Effects of soil composition and mineralogy on remote sensing of crop residue cover_Remote Sensing

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Accepted 18 September 2008
Keywords:
ASTER
Remote Sensing of Environment 113 (2009) 224–238
Contents lists available at ScienceDirect
Remote Sensing o
.e
methods, are increasingly used in crop production as they can help
reduce soil erosion, sequester carbon to the soil, decrease the need for
fertilizer, and improve net farm profitability (Paudel et al., 2006;
McGinnis, 2007). Reduced- and no-till agriculture disturbs the soil
significantly less than conventional (intensive) tillage methods, and
often leaves considerable amounts of the previous season's crop
residues (non-photosynthetic vegetation) on the surface.
Although remote sensing methods are routinely used for identify-
ing crops and assessing their condition, attempts to measure crop
due to poor contrasts between crop residues and many soils, and
because these indicesweremore strongly affected by green vegetation
(Gill & Phinn, 2008; Serbin et al., unpublished data). This is due, in
part, to the relatively broad Landsat TM bands which are unable to
discriminate between specific material absorptions that occur in the
1000 to 2500 nm wavelength region. Hyperspectral and advanced
multispectral sensors (e.g., Advanced Spaceborne Thermal Emission
and Reflection Radiometer, ASTER) have relatively narrow spectral
bands and can potentially discriminate between crop residues and
residue cover using remotely sensed data ha
agricultural areas where the multispectral co
residue reflectances were large, Landsat Them
indices have successfully identified broad cr
⁎ Corresponding author.
E-mail address: guy.serbin@gmail.com (G. Serbin).
0034-4257/$ – see front matter. Published by Elsevier I
doi:10.1016/j.rse.2008.09.004
ude reduced- and no-till
(McNairn & Protz, 1993; Biard & Baret, 1997; van Deventer et al., 1997;
Qi et al., 2002). However, these indices were less effective when used
in agricultural regions with different soil types (Daughtry et al., 2005),
Conservation tillage practices, which incl
1. Introduction
Imaging spectroscopy
Cellulose Absorption Index (CAI)
Lignin-Cellulose Absorption (LCA) index
Chemical absorption features
Spectral libraries
Shortwave infrared
Soil
Mineral
Non-photosynthetic vegetation
Crop residue
Tillage
related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop
residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400–
1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper
shortwave infrared (1900–2500 nm) region. Two reflectance band height indices used for estimating residue
cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of
which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect
soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives
were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm
wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing
estimates of crop residue cover. Most common soil minerals had CAI values≤0.5, whereas crop residues were
always N0.5, allowing for good contrast between soils and residues. However, a number of common soil
minerals had LCA valuesN0.5, and, in some cases, the mineral LCA values were greater than those of the crop
residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry
residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation
Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such
as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often
do not identify individual mineral species required for precise CAI estimation. However, these methods are
still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and
hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.
Published by Elsevier Inc.
Received 28 December 2007
Received in revised form 18 August 2008
erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and
Article history: The management of crop
 residues (non-photosynthetic vegetation) in agricultural fields influences soil
Effects of soil composition and mineralog
Guy Serbin a,⁎, Craig S.T. Daughtry a, E. Raymond Hun
a USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
b USDA-ARS Environmental Management and Byproduct Utilization Laboratory, Beltsville,
c Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
a b s t r a c ta r t i c l e i n f o
j ourna l homepage: www
ve had mixed success. In
ntrasts between soil and
aticMapper (TM)-based
op residue cover classes
nc.
on remote sensing of crop residue cover
Jr a, James B. Reeves III b, David J. Brown c
USA
f Environment
l sev ie r.com/ locate / rse
soils. For example, both the hyperspectral Cellulose Absorption Index
(CAI) and the multispectral Lignin-Cellulose Absorption (LCA) index
are reflectance band height indices that use three spectral bands
between 2000 and 2400 nm to estimate crop residue cover (Daughtry
et al., 2005). Reflectance band height methods are similar in approach
to the continuum-removal method (Clark et al., 2003a). The primary
factors affecting these indices include crop residue type, residue age,
degree of soil coating on the residue, and water content of the soil and
225G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
residue (Nagler et al., 2000; Daughtry et al., 2004). Very little is known
about the effects of soil mineralogy and soil organic matter content on
these spectral indices for assessing crop residue cover.
The mineralogy of the parent material and the chemical changes
made during soil formation are major reasons for spectral differences
among soil types (Stoner & Baumgardner, 1981; Baumgardner et al.,
1985; Clark, 1999; Ben-Dor, 2002; Brown et al., 2006). Texture also
affects soil reflectance (Baumgardner et al., 1985; Clark,1999; Ben-Dor,
2002; Brown et al., 2006; Daughtry & Hunt, 2008), but textural
differences will not cause spectral features that would interfere with
the crop residue indices CAI and LCA. Our objectives are to (1) identify
minerals and other soil components with absorption features in the
2000 to 2400 nm wavelength region that affect CAI and LCA and
(2) assess their potential impact on remotely sensed estimates of crop
residue cover.
2. Theoretical considerations
2.1. Minerals and organic compounds in soils
Most soils are mixtures of minerals and organic compounds, albeit
that certain soils, such as quartz sands, can be monomineralic, or
comprised solely of organic matter, such as peat soils. According to
Klein and Hurlbut (1993, p. 1), “a mineral is a naturally occurring
homogeneous solid with a definite (but generally fixed) chemical
composition and a highly ordered atomic arrangement. It is usually
formed by inorganic processes.” Any other solid material in the soil
which does not fit that description can be classified as either a
mineraloid (e.g., volcanic glass and limonite, which are amorphous
and do not have an ordered atomic arrangement) or an organic
compound (e.g., cellulose, lignin, and humus). Mineral classes are
based upon themajor anions: SiO44− tetrahedral polymers for silicates,
OH− for hydroxides, O2− for oxides, S2− for sulfides, CO32− for car-
bonates, PO43− for phosphates, and SO42− for sulfates.
