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ACARS CONTRACT REPORT 1231 87 

FINAL REPORT 

-. 

DEVELOPMENT OF REMOTE SENSING TECHNIQUES CAPABLE 

OF DELINEATING SOILS AS AN AID TO SOIL SURVEY 

ACAR 

Alabama Center for Application of Remote Sensing 

Alabama A&M University 

Huntsville, Alabama 

E October 1,1984 - March 1, 1986 

NAG 13.3 

(NASA-CR-182610) DEVELOPHENT OF REROTE N 8 8- 2 24 5 2 

SENSING TECHNIQUES CAPABLE OF DELINEATING 

’ SOILS AS AN AXD TO SOIL SURVEY Final Report, 

1 Octo 1984 - 1 Rar. 1986 (Alabama A & H Unclas 

I 

Univ.) 28 p CSCL 08H G3/43 0740161 
FINAL REPORT 

Reporting Period: October 1, 1984 - March 1, 1986 

Grant No. NAG-13.3 

Title of Investigation: 

Development of Remote Sensing Techniques Capable of 

Delineating Soil as an Aid to Soil Survey 

Principal Investigators: 

T. L. Coleman 

0. L. Montgomery 

A1 abama A&M Uni versi ty 

School of Agriculture and Home Economics 

Huntsvi 11 e, Alabama 35762 

Submitted To: 

National Aeronautics and Space Administration 

National Space Technology Laboratories 

NSTL, Mississippi 39529 
TABLE OF CONTENTS 

i 

Page 

i 

ii 

1 

2 

3 

10 

20 

21 

22 
LIST OF FIGURES 

Page 

Figure 1. Representative reflectance spectra of six soil orders 

obtained from air-dried soil samples.--------------------- 11 

ii 
LIST OF TABLES 

Page 

Table 2. Results from stepwise discriminant analysis for selecting 

the best portion of the spectrum in differentiating among soils.-------------------------------------------------- 19 

iii 
Abstract 

Eighty-one benchmark soils from Alabama, Georgia, Florida, 

Tennessee, and Mississippi were evaluated to determine the feasibility of spectrally differentiating among soil categories. 

Relationships among spectral properties that occur between soils and 

within soils were examined, using discriminant analysis. Soil 

spectral data were obtained from air-dried samples using an Exotech 

Model 20C field spectroradiometer (0.37 to 2.36 urn). Differentiating among the. orders, suborders, great groups, and subgroups using 

reflectance spectra achieved varying percentages of accuracy. Six 

distinct reflectance curve forms were developed from the air-dried 

samples based on the shape and presence or absence of adsorption 

bands. Iron oxide and organic matter content were the dominant soil 

parameters affecting the spectral characteristics for differentiating among and between these soils. The reflectance response of 

Alfisols, Inceptisols, and Entisols is predominantly influenced by 

iron oxide. These soils have a concave character in the 0.5 - 

0.8 1.1 m region and a higher reflectance throughout the 0.5 - 1.8 1.1 m 

region. The U1 ti sol s and Mol 1 i sol s are predominately affected by 

organic matter content. The reflectance response is lower and they 

have a concave character in the 0.5 - 0.8 1.1 m region. The Spodosols 

show evidence of organic matter influence in the 0.5 - 0.8 1.1 m region 

and the characteristic water adsorption bands are almost 

indistinguishable in these soils. 
2 

INTRODUCTION 

The general objective of this study was to develop time-saving and 

cost-effective remote sensing techniques capable of delineating soils in 

a manner which would serve to expedite the preparation of soil surveys. 

This general objective was to be addressed through four specific 

objectives, which were as follows: 

1. 

2. 

3. 

4. 

Identify soil parameters of importance in soil mapping unit 

delineation which can be correlated with spectral soil properties. 

Quantify the extent to which field conditions (surface roughness , 

vegetation cover, moisture difference, etc.) a1 ter the spectral 

response of surface soi 1s. 

Determine the effect of wave band selection, 

wave band on the correlation of spectral ref 

parameters. 

Provide a body of knowledge and interpretive 

wave bandwidth, and 

ectance with soil 

skills which will 

render remote multispectral sensing a more valuable tool for soil 

survey. 

These objectives were to be accomplished via a three-phase 

approach: Phase 1-Laboratory study: Phase 11-Field Study: and Phase 

111-Correlation Studies with Satellite Imagery. However, as a result of 

budget cuts and a change in the research objectives of the funding 

agency this project was terminated after the first year of funding and 

phases I1 and 111 could not be completed. 

This report is a sunnnary of the work that was completed and 

discussed in the annual report (ACARS CONTRACT REPORT #013186) and in a 
3 

manuscript entitled "Assessment of spectral characteristics for differentiating among soil categories in the southeastern Uni'ted States. The 

manuscript has been submitted to the Soil Science Society of American 

Journal and is currently under review. 

