by the amount of Fe oxide present. It is the type of Fe mineral, namely the hematite content, that usually determines the redness of the soil. Torrent and Schwertmann (1987) and Torrent et al. (1983, 1980) successfully related soil color to the hematite content of soils and sediments.

Roughness of soil surfaces also affect soil albedo (Zobeck and Onstad, 1987; Cierniewski, 1987; Potter et al., 1987; Cresswell et al., 1993). The general consensus is that soil color, soil moisture, and surface roughness are the three factors that most determine soil albedo. Since soil color changes as a function of the soil's moisture content, the absolute moisture content may not be needed to predict the soil's albedo if color parameters are accurately measured.

There is also an interest in calculating soil colors from reflectance spectra collected in either the laboratory or the field. The reflectance spectrum itself gives little direct information on the color of a sample in terms understood by most soil scientists. However, the color of a sample expressed in the Munsell color notation can be calculated from its visible reflectance spectrum (Judd and Wyszecki, 1975; Chamberlin and Chamberlin, 1980; Wyszecki and Stiles, 1982). Fernandez and Schulze (1987) and Escadafal et al. (1989) have also calculated soil colors from reflectance spectra using data between 360 and 830 nm. These researchers did not relate this data to soil albedos.

Data from various remote sensing systems have been used to estimate albedo across large areas (Berkofsky, 1976; Pease and Nichols, 1976; Robinove et al., 1981; Brest and Goward, 1987; Dickinson et al., 1993). It is therefore desirable to use ground-based or satellite multispectral radiometers and relate the percentage of reflected energy in discrete spectral band wavelengths to albedo measurements made with instruments like the wide band (0.3–2.8 µm) Eppley pyranometer.

The objectives of this research were (i) to determine the empirical relationships between the albedo of <2-mm sieved soil samples for dry and wet soil conditions and soil color and (ii) to develop statistical relationships between radiometric reflectance bands from 0.45 to 0.90 µm and soil albedos. Both of these relationships could then be used as a surrogate property to predict soil albedos for <2-mm, smooth soil surfaces.

Troeh and Thompson (1993)(p. 58) state that "the numerical Munsell color value notation is equal to the square root of the percentage of incident light reflected by the sample being described." The accuracy of this statement will also be considered.

	Materials and methods

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
Twenty-six soils having a wide range of color and texture characteristics were studied. All soils were from either A or Ap horizons, except for the Canelo (clayey-skeletal, mixed, mesic Aquic Haplustalf) and White House (fine, mixed, superactive, thermic Ustic Haplargid) soils, which were from the E and Bt horizons, respectively. These two samples were selected because they had unique color characteristics. The Canelo soil has a high color value of 7.3, and the Whitehouse is quite red, having a hue of 3.8YR and a chroma of 5. Table 1 lists the soil series, family and texture classifications, percentages of the dithionite-extractable Fe, and organic C characteristics of these soils. The procedures used to measure these parameters are described in Soil Survey Staff (1996) and Holmgren (1967). All samples were air dried and passed through a 2-mm sieve, and the albedo, soil color, and radiometric measurements were made on a 0.01-m layer of smoothed soil.

An Eppley black and white hemispherical pyranometer (Model 8-48, Eppley Instruments, Newport, RI¹ ; bandpass 0.3–2.8 µm) was used to measure the reflected energy (K) and incoming radiation (K) for calculation of the soil's albedos.¹ For K the Eppley was inverted and attached to the end of a 1-m-long steel pipe (0.02-m o.d.), which extended out over the center of the tray. The viewing height of the sensor was 0.17 m. Determinations of albedos for small areas are complicated by light reflected to the inverted pyranometer from terrain beyond the area of interest and from the soil shaded by the pyranometer. To ascertain the albedo for the soil area of interest, the fractional amount of radiation received by the pyranometer from each surface component must be known. The albedos reported in this paper were corrected for this as described in Reifsnyder (1967) and Siegel and Howell (1972). All albedo measurements reported in this paper are the mean of albedo measurement made between 30 and 50° sun elevation angles, and the number of readings varied from three to six for the various soils. Matthias et al. (1999) describes in detail the procedure we used to measure albedos for small areas of soil.