2.1.1. Silicate minerals
Themajority ofminerals in the Earth's crust are silicateminerals,of
which SiO44− tetrahedra comprise the major anion (Deer et al., 1992;
Klein & Hurlbut, 1993). Silicate minerals are separated into six classes
according to the degree of polymerization along basal oxygen ions for
SiO44− tetrahedra. As the degree of polymerization increases, the ratio
of Si:O decreases, as discussed by Klein and Hurlbut (1993, pp. 440–
444). The first class, nesosilicates (orthosilicates), consists of single
tetrahedra and includes important minerals such as the olivine group,
the garnet group, titanite (sphene), and zircon. The second class,
sorosilicates (disilicates) involves two silicate tetrahedra polymerized
along a single oxygen ion to form a Si2O76− anion, and includes mi-
nerals from the epidote group. The third class, cyclosilicates (ring
silicates), is comprised of rings of six tetrahedral sharing two oxygens
each to form a Si6O1812− anion and includes minerals from the
tourmaline group and beryl. The fourth class, inosilicates (chain
silicates), are assembled from long chains of tetrahedra. This class has
two main groups, pyroxenes and amphiboles. Pyroxenes are single
symmetrical chains of tetrahedra sharing two oxygens each and
forming repeating Si2O64− anion unit cells, whereas amphiboles are
symmetrical double chains of repeating Si4O116− unit cells and the
tetrahedral sharing two and a half oxygens on average (two oxygens
for exterior tetrahedra, three for interior). Common pyroxenes include
augite, enstatite, and hypersthene; common amphiboles include
actinolite, glaucophane, and hornblende. Both pyroxenes and amphi-
boles are further divided according to crystallography. Orthorhombic
species form orthopyroxenes and orthoamphiboles; monoclinic
species form clinopyroxenes and clinoamphiboles. In the fourth
class, there is an additional group of asymmetrical single chains
referred to as pyroxenoids, and include such minerals as wollastonite
and rhodonite. The fifth class, phyllosilicates (sheet silicates), is
composed of sheets of tetrahedral sharing three basal oxygen ions and
includes a number of groups including clays (illite, kaolinite, mont-
morillonite), serpentines, chlorites (a group of mostly green colored
phyllosilicate minerals, which incidentally do not contain chlorine),
micas, talc, pyrophyllite, and vermiculites. The sixth class is the
tectosilicates (framework silicates), whereby all of oxygens in the
tetrahedron are shared with others, and includes the quartz, feldspar,
feldspathoids (nepheline, sodalite), and zeolite groups.
2.1.2. Nonsilicate minerals and inorganic compounds
Hydroxide compounds include minerals like gibbsite, brucite, and
goethite, and oxides includeminerals such as corundum and hematite.
Many oxide and hydroxide minerals result from weathering, albeit
that some are primary minerals formed in igneous environments.
Sulfide minerals, such as pyrite, can be found in igneous and
sedimentary environments. Carbonate group minerals, such as calcite
and dolomite, are the main components of limestone and dolomite
rocks, respectively, and comprise the main bedrock components for a
large proportion of the Earth's surface. Phosphate minerals, such as
those of the apatite group, are important accessory minerals in many
igneous rocks, and are required by many living organisms (vertebrates
in particular) for skeletal materials and teeth. Sulfate minerals, like
gypsum, anhydrite, alunite, and jarosite, result from the oxidation of
sulfide minerals and are indicative of evaporitic sedimentary or
hydrothermal environments. Additional nonsilicate mineral groups
not discussed include nitrate (NO3−) minerals like niter (saltpeter) and
borate (BO33−) minerals like howlite; these are formed in arid
terrestrial evaporite environments.
2.2. Mineral and organic compound absorptions
In the shortwave infrared (SWIR) bands of interest for this study
(2000–2400 nm), there are a number of vibrational and overtone
absorption features which are related to the specific molecular bonds
in compounds, e.g., the O–H bending and C–O stretching combination
at 2101 nm and the C–H stretching plus CH2 deformation combination
at around 2335 nm seen in cellulose, or the combination metal—OH
bends and -OH stretches that are seen in many OH-bearing minerals,
e.g., the Al–OH bands at 2180 and 2210 nm in kaolinite (Hunt &
Salisbury, 1970; Clark, 1999; Workman & Weyer, 2008). Reflectance
band height indices, which include CAI and LCA (Daughtry et al.,
2005), can be affected by these material absorptions, which can
change the “shape” and/ or depth of the spectral absorption features.
For example, a change in the spectral shapes of absorption features
from convex to concave can dramatically alter the resulting index
values.
The indices used in this study are reflectance band heightmethods,
which are based on a continuum-removal method (Clark et al., 2003a).
In continuum-removal a spectral continuum is removed between two
locations to expose underlying absorption features and values are
normalized, such as those of minerals between 2200 and 2400 nm and
chlorophyll at 680 nm (Clark, 1999; Clark et al., 2003a; Kokaly et al.,
2003). Reflectance band height methods vary from continuum-
removal in that spectral reflectance values are not normalized in
calculations, but can be scaled or averaged to better show a central
spectral emission or absorption feature relative to two other spectral
bands.
2.3. Visible, NIR, and SWIR spectrum and optical sensor bands
A number of existing air- and space-borne multispectral and
hyperspectral sensors are capable of detecting SWIR absorption features
related to crop residue cover. Empirical crop residue spectral indices that
use Landsat TM band 7 (2080–2350 nm) rely on multispectral
reflectance differences to distinguish crop residues from soil. Unfortu-
nately, these reflectance differences change from scene to scene as crop
residues decompose and soils are tilled. For example, crop residuesmay
bebrighter than the soil in allwavelengths shortlyafterharvest, but later
as the residues weather and decompose they may be darker than the
soil. The characteristic absorption features of cellulose, lignin, and a
number of common minerals occur in the 2080–2350 nm wavelength
interval (Table 1), but are not resolved by the spectrally broad Landsat
TMband7. Advancedmultispectral andhyperspectral sensorshavebeen
developed with spectral resolutions sufficient to resolve these diag-
nostic absorption features fromairborne and space-borneplatforms. For
example, AVIRIS (NASA Jet Propulsion Laboratory, 2007), HYMAP
(Integrated Spectronics, Castle Hill, NSW, Australia), MODIS Airborne
2101 nm, but rather relies on shoulder of that absorption feature in
ASTER band 5 and the C–H stretching plus CH2 deformation
combination cellulose at 2335 nm (Workman & Weyer, 2008 and
references therein) in ASTER band 8. Because it is based upon a
reflectance peak height, convex spectral shapes are positive and
concave shapes negative, as ASTER5 is on the slope and ASTER6 on the
side peak of lignin and cellulose absorptions, and ASTER8 is at the
center of a cellulose absorption (Fig. 1).
Soils and residues spectrally mix in a linear fashion (Daughtry &
Hunt, 2008) for both CAI and LCA. Thus, the fractional crop residue
cover of a pixel can be calculated once endmember CAI or LCA values
are determined for soil and crop residue.
3. Materials and methods
3.1. Spectral libraries used in this study
The majority of spectra analyzed in this paper were acquired from
spectral libraries. The libraries include:
• The Elvidge (1990) library of live and dry vegetation and organic mate-
rials (including simple and complex sugars, cellulose, lignin, proteins,
waxes, and humic acid), which was acquired fromwithin the ENVI 4.3
software package (ITT Visual Information Solutions, Boulder, CO).