MATERIALS AND METHODS 

Eight-one soil samples, representing the surface horizon. of 

eighty-one benchmark soils from the southeastern United States were 

utilized in this study (Soil Survey Staff, 1972). The soils were 

randomly selected from the pool of benchmark soils of the southeastern 

U. S., which resulted in samples from six soil orders and several 

suborders, great groups, and subgroups (Table 1). The standard sieved 

soil fraction (5 2mm) was used for laboratory determination of the 

spectral properties. Reflectance measurements were made on air-dried 

samples held in large (10-cm diam) sample holders. Spectral 

bidirectional reflectance factors (Nicodemus et al., 1977) were measured 

with an Exotech Model 2OC spectroradiometer (Learner et a1 ., 1973) 

adapted for indoor use with a reflectometer equipped with an artificial 

illumination source, transfer optics, and sample stage. Spectral 

readings were taken in 0.01 increments over the 0.45 to 2.36 m wavelength range. A 1,000 watt tungsten-iodide coiled filament lamp and 

paraboloidal mirror provided highly collimated incident irradiation 

similar to that of solar illumination. Pressed barium sulfate was used 

as a calibration standard to account for fluctuations in intensity of 

the illumination source (Robinson and Biehl , 1979). The spectral data 

were separated into seventeen wavelength intervals or band widths, 

which coincided with the band widths of the Thematic mapper (TM) and 

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The spectral data were organized in a databank and analyzed statistical ly using discriminant analyses (Ray, 1982). The discriminating 

variables (wavelength intervals or band widths) are used directly in the 

analysis or a select number of the variables are used as determined 

through a stepwise method. The soil samples of the Ultisols and 

Alfisols soil order were later divided into databanks containing 

suborders, great groups, and subgroups such that the discriminant 

functions could be applied to each category individually. 

RESULTS AND DISCUSSION 

Six distinct reflectance curve forms were produced from the 

air-dried samples, based on the shape and presence or absence of water 

and iron oxide absorption bands (Fig. 1). The curve forms are 

considered characteristic of the six soil orders used in this study. 

The sharpness or broadness of the water adsorption bands (1.4 pm and 1.9 

u m) is attributed to the presence or absence of hydoscopic moisture 

around the soil aggregates. The Aflisols, Inceptisols, and Entisols are 

primarily affected by the iron oxide content and are similar to the 

iron-affected and minimally a1 tered curve forms of Stoner and 

Baumgardner (1981). The reflectance responses of the soils shows .a 

convex character in the 0.5 - 0.8 pm region. They also have a higher 

response throughout the 0.5 - 1.8 pm region. The Mollisols and Ultisols 

are predominantly affected by organic matter content and are similar to 

the organic-dominated and organic-affected curve forms of Stoner and 

Baumgardner (1981). They have a concave character in the 0.5 - 0.8 IJ m 

region. The Ultisols have considerably lower reflectance than the other 

orders throughout the 0.45 - 2.4 vm region. The Spodosols show evidence 
W 

0 

0.48 ' 0.62 ' 0.72 ' 0.95 ' 1.22 ' ' 

m 

-- \o Spodosols 

Water 

\bsorptio 

Wavelength ( IJ ) 

Figure 1. Representative reflectance spectra of six 

soil orders obtained from air-dried soil 

samples. 
12 

of organic matter influence in the 0.5 - 0.8~ m region and the water 

absorption bands of these soils are almost indistinguishable. These 

findings infer that spectral properties from air-dried soils are 

adequate for separating soils; however, quantitative data, not visual 

data is more useful in a classification scheme. The Mollisols and 

Spodosols were accurately differentiated (100%) based on their spectral 

properties, while the Inceptisols, Entisols, Alfisols, and Ultisols were 

differentiated at levels of 80%, 75%, 68%, and 55%, respectively (Fig. 

2). The low percentage levels achieved with the Alfisols and Ultisols 

were attributed to the varying percentages of organic matter they 

contained. 

The ability of the spectral properties to differentiate Alfisols 

and Ultisols increased when the samples were compared and analyzed 

separately from the other orders (Fig. 3). The Ultisols were differentiated at 98% which is an increase of 45%. The Alfisols increased 

12% from 68% to 80%. The Ultisols and Alfisols were used to assess the 

ability of the spectral properties in differentiating among soil samples 

at lower categories of the taxonomic scheme. These two orders were used 

because they represented the largest group of samples within each 

category that could be used for statistical comparison. 

The classification performance for the suborders is given in 

(Fig.4). The Aqualfs were accurately differentiated (loo%), while the 

Udults, Udalfs, and Aquults were differentiated at levels of 88%, 71%, 

and 67%, respectively. The classification performance of the great' 

groups is given in Figure 5. The Paleaquults and Ochraqualfs were 

correctly differentiated (100%). The Fragiudalfs, Hapludul ts, 

Paleudalfs, Hapludalfs, Paleudults, and Fragiudul ts were differentiated 

at levels of 84%, 69%, 59%, 49%, 44%, and 44%, respectively. 
13 

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Figu6 2. Percentage of samples accurately 

differentiated at the order level. 
I 

8 

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Figure 3. Percentage of Ultisols and 

Alf i so Is accurately differentiated . 

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Figure 4. Percentage of samples 

I 

accurately 

differentiated at the suborder level. 