With only one Eppley sensor, K and K could not be measured simultaneously. During K measurement by the Eppley sensor, K was estimated using the following protocol. First, the Eppley sensor and a Licor LI-200S (Licor, Lincoln, NE)¹ pyranometer were pointed upward to simultaneously measure K for 120 s. Millivolt outputs from both sensors were recorded at 1-s intervals with a Campbell Scientific 21X Micrologger (Campbell Scientific, Logan, UT)¹. A linear regression equation relating the two time series K values were later developed and the Eppley sensor was then inverted to view the soil surface. After waiting 1 min to allow for full sensor response to radiation from the surface, a series of K values was collected every 1 s for 1 min. During that period the upward-viewing Licor pyranometer provided the K data needed for calculating Eppley K values from the linear regression equation. The albedo was calculated from the ratio: measured Eppley K/calculated Eppley K (in millivolts).

The colors of the 26 soils were measured, dry and wet, with a Model CR-200 Chroma Meter (Minolta Co., Ramsey, NJ)¹. The <2-mm soil samples were evenly distributed on a flat surface to provide a soil thickness of 0.01 m or more, and the measuring probe was rested in a vertical position on the soil surface. The samples were moistened by misting water onto the soil surfaces, and measurements were taken after the water films had penetrated into the soil and no water glistened on the surface. The CR-200 Chroma Meter uses a pulsed xenon arc lamp that provides diffuse illumination with a 0° viewing angle geometry to obtain a reading from the 8-mm-diam. measuring area. Sensors measure the light reflected from the sample in the entire visible portion of the spectrum. This reflected light is split by filters, and three photocells read the color of the incident light in the blue, green, and red portions of the spectrum. The color readings are translated by the microprocessor in the instrument into four color systems, including the Munsell System. Instrument calibration is accomplished using a standard white plate of known reflectance, and recalibrated after 10 to 15 min of use.

The value and chroma Munsell color components are recorded to the nearest tenth. Hue is numerically more complex, and a number from one to seven has been assigned to each of the soil color charts . Post et al. (1993) explains this coding in greater detail, but this code made it easier for field soil scientists to make between-hue estimations, and the range of responses is numerically comparable for the hue, value, and chroma color components. The Chroma Meter measurements were adjusted slightly to agree with field soil scientists' observations on a set of 40 reference soils because Post et al. (1993) showed that soil scientists color estimations are slightly, but consistently, different from the Chroma Meter.

Multispectral reflectance measurements of each soil were made using a Spectron CE-590 spectroradiometer (Spectron Instruments, Denver, CO)¹. Measurements were taken outdoors on cloudless days. The instrument measures the reflected sun's energy at 0.01-µm increments between 0.4 to 0.9 µm (within a 15° field of view). All measurements were made between 30 and 50° sun angles, the same sun angles as the albedo measurements. Reflectances were averaged across the nominal thematic mapper bands: blue (0.45–0.52 µm), green (0.52–0.60 µm), red (0.63–0.69 µm), and NIR (0.76–0.90 µm), plus the sum of these four bands, which is called brightness. Instrument calibration was accomplished using a standard barium sulphate reference plate. These reflectance parameters were also regressed against albedo. Hierarchical cluster analyses of the spectral curves were completed, and the 52 spectral curves (26 soils, dry and wet conditions) grouped into nine clusters. This uses the centroid method, which groups data on the basis of the centers of the clusters and represents a way of grouping the 52 soil spectral curves studied as part of this research (Sneath and Sokal, 1973).

	Results and discussion

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
Table 2 presents the Eppley pyranometer 0.3- to 2.8-µm albedo data and the Chroma Meter hue, value, and chroma. The soils in the table are listed in order of increasing albedos, with the Clover Springs (fine-silty, mixed Cumulic Cryoboroll) wet soil having the lowest albedo (0.048) and Superstition (sandy, mixed, hyperthermic Typic Haplocalcid) dry soil the highest (0.402). The minimum and maximum hue, value, and chroma color components were as follows: hue 2.2 to 5.7, which corresponds with 3.0YR and 1.8Y Munsell notation; values 2.1 to 7.3; chroma 0.9 to 5.8.