• The Norris (2006) USDA-ARS Instrumentation Laboratory (Beltsville,MD) library of organic materials (various sugars, cellulose, and
lignin), which is hosted by Thermo-Galactic Inc. (http://www.
spectraonline.com/).
• The USGS Spectroscopy Laboratory's Splib05 of minerals and
compounds (Clark et al., 2003b), which was been superseded by
and included in Splib06 (Clark et al., 2007, http://speclab.cr.usgs.
226 G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
Simulator (MAS) (King et al., 1996), and EO-1 Hyperion (U. S. Geological
Survey, 2007) have the capability of detecting specific absorptions
related to both organic and mineral compounds (Table 1) in a greater
spectral range than that served by Landsat TM band 7. Additional
advanced multispectral sensors, such as the MODIS/ASTER Airborne
Simulator (MASTER, Hook et al., 2001) and the ASTER sensor are capable
of imaging specific absorptions related to soil organic and mineral
compounds, including parts of the cellulose absorption at 2101 nm.
2.4. Spectral indices
The reflectance band height indices used for this research were the
Cellulose Absorption Index (CAI, Daughtry, 2001; Daughtry et al.,
2005) and the Lignin-Cellulose Absorption Index (LCA, Daughtry et al.,
2005). Both indices used in this paper were multiplied by a factor of
100 to convert from reflectance units to percent reflectance, which we
found to be visually more appealing and useful. CAI is determined via
the following equation:
CAI ¼ 100 � R2:0 þ R2:2
2
−R2:1
� �
ð1Þ
where R denotes the reflectance and the subscripts 2031, 2101, and
2211 denote 11 nm wide hyperspectral bands centered at 2031 nm,
2101 nm, and 2211 nm, respectively (Table 1). CAI essentially
measures the depth or intensity of the 2101 nm absorption attributed
to an O–H stretching and C–O bending combination (Workman &
Weyer, 2008 and references therein) as shown in Fig. 1, and thus, the
deeper this absorption occurs relative to R2031 and R2211, i.e., the more
concave the shape of the spectral feature characterized by these
bands, and the more positive CAI will be. Conversely, when
absorptions occur in either R2031 or R2211 but not on R2101, the spectral
shape becomes convex and the resulting CAI becomes negative. A
linear change from R2031 to R2211 will yield a CAI equal to zero. R2031 is
determined at 2031 nmwavelength to avoid a narrow CO2 absorption
feature centered at 2010 nm (Sabins, 1987; Green, 2001).
LCA is determined via the following equation:
LCA ¼ 100d 2dASTER6− ASTER5þ ASTER8ð Þ½ � ð2Þ
where ASTER5, ASTER6, and ASTER8 correspond to ASTER bands 5, 6,
and 8 respectively (Table 1). This index varies from CAI in that (1) it
measures the height of a reflectance peak between two absorption
features, not the depth of an absorption feature, (2) the center band is
Table 1
Cellulose Absorption Index (CAI) and Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) Lignin Cellulose Absorption (LCA) Index spectral
reflectance bands used in this study
Band Wavelengths (nm) Index Common absorbing compounds
R2031 2026–2036 CAI
R2101 2096–2106 CAI Cellulose, NH4-bearing minerals, topaz
R2211 2206–2216 CAI Clay minerals, muscovite
ASTER5 2145–2185 LCA Cellulosea
ASTER6 2185–2225 LCA Clay minerals, muscovite
ASTER8 2295–2365 LCA Lignin, cellulose
a
 Denotes trailing edge of absorption feature.
multiplied by two, rather than the side bands being averaged, and (3)
that it does not directly capture the cellulose absorption feature at
Fig. 1. Carbon compounds in soils and spectral bands used in this study. CAI and LCA
values are presented in Table 2. R2031, R2101, and R2211 denote 11 nm wide reflectance
bands centered at 2031, 2101, and 2211 nm respectively. A5, A6, and A8 denote ASTER
bands 5, 6, and 8, respectively, vertical dash-dot-dot line denotes the boundary between
ASTER bands 5 and 6. ⁎Data from Elvidge (1990).
gov/spectral.lib06/) during preparation of this manuscript.
227G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
3.2. Spectral data acquisition in-house
We also acquired additional reflectance spectra of crop residues,
soils, and various other samples with a spectroradiometer (FieldSpec
Pro, Analytical Spectral Devices, Boulder, CO) covering the 350 to
2500 nm wavelength region at 1 nm intervals. The samples were
illuminated by six 100 W quartz-halogen lamps mounted on the arms
of a camera copy stand at 45 cm over the sample at a 45° illumination
zenith angle. A current regulated DC power supply stabilized the
output of the lamps. A digital camera and the fore-optic of the
spectroradiometer were aligned and positioned 90 cm from the
sample surface at a 0° view zenith angle. The 1° fore-optic was used
for the soils and powders which resulted in a 1.6 cm diameter field of
view and the 18° fore-optic was used for crop residues with a 28.5 cm
diameter field of view. The illumination and view angles were chosen
to minimize shadowing and to emphasize the fundamental spectral
properties of the samples. Four spectra of 20 scans each were acquired
from each sample by rotating the sample tray 90° after each spectrum.
All sampleswere placed in trays that were spray-painted flat black. For
the 1° fore-optic, a 30-cm square Spectralon reference panel (Lab-
sphere, Inc., North Sutton, NH) was placed in the field of view,
illuminated, andmeasured in the samemanner as the samples. For the
18° fore-optic, we used a 61-cm square reference panel. Reflectance
factors were calculated and corrected for the reflectance of the
Spectralon reference panel. Spectra of green corn canopy were
acquired in corn fields on 7 July 2006 at silking stage (R1, Ritchie
et al., 2005) using the ASD spectrophotometer and the 18° fore-optic.
Additional spectra were acquired utilizing FOSS NIRSystems
(Laurel, MD) 6500 scanning monochromator including: pure cellulose
(Reeves, 1996), wet dairy manures (Reeves & Van Kessel, 2000), dry
dairy manures (Reeves & Van Kessel, 2002), and dry poultry manures
(Reeves et al., 2002). Wet dairy (Reeves & Van Kessel, 2000) and dry
poultry (Reeves et al., 2002) manures were scanned in polyethylene
bags, whereas cellulose (Reeves, 1996) and dry dairy manures (Reeves
& Van Kessel, 2002) were scanned in sample cups.