15 
16 

v) 

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100 

U 

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Figure 5. Percentage of samples accurately differentiated 

at thegreat group level. 
The spectral 

subgroups base on 

in comparison to 

properties were better in 

the number of samples that 

the other categories (Fig. 

P1 inthic Paleudul ts, Typic Fragiudul ts , Typic 

17 

differentiating among the 

were correctly identified 

6). The Grossarenic and 

and Vertic Hapludalfs , and 

Typic Paleudalfs were accurately differentiated (100%). The Rhodic 

Paleudul ts, Typic Paleudalfs, and Typic Hapludults were differentiated 

at levels of 85%, 69% and 59%, respectively. 

Table 2 contains results of a stepwise discriminant analysis 

procedure for selecting the best wavelength bands for differentiating 

among the six soil orders and the suborders, great groups, and subgroup9 

of the Alfisols and Ultisols. Soil orders are best differentiated using 

wavelength intervals 0.60 - 0.63 urn, 0.76 - 0.90 urn and 1.85 - 1.95 vm. 

The suborders of the Alfisols and Ultisols are best differentiated using 

wavelength intervals 0.60 -0.63 pm, 0.63 - 0.69vm, and 0.76 - 0.90 urn. 

The great groups of the Alfisols and Ultisols are best differentiated 

using wavelength intervals 0.60 - 0.63 vm and 0.63 - 0.69 vm. The 

subgroups are best differentiated using wavelength intervals 0.45 - 0.52 

Urn, 0.63 - 0.69 urn, 0.69 - 0.76 vm, and 0.90 - 1.00 vm. 

.The increase in the a-ccuracy, at lower levels of the classification 

scheme, by which the spectral properties were able to differentiate 

among the soil samples is a good indication of the potential use of 

spectral soil properties in identifying soils. This increase in 

accuracy maybe attributed to the number and/or section of the spectrum 

from which the spectral bands or wavelength intervals used in the 

algorithm were selected. Except for the great group category the 

number and/or wavelength interval changed at each level. The bandwidths 

are also narrower in most cases which may result in less interference 

from atmospheric variables, such as C02 and water. 
100 

U 

a> 

80 

.- 

CI 

t 

f 60 

n 

Y- 

’c 

C 

IFigure 6. Percentage of samples accurately differentiated 

at the subgroup level. 

18 
19 

Table 2. Results from stepwise discriminant analysis for 

selecting the best portion of the spectrum in 

differentiating among soils. 

Taxonomic Wave1 ength Wil ks F Prob F 

Category Intervals Lambda 

Entered 

( pm) 

1 

0.60-0.63 

1.85-1.95 

Order 0.76-0.90 0.6798 5.167 0.0264 

0.60-0.63 

0.76-0.90 

Suborder 0.63-0.69 0.5771 2.840 0.0315 

Great Group 0.60-0.63 0.3764 1.475 0.1577 

0.63-0.69 

0.45-0.52 

Subgroup 0.52-0.60 

0.62-0.63 0.0057 2.054 0.0240 

0.63-0.69 

~ ~~ ~ 

'Wilks Lambda is close to 0 if the groups are well separated. 
20 

SUMMARY 

The spectral data and curve-forms presented here were generated 

from air-dried samples, whereas the findings presented by previous 

investigators were generated' using uniformly moist soil samples. The 

most frequently occurring natural condition of the surface soil is 

closer to an air-dry state and not a uniformly moist state. Moisture 

tends to reduce reflectance in all wavelength bands and will result in a 

biassed indicator of true soil reflectance. The characteristic 

adsorption bands that are commonly used in distinguishing spectral 

curve-forms, water and iron oxide, are still recognizable using the 

air-dried samples. 

The accuracy achieved uti7izing soil reflectance spectra in differentiating among soil groups, as tested using discriminant analysis, 

proved that spectral properties of air-dried soils are useful for 

differentiating among soil orders, suborders, great groups, and 

subgroups; however, the accuracy achieved at each taxonomic level was 

variable. The increase in the accuracy at lower levels of the taxonomic 

scheme can be attributed to the increase in the number of bands entered 

into the algorithm and the section of the spectrum they represent. The 

greatest asset of using spectral properties in studing soils lie in the 

ability to acquire reliable information about the soil which may 

speed-up the classification process of an uncharted area. Knowing how 

dry conditions affect the spectral properties are important in supplying 

needed' information to the classifier. This study has attempted to 

address that issue inorder to more fully utilize all of the tools 

available for classifying soils. 
ACKNOWLEDGEMENT 

The authors wish to acknowledge the contribution of Or. L. Beihl in 

obtaining the spectral data used in this study. Acknowledgement is also 

given to the Soil Conservation Service personnel in the. states of 

Florida and Georgia who assisted in sample collection and Dr. George 

Brown for assistance in statistical analysis. 
22 

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Condit, H. R. 1970. The spectral reflectance of American soils. 

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Condit, H. R. 1972. Application of characteristic vector analysis 

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23 

Ray, Alice A. 1982. SAS Users Guide: Statistics. SAS Institute 

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