View this table: [in this window] [in a new window]   Table 2 The 0.3–2.8m soil albedos, Chroma Meter hue, value, and chroma color data and percentage of reduction in the albedo () from the dry to wet soil condition for the 26 soils

(1)

View larger version (15K): [in this window] [in a new window]   Fig. 1 Relationship between the 0.3–2.8 µm soil albedo and the Munsell color value component

Soil albedo is reduced when it becomes wet; however, the Munsell color value component (lightness and darkness of the soil) also changes when it becomes wet. For the 26 soils we studied, the wet albedo was reduced 32 to 58%, with an overall mean percentage of change of 45%. As an example, the Pima [fine-silty, mixed (calcareous), thermic Typic Torrifluvent] soil dry and wet albedos were 0.220 and 0.115, respectively, which is a 48% albedo reduction. The dry Munsell color values were reduced from 10 to 36%, with an overall mean percentage of change of 27%. For example, if the dry color value was 4.0, one could expect the moist color value to be 2.9. Separate equations for dry and wet soil conditions were tested but did not improve the significance of the relationship.

We also evaluated the relationships between soil albedos and the Fe and organic C content of these soils. The r² correlation coefficients for total Fe content and albedo were: 0.14 dry, 0.12 wet, and 0.07 dry and wet conditions combined. The r² values relating percentage of organic C and albedo were: 0.31 dry, 0.41 wet, and 0.18 dry and wet conditions combined. Although the Fe and organic C content of soils significantly contributes to soil color, it was not a good predictor of albedo for the soils used in this study.

The albedo of land surfaces are sometimes predicted using Thematic Mapper satellite data, which records reflected energy in the visible and NIR parts of the spectrum. Interest in using spectral signatures to characterize and map soils was reported in the literature in the mid seventies when remote sensing activities were being extensively evaluated and used. Numerous articles have been written that relate soil reflectance to various physical and chemical soil properties, as summarized in Baumgardner et al. (1985). We selected soils with a wide range of physical and chemical properties; however, many spectral curves were very similar.

Table 3 gives the fraction of reflected energy for the 26 soils under dry and wet conditions. The soils are arranged in the same order as in Table 2, which lists them from the lowest to highest albedos. Through cluster analysis nine spectral signature curves were generated using the 50 data points (0.01 µm) for each of the 52 curves (Fig. 2) . Two clusters included only one soil curve, the Clover Springs wet soil, the soil with the lowest albedo, and the Loring (fine-silty, mixed, active, thermic Oxyaquic Fragiudalf) dry soil, which has a similar albedo as other soils; however, it shows a sharp increase in reflectance in the green, yellow, and red part of the spectrum. The colors of these two soils were 7.1YR 2.1/0.9 and 1.0Y 5.8/5.8 for the Clover Springs and Loring soils, respectively. Two clusters grouped two soil curves, three clusters combined six soil curves, and two clusters combined 14 soil curves. Frequently dry and wet soils clustered together, because their spectral curves were similar. Figure 2 identifies the blue, green, red, and NIR thematic mapper bands, and also the mean Munsell color notation for each cluster is given. Although the soils have a wide range of physical and chemical properties, the Munsell color for each cluster is the soil property that defines each cluster.

View larger version (57K): [in this window] [in a new window]   Fig. 2 Spectral cluster curves from 0.45- to 0.90-µm computed from 0.1-µm multispectral radiometer reflectance data

(2)

View larger version (27K): [in this window] [in a new window]   Fig. 3 Relations between soil albedo and the blue (0.45–0.52 µm), green (0.52–0.60 µm), red (0.63–0.69 µm), near infrared (NIR) (0.76–0.90 µm), brightness (sum of four color bands), and percentage of reflectance. Panel (F) plots the regression lines for blue, green, red, and NIR reflectances

	Conclusions

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
This study shows that soil albedo for the fine earth soil fraction (<2 mm) can be predicted using Munsell soil color value data (Eq. [1]). When using this regression to predict soil albedo, the Munsell color value should be accurately measured and reported to the nearest tenth for best results. Soil moisture strongly reduces the albedo of soils; however, the color value changes too, and the above equation is applicable to dry or wet soil conditions.

Multispectral visible and NIR radiometric reflectances can also be used to predict 0.3- to 2.8-µm soil albedos. When soil albedo is discussed, the wavelength range should always be noted. The NIR regression equation most accurately predicts the broad band (0.3–2.8 µm) albedo and should be used if available. The equations presented here are not applicable if the soil has vegetation on it, because the spectral reflectance curves for vegetation are different than those for soil.Hummel Reck 1979; Zobeck Onstad 1987

	NOTES

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 
¹ Company names are included for benefit of the reader and do not imply endorsement by the University of Arizona.

Received for publication July 20, 1998.

	REFERENCES

TOP ABSTRACT INTRODUCTION Materials and methods NOTES Results and discussion Conclusions REFERENCES

 

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