3.3. Data processing
Clark et al. (2003b) rated individual spectra in Splib05 based upon
sample purity from “A” being the highest to “D” being the lowest. In
general, we selected spectra of the best quality samples in the Splib05
library, though in a few cases samples of mixed spectral purity were
used for important minerals because these were the only data
available. No spectral purity assessments were available for the
other datasets. Furthermore, only data acquired by Clark et al. (2003b)
using ASD spectrophotometers and Beckman 5270 spectrometers
were used. Spectra acquired by these workers using the Nicolet
Fourier Transform Infra-Red (FTIR) Interferometer Spectrometer were
of questionable value in the SWIR region and were excluded (Clark
et al., 2003b). We also only selected spectra of minerals that were
listed in the USDA-NRCS National Soil Survey Center-Soil Survey
Laboratory (NRCS-NSSC-SSL) data base (Soil Survey Staff, 2007a).
Reflectance spectra were organized according to material class,
group, and mineral. Band reflectance values were calculated by
averaging reflectance values at wavelengths within the band intervals
(Table 1), and spectral indices were calculated for each spectrum.
These data are available at the Hydrology and Remote Sensing
Laboratory (HRSL) FTP site ftp://hydrolab.arsusda.gov/pub/residue/.
3.4. Soil mineralogical analyses
The NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a) contained
336,820 mineralogy records for 34,357 soil samples acquired from
9869 pedons. Any mineralogical results reported as glass, glass-
covered minerals, mineral mixes (e.g., biotite–chlorite, clay covered
quartz, montmorillonite-mica), ambiguousmineral descriptions, and
minerals not found in Clark et al. (2003b) were excluded. For mineral
occurrences in soils, we included the number of counts found in the
NRCS-NSSC-SSL data base (Tables 3–5). The NRCS-NSSC-SSL used a
number of methods described in detail by Burt (2004) to determine
mineralogy, and include X-ray diffractometry (XRD), thermal analyses,
indirect methods, i.e., linear extensibility, elemental analysis, and
cation exchange capacity (CEC)/clay ratios, as well as optical
petrographic methods. In general, XRD methods can provide better
qualitative information on mineral species than optical petrography,
particularly for clays, which are too small to effectively be viewed
through an optical microscope, or mineral groups, e.g., chlorites,
pyroxenes, or amphiboles, which can be problematic to differentiate
into individual species if not enough mineral grains are present for a
proper analysis. The NRCS-NSSC-SSL frequently used multiple meth-
ods to determinemineralogy on the same sample, and often tested the
same location multiple times in different particle size fractions. We
would like to note that the NRCS-NSSC-SSL did not test every soil
sample in their data base for mineralogy, and even if tests were
conducted, only certain size fractions were tested using one or more
methods. The NRCS-NSSC-SSL (Burt, 2004) also tested soils for highly
soluble salts (saturated paste extractable ions), and separate tests for
gravimetric estimates of undifferentiated carbonate minerals via the
pressure calcimeter method and gypsum via the multiple dissolution
method (Loeppert & Suarez, 1996; Burt, 2004). It is common for soil
samples tested using X-ray diffractometry to be pre-treated to remove
soil carbonates, gypsum, and iron oxides prior to analysis, hence
separate tests are often conducted for these (Whittig & Allardice,1986;
Burt, 2004). We differentiated commonality and rarity of minerals
based upon a number of factors, including weatherability, and their
prominences in surficial rocks (Burt, 2004; Marshak, 2005), the NRCS-
NSSC-SSL data base (Soil Survey Staff, 2007a), and Allen and Hajek
(1989) and other chapters in Dixon and Weed (1989).
4. Results
4.1. Index results
Results for CAI and LCA are presented separately. Anymaterial with
an index (CAI or LCA) value of b−0.5 is considered to be index-
negative, those between −0.5≤ index value≤0.5 are index-neutral, and
any material with a value of N0.5 is considered to be index-positive. In
an ideal situation, one endmember, e.g., soil, would be index-negative,
whereas, the other endmember, e.g., crop residue, would be index-
positive, which would have good contrast and allow for a more
accurate estimation of crop residue cover.
4.2. Cellulose Absorption Index (CAI)
The shortwave infrared (SWIR) region located between 2020 and
2220 nm contains absorption features related to crop residues that are
distinctive from common soil minerals, unlike the visible (400–
700 nm), near infrared (NIR, 700–1200 nm) and SWIR regions
between 1200 and 1900 nm, as shown in Fig. 2. Furthermore, the
spectral bands between 2020 and 2220 nm are minimally affected by
atmospheric gas and water vapor absorptions, with the exception a
narrow CO2 absorption band centered around 2060 nm (Sabins, 1987;
Green, 2001). In Fig. 2, it is clear that the shape of the Codorus series
soil (a fine-loamy, mixed, active, mesic fluvaquentic dystrudepts found
in the Mid-Atlantic Piedmont region of the United States, (Soil Survey
Staff, 2007b) is very similar to that of crop residue in the visible, NIR,
and SWIR bands below 1900 nm (lower-SWIR, SWIR bands above
1900 nm are referred to as upper-SWIR). At longer wavelengths the
spectral shapes are quite different, due to the O–H stretching and C–O
bending combination absorption occurring at 2101 nm (Workman &
Weyer, 2008) in dry residues. Thus, the visible, NIR, and lower-SWIR
regions are clearly not useful for discriminating between Codorus soil
Crop residues (Fig. 3) are essentially cellulose-lignin mixtures and
display the characteristic cellulose absorption feature at 2101 nm and
the less prominent lignin feature at 2270 nm. Since cellulose is more
easily metabolized by microorganisms than lignin, the relative
abundance of cellulose in crop residues in the field decreases with
age, which results in a net decrease in CAI for the wheat residues with
age, as can be seen for fall and spring corn and soybean residues in
Table 1. Similar declines in CAI have been observed as plant residues
decompose (Nagler et al., 2000; Daughtry et al., 2004 and references
therein). In green vegetation, the broad, very strong water absorption
centered at 1940 nm (Curcio & Petty, 1951; Workman & Weyer, 2008)
completely obscures the cellulose and lignin absorption features
(Elvidge, 1990; Gao & Goetz, 1994).
The CAI values of manures are generally N0 but varied from slightly
CAI-negative to as high as 6.1 (Table 2), which is comparable to some
clean, fresh corn residues. These manures contained cellulosic
materials which undoubtedly contributed to the variations in CAI.
Furthermore, Smeaton (2003) reported that in dairy cattle typically
digest up to 70–90% of cellulose and 60–90% of hemicellulose in their
diets, the remainder of which would end up in manure. No
information was available on the cellulose or bedding material
content of these manures. Wet cow manure is CAI-neutral, which is
to be expected of any wet material containing cellulose, including live
vegetation and crop residue (Nagler et al., 2000; Daughtry & Hunt,
2008).
4.2.2. Minerals and mineral groups
The majority of common soil minerals are either CAI-neutral or
CAI-negative, which shows that they would contrast well with crop
residues should they have a significant effect on soil spectral properties.
Table 2
Minimum and maximum Cellulose Absorption Index (CAI), ASTER Lignin-Cellulose
228 G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
and crop residue, though Codorus soil and crop residue are spectrally
distinct in the upper-SWIR.
Fig. 2. Top: Spectra of dry Codorus soil, dry corn residue, and a live corn canopy. Bottom:
Landsat Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER), and Cellulose Absorption Index (CAI) bands.
4.2.1. Organic compounds
Reflectance spectra of selected organic materials are shown in
Fig. 1. Cellulose, with a strong O–H stretching and C–O bending
combination absorption feature at 2101 nm, produces the highest CAI
values (10.8–18.0) for all organic materials shown in Table 2. Simple
and complex sugars (e.g., glucose, sucrose, pectins, and starch), which
also have prominent O–H stretching and C–O bending combination
absorption features and high CAI values, range from 2.7 to 20.1 (full
details available on the HRSL FTP site), and are degraded so rapidly by
microbes that they are not commonly found in crop residues or soils.
Lignin compounds, which have fewer C–OH bonds than cellulose
(Pacsu, 1947; Sarkanen & Ludwig, 1971), display weak absorptions at
2101 nm and therefore yield slightly positive CAI values (Table 2). The
primary absorption features of lignins occur near 2270 nm (Workman
& Weyer, 2008). Peat moss, which is partially decomposed plant
tissues, has a clear O–H stretching and C–O bending combination
absorption feature (Fig. 1) and yields a CAI value of 2.6 (Table 2). The
humus sample shown (Fig. 1) was derived from fully degraded plant
residues in leonardite, shows no clear O–H stretching and C–O
bending combination or other absorption beyond that of water
centered at 1923 nm (Curcio & Petty, 1951; Hunt & Salisbury, 1970;
Workman &Weyer, 2008), and produces a CAI-neutral value (Table 2).
Soil humus content varies significantly over short distances in
agricultural fields (Ritchie et al., 2007) and soil reflectance decreases
as humus content increases (McCartyet al., 2002; Brown et al., 2006;
Serbin et al., 2008). Thus, changes in soil humus content were shown
to bias CAI (Serbin et al., 2008). These workers showed that for soils
from Indiana, low humus content soils were CAI-negative, but as
organic carbon contents increased CAI values tended toward zero.
Both the activated charcoal and pure black carbon spectra (not shown)
produce very low reflectance (b0.04) across the entire 400–2400 nm
wavelength region and are CAI-neutral (Table 2).
Absorption (LCA) Index, and absorption band reflectance values for selected organic
compounds and crop residues
Compound Library CAI R2101 LCA ASTER6 N
Activated charcoal New dataa 0.0 0.04 −0.1 0.04 1
Carbon Splib05b 0.1 0.01 −0.1 0.01 1
Cellulose New data, Elvidgec,
Reevesd
10.8–
18.0
0.38–0.69 14.9–20.9 0.42–0.84 11
Lignin Elvidge, Norrise 1.1–1.5 0.36–0.73 5.9–7.6 0.47–0.73 3
Peat moss New data 2.6 0.35 5.8 0.37 1
Chicken manure,
dry
Reevesf 0.7–6.1 0.27–0.45 5.0–11.5 0.27–0.48 124
Cow manure, dry Reeves and Van
Kesselg
−0.2–4.3 0.21–0.47 4.6–11.7 0.21–0.48 106
Cow manure, wet Reeves and Van
Kesselh
0.0–0.4 0.00–0.04 −44.7–2.8 0.00–0.07 105
Humus WP80 New data 0.0 0.18 1.5 0.19 1
Corn residue, fall New data 4.3–7.1 0.26–0.40 7.2–11.5 0.29–0.46 8
Corn residue,
spring
New data 1.2–6.3 0.27–0.39 3.3–9.9 0.30–0.42 39
Cotton residue New data 1.5 0.34 6.9 0.36 1
Soybean residue,
falli
New data 4.3–6.6 0.25–0.40 8.0–11.4 0.29–0.45 5
Soybean residue,
springi
New data 1.4–4.6 0.27–0.39 3.4–9.2 0.29–0.43 26
Wheat residue New data 1.1–6.0 0.27–0.34 4.7–10.0 0.31–0.37 8
Green corn canopy New data −0.4–0.3 0.03–0.23 2.1–4.7 0.05–0.24 40
R2101 is themean reflectance for the 2096–2106 nm hyperspectral band ASTER6 denotes
reflectance values for ASTER band 6. N denotes number of spectra.
a New data hosted on ftp://hydrolab.arsusda.gov/pub/residue/.
b Splib05, (Clark et al., 2003b), http://speclab.cr.usgs.gov/.
c Elvidge (1990).
d Reeves (1996).
e Norris, K (2006). http://www.spectraonline.com/.
f Reeves (2001).
g Reeves and Van Kessel (2002).
h Reeves and Van Kessel (2000).
i
 Soybean residues may contain some previous year corn residue.
Serbin et al. (unpublished data) did not find any CAI-positive soils in their
analysis of the reflectance spectra of Brown et al. (2006), showing that
soils should contrast well with crop residues. Nevertheless, soils vary
from strongly CAI-negative to CAI-neutral, a factor which must be
accounted for when analyzing hyperspectral imagery. Soil mineralogical
reports (Soil Survey Staff, 2007a) often identify certain mineral groups
rather than individual minerals belonging to those groups. Certain
mineral groups, e.g., garnet or chlorite, have large variations in CAI
between individual mineral species, rendering group-only identification
problematic for this purpose.
4.2.2.1. Nesosilicates, sorosilicates, and cyclosilicates. Nesosilicate
minerals (Table 3) range from CAI-neutral down to strongly CAI-
negative.Minerals of the olivine group are slightly CAI-negative to CAI-
neutral. Whereas olivines are major constituents of mantle rocks and
basalts, they are a highly weatherable group of minerals (Deer et al.,
1992; Klein & Hurlbut, 1993; Marshak, 2005). As such, olivines were
rarely observed in the NRCS-NSSC-SSL data base (Soil Survey Staff,
2007a) and in soils in general (Allen & Hajek, 1989; Huang, 1989; Burt,
2004). Minerals of the garnet group were relatively common (2901
samples in the NRCS-NSSC-SSL database (Soil Survey Staff, 2007a).
Allen and Hajek (1989) showed that garnets are more stable than
quartz. Garnet minerals tend to be CAI-neutral (uvarovite, Fig. 4) to
CAI-negative (CAI=−2.3 for grossular, Fig. 4, and −2.4 for andradite).
Hydrogrossular garnet produce strongly CAI-negative values due to
the presence of -OH substituting SiO4 tetrahedra in the molecular
229G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
The reader should be aware, however, that many common soil minerals
often only occur in trace amounts. In such a case soil spectral properties,
and thus CAI, would be minimally affected, even if the mineral itself is
strongly CAI-negative or CAI-positive. A large number of important soil
minerals are strongly CAI-negative (e.g., certain clay minerals), or have
low reflectance values (e.g., magnetite), both of which can bias soil CAI.
Fig. 3. Crop residue and vegetation spectra. CAI and LCA values are presented in Table 2.
The reflectance axis scale is reduced to accentuate differences between spectra.
While a few minerals are CAI-positive (e.g., topaz, tremolite amphibole,
talc), they are usually rare in soils, with the exception of tremolite
amphibole (Allen & Hajek, 1989; Soil Survey Staff, 2007a). However,
Table 3
Minimum andmaximum CAI, LCA, and absorption band reflectance values for selected neso-
in the NRCS-NSSC-SSL database (Soil Survey Staff, 2007a)
Mineral class Compound Group CAI R2101
Nesosilicate Olivine Olivine −0.9–0.0 0.53–0
Andalusitea – −1.7 0.75
Sillimanitea – −4.5 0.81
Titanite (Sphene) – 0.1 0.37
Garnet Garnet −9.7–−0.1 0.24–0
Staurolite – −0.3 0.07
Topaz – 1.0–4.1 0.72–0
Zircon – 0.1 0.76
Sorosilicate Clinozoisite Epidote −1.5 0.77
Epidote −4.5–−4.1 0.43–0
Zoisite −2.2 0.73
Cyclosilicate Tourmaline Tourmaline −13.4–−7.6 0.53–0
Beryl – 0.1 0.79
Inosilicate Augite Pyroxeneb −0.5–−0.1 0.37–0
Diopside −1.0 0.62
Enstatite −0.2 0.55
Hypersthene −0.2–2.2 0.08–0
Actinolite Amphiboleb −0.6 0.69
Amphibole1 0.6 0.74
Anthophyllite −0.9 0.54
Glaucophane −3.6 0.48
Hornblende 0.1 0.41
Tremolite 1.2–1.9 0.52–0
a The andalusite and sillimanite samples were chemically altered (spectra rated C), which
b Undifferentiated pyroxene and amphibole spectra and frequency counts are for unident
soil samples from the NRCS-NSSC-SSL database (Soil Survey Staff, 2007a).
structure (Klein & Hurlbut, 1993), which results in absorption affecting
R2211 (Fig. 4). It should be noted that the NRCS-NSSC-SSL database (Soil
Survey Staff, 2007a) did not differentiate between garnet minerals. As
such, any soil containing garnet in greater than trace quantities may
need further assessment to determine if that particular mineral would
cause a soil to be more CAI-negative. Staurolite was rare in the NRCS-
NSSC-SSL database (Soil Survey Staff, 2007a) and is CAI-neutral, but
very dark, with a reflectance value of 0.07. Topaz is rare in soils, i.e.,
found in only 10 samples in the NRCS-NSSC-SSL database (Soil Survey
Staff, 2007a). Topaz is CAI-positive, due to an absorption at 2085 nm,
the trailing edge of which affected R2101 (Fig. 5). Zircon (Fig. 5), a
common (Soil Survey Staff, 2007a) and highly resistant soil mineral
(Allen & Hajek, 1989), is CAI-neutral.
, soro-, cyclo-, and inosilicates from Clark et al. (2003b) and occurrence of these minerals
LCA ASTER6 Spectra NRCS-NSSC-SSL samples
.81 −1.5–0.5 0.53–0.81 16 32
−2.7 0.73 1 8
−2.3 0.68 1 9
0.2 0.37 1 127
.80 −4.7–1.7 0.22–0.75 11 2901
0.1 0.07 1 8
.93 1.5–5.5 0.73–0.94 16 10
−1.1 0.76 1 5013
21.1 0.74 1 17
.67 15.0–34.9 0.30–0.68 3 1551
19.0 0.71 1 8
.77 −6.3–−3.9 0.23–0.60 3 4508
5.5 0.79 1 247
.41 −1.6–0.7 0.37–0.38 2 36
0.5 0.59 1 15
2.9 0.57 1 16
.57 −3.5–0.7 0.12–0.60 9 74
10.4 0.64 1 11
12.7 0.74 1 2487
9.6 0.48 1 4
15.0 0.42 1 9
8.5 0.42 1 5143
.81 9.8–17.2 0.53–0.81 2 2
induced absorptions at around 2200 nm (Clark et al., 2003b), biasing CAI negatively.
ified minerals belonging to that group. Undifferentiated pyroxenes were found in 5458
2007a) as they are highly stable minerals (Allen & Hajek, 1989), and
Fig. 6. Selected spectra of cyclosilicate minerals elbaite (tourmaline group) and beryl
from Clark et al. (2003b). CAI and LCA values are presented in Table 3.Fig. 4. Garnet group (nesosilicate) spectra from Clark et al. (2003b). CAI and LCA values
are presented in Table3.
230 G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
All sorosilicates found in soils by the NRCS-NSSC-SSL (Soil Survey
Staff, 2007a) were limited to the epidote group, which is somewhat
common in soils. These minerals are rated just below quartz by Allen
and Hajek (1989) with respect to stability. These minerals are all CAI-
negative (Table 3) in Fig. 5, as R2211 falls on the leading edge of doublet
absorptions centered from 2225 nm.
The cyclosilicates shown in Table 3 consist only of the tourmaline
group and beryl. Tourmalines are common in soils (Soil Survey Staff,
Fig. 5. Nesosilicate (zircon and topaz) and sorosilicate (epidote) spectra from Clark et al.
(2003b). CAI and LCA values are presented in Table 3.
are strongly CAI-negative due to strong mineral-OH absorptions
affecting R2211. Beryl is somewhat rare (Soil Survey Staff, 2007a) and
CAI-neutral (Fig. 6).
4.2.2.2. Inosilicates. Inosilicates range in value from CAI-negative to
CAI-positive (Table 3). The pyroxene group varies from slightly CAI-
negative to slightly CAI-positive. Undifferentiated pyroxenes were
Fig. 7. Selected spectra of amphibole and pyroxene minerals from Clark et al. (2003b).
CAI and LCA values are presented in Table 3. The reflectance axis scale is reduced to
accentuate differences between spectra.
common in soils as seen in Table 3, as many mineralogical analyses
performed by the NRCS-NSSC-SSL (Soil Survey Staff, 2007a) did not
differentiate them into individual minerals. Grain size variations affect
the CAI and reflectance values of Hypersthene PYX02 (Clark, 1999
Fig. 21a; 2003b). The highest CAI value occurs for the 60 µm fraction
(incidentally the median size fraction), which also has the largest
overall difference in reflectance values between the broad highly
disordered octahedral site Fe2+ absorption band in the 1750 to
1950 nm range (Hunt & Salisbury, 1970) and the shoulder from
2200 nm and higher. These high differences in reflectance results in a
more concave spectral shape in the CAI spectral region for the median
particle size fraction relative to the other size fractions. Overall
reflectance increases as grain size decreases for this mineral specimen
(Hunt & Salisbury, 1970; Clark et al., 2003b). As CAI is dependent upon
its band reflectances relative to adjacent absorption features, the
relationship between grain size and CAI will be mineral specific. There
is no consistent relationship between the grain size of other minerals
and CAI, as can be seen in data available on the FTP site (please look at
grain size information for two phyllosilicates, Chlorite SMR-13 and
Antigorite NMNH96917 which show the opposite behavior for similar
size fractions). A spectrum of augite (WS588), one of the more
common pyroxenes (Soil Survey Staff, 2007a), may be seen in Fig. 7,
and lacks any major CAI-affecting absorption features. Both
hypersthene and augite were reported by Allen and Hajek (1989) as
being common accessory minerals in soils even though they are
relative unstable minerals.
Minerals of the amphibole group range from CAI-negative
(glaucophane, anthophyllite, and actinolite), CAI-neutral (hornblende,
Fig. 7), to CAI-positive (tremolite, Fig. 7). A spectrum for magnesio-
hornblende (NMNH117329), a common amphibole, is shown in Fig. 7,
like augite, is spectrally featureless throughout the CAI bands. Of the
amphibole minerals. Allen and Hajek (1989) stated that of amphibole
mineral species, hornblendewas the most common, but tremolite and
actinolite were also reported in certain soils. It should be noted that
while tremolite (Fig. 7) is CAI-positive due to slight concavity of the
CAI band spectral space, no CAI-positive soils were discovered by
Serbin et al. (unpublished data).
4.2.2.3. Phyllosilicates. This mineral class, which is of major
importance in most soils, shows a good deal of variability in CAI values,
although none are strongly CAI-positive (Table 4). Clay minerals, which
include the kaolinite, illite, and montmorillonite (smectite) groups, are
moderately to strongly CAI-negative due to the mineral-OH absorption
affecting R2211 (Fig. 8). Of these, halloysite is the most CAI-negative,
whereas illite is the least CAI-negative. Kaolinite was the most common
clay mineral in the NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a),
with illite being the least common, even though illite was shown by
Allen andHajek (1989) to bemore stable thanhalloysite. Allen andHajek
(1989) reported that kaolinitewasmore common thanhalloysite in soils,
which agrees with their stability claims. We would like to reiterate that
in theNRCS-NSSC-SSLdata base (Soil Survey Staff, 2007a) differentiation
of clays into specific species was frequently not performed, and inmany
cases phyllosilicate mixtures (e.g., montmorillionite-mica, vermiculite-
mica) were reported in significant numbers. Furthermore, a number of
authors have noted that illite is frequently undifferentiated from clay-
sizedmicas and that there is somedebate as to its exactnature relative to
other minerals, e.g., muscovite, sericite, and glauconite (Whittig &
Allardice, 1986; Allen & Hajek, 1989; Fanning et al., 1989). From a
mineralogical perspective illite is differentiated from muscovite in that
it has lower Al and K in the crystal lattice (Deer et al., 1992; Klein &
Hurlbut, 1993).
Serpentine minerals range from slightly CAI-negative to CAI-
phy
R210
0.46
0.70
0.49
0.52
0.35
0.83
0.75
0.57
0.14
0.36
0.25
0.73
0.70
0.38
0.88
0.27
0.86
0.83
0.76
0.82
0.84
0.81
0.23
0.04
0.51
0.71
0.33
0.33
. Un
il sa
agg
231G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
amphiboleminerals, hornblende (containing Fe andMg endmembers)
was the most common in the NRCS-NSSC-SSL (Soil Survey Staff,
2007a) data base (Table 3), followed by other undifferentiated
Table 4
Minimum and maximum CAI, LCA, and absorption band reflectance values for selected
NSSC-SSL database (Soil Survey Staff, 2007a)
Mineral class Compound Group CAI
Phyllosilicate Halloysite Kaolinite −10.5–−9.2
Kaolinite −8.3
Illite Illite −4.5
Montmorillonite Montmorillonite −9.6
Antigorite Serpentine −0.7–1.7
Chrysotile −0.1
Pyrophyllite – −5.0–−4.0
Talc – 1.6–1.7
Chlorite Chlorite −13.8–0.7
Biotite Micaa 0.3–0.4
Glauconite −0.1
Muscovite −14.9–−7.6
Phlogopite 0.8–1.7
Vermiculite Vermiculite −0.7
Tectosilicate Albite Plagioclase Feldsparb −0.8
Labradorite −0.2
Oligoclase −3.3–−1.7
Anorthite −0.8–−0.3
Microcline Alkali Feldsparb −0.6
Orthoclase −0.5
Sanidine −0.4
Quartz Quartzc −0.6–−0.3
Chalcedony −6.4–−1.7
Opal −7.6–−1.1
Nepheline Nepheline −0.7
Analcime Zeolited −0.6
Stilbite −1.5
Other zeolites −6.1–−0.9
a Undifferentiated micas were found 22,537 soil samples, and sericites in 68 samples
b Undifferentiated plagioclase and alkali feldspars were found in 5576 and 13,078 so
c Opal was reported as “plant opal,” diatoms were reported in 196 samples, siliceous
d
 Undifferentiated zeolites were found in 426 samples.
positive, with certain samples exhibiting an absorption in around and
just above R2101 (Table 4). Serpentine minerals were somewhat rare in
the NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a) and rare in
llo- and tectosilicates from Clark et al. (2003b) and occurrence of minerals in he NRCS-
1 LCA ASTER6 Spectra NRCS-NSSC-SSL samples
–0.62 −11.7–−9.6 0.24–0.39 2 1693
−4.5 0.48 1 31,260
−4.8 0.42 1 185
−5.8 0.43 1 17,271
–0.66 12.1–24.2 0.37–0.66 7 127
13.5 0.83 1 19
–0.76 22.6–30.8 0.59–0.64 2 83
–0.76 22.8–29.8 0.55–0.75 2 165
–0.88 −6.1–21.2 0.24–0.84 25 5150
–0.47 −1.1–1.5 0.39–0.50 3 7381
5.1 0.27 1 81
–0.79 −25.6–−14.6 0.48–0.61 10 6751
–0.76 18.4–25.1 0.71–0.77 3 48
14.5 0.43 1 9426
−1.3 0.87 1 59
−0.7 0.27 1 4
–0.89 −1.9–−1.5 0.82–0.86 2 6
−1.2–−0.9 0.82–0.83 2 1
−1.6 0.75 1 313
−1.1 0.82 1 7
−0.8 0.83 1 42
–0.87 −0.9–−0.8 0.81–0.872 20,629
–0.59 −7.4–0.6 0.20–0.48 2 3403
–0.63 −8.2–−0.8 0.03–0.52 2 5013
0.3 0.50 1 12
6.9 0.70 1 23
–0.51 9.4–10.4 0.35–0.53 2 5
–0.68 −6.1–10.8 0.36–0.69 8 ?
differentiated micas may also account for illite.
mples, respectively. Undifferentiated feldspars were reported in 2732 samples.
regates were in 12 samples.
mineral group is frequently undifferentiated by individual mineral
species, as was the case with the NRCS-NSSC-SSL data base (Soil
Survey Staff, 2007a). This could present a problem should such
adequate mineralogy information be lacking. While Allen and Hajek
(1989) and Barnhisel and Bertsch (1989) stated that chlorites can be
relatively unstable, this is dependent upon the nature of the chlorite
mineral. Chlorites inherited from rocks (primary chlorites) are usually
unstable in acidic weathering environments and are usually found in
recent deposits, Aridisols, and Mollisols, but aluminous or pedogenic
(secondary) chlorites associated with vermiculite are relative stable
phases found inmore highly weathered soils, e.g., Ultisols (Barnhisel &
Bertsch, 1989). Additionally, Barnhisel and Bertsch (1989) reported
that primary chlorite weathers to either vermiculite or smectite with
intermediary chlorite:vermiculite (e.g., corrensite) and chlorite:smec-
tite phases, respectively. Chlorites were common (5150 samples) in
the NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a).
Micas, which are very common in soils as confirmed in the NRCS-
NSSC-SSL data base (Soil Survey Staff, 2007a), vary from strongly CAI-
negative for muscovites to slightly CAI-positive for phlogopite
(Table 4). Biotites are CAI-neutral. Biotite was identified more times
(7381) in the NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a) than
muscovite (6751); phologopites were identified only 48 times.
Undifferentiated micas were identified 22,537 times; as stated
previously some of these may indeed be illites or glauconites, the
latter of which differ from illite in that they have increased Fe contents
(Allen & Hajek, 1989; Fanning et al., 1989). However, Allen and Hajek
(1989) stated that muscovites are more resistant than biotites to
weathering, and that muscovite:biotite ratios increase with soil age.
Fig. 8. Selected spectra of clay minerals from Clark et al. (2003b). CAI and LCA values are
presented in Table 4. The reflectance axis scale is reduced to accentuate differences
232 G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
soils generally (Allen & Hajek, 1989; Dixon,1989), as they are relatively
unstable minerals, which quickly weather to other minerals.
The chlorite group show a wide range of values from slightly CAI-
positive (0.7 for the Thuringite SMR-15.db30 µm grain size fraction) to
strongly CAI-negative (−13.8 for the Cookeite CAr-1.a 104–150 µm
grain size fraction) as shown in Table 4. It should be noted that the
between spectra.
majority of the chlorite group samples yield CAI-negative values due
to their general convex shapes in the CAI band space (Fig. 9). Also, this
Fig. 9. Selected spectra of chlorite minerals from Clark et al. (2003b). CAI and LCA values
are presented in Table 4.
Klein and Hurlbut (1993) stated that biotite is black in color, which
makes it more easily identified in hand specimens than muscovite
(usually clear) or phlogopite (yellow-brown). For muscovites the
dominant factor affecting their CAI negativity is the substitution of Al
in the crystal structure by other elements, which slightly affects Al–O–
H bond lengths (Duke, 1994; Clark, 1999). For example, as Al decreases
the absorption affecting R2211 shifts toward longer wavelengths (Duke,
1994; Clark, 1999) and results in a decrease in CAI value (Fig. 10). This
effect also occurs in illite (1999). Clay-sized muscovite and paragonite
Fig. 10. Selected spectra of mica minerals from Clark et al. (2003b). CAI and LCA values
are presented in Table 4.
CAI-negative, and are very common in soils (Allen & Hajek, 1989; Soil
Survey Staff, 2007a). In Fig. 11, the two endmembers for the
plagioclase group, albite (GDS30) and anorthite (GDS28), and the
alkali feldspar orthoclase (NMNH113188) appear similar, with an-
orthite and orthoclase overlapping spectrally as does quartz (GDS310)
and albite. These spectra do not have any noticeable absorption
features affecting CAI, which suggests no alteration or presence of
sericite, which would induce absorption in R2211. The CAI-negative
values for oligoclase are due to weak absorption features affecting
R2211 due tomuscovite impurities (Clark et al., 2003b). Other members
of the quartz group, such as chalcedony (which includes chert) and
opal are all CAI-negative, and relatively common in soils (Allen &
Hajek, 1989; Soil Survey Staff, 2007a). Zeolites range from slightly to
moderately CAI-negative and are relatively rare in soils (Allen & Hajek,
1989; Soil Survey Staff, 2007a).
4.2.2.5. Hydroxides, oxides, and sulfides. Minerals of the hydroxide
and oxyhydroxides groups trend from CAI-neutral to strongly CAI-
negative, though hydroxides common in soils (Table 5) are all CAI-
negative. The most CAI-negative of the group is gibbsite, which is also
a relatively dark mineral with R2101=0.28 and R2211=0.11 (due to a
broad absorption centered around 2275 nm). Gibbsite is also a
common mineral in tropical soils (Allen & Hajek, 1989; Soil Survey
Staff, 2007a), thereby giving it increased potential to bias overall soil
spectral properties (Fig. 12). While diaspore is similarly as dark as
gibbsite (except for its R2211 where it is brighter), it is only slightly CAI-
negative and has a relatively flat spectrum. Diaspore did not appear atFig. 11. Selected spectra of quartz and feldspar minerals from Clark et al. (2003b). CAI
233G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
are referred to as sericite and are frequently found within altered
feldspar grains (Deer et al., 1992; Klein & Hurlbut, 1993). The one
vermiculite sample we analyzed (Table 4) is slightly CAI-negative.
Vermiculites are common in soils (Soil Survey Staff, 2007a) and
moderately stable secondary minerals (Allen & Hajek, 1989).
4.2.2.4. Tectosilicates. Table 4 includes the results for all tectosilicate
minerals; selected feldspars and quartz are shown in Fig. 11.
and LCA values are presented in Table 4.
Plagioclase and alkali feldspars and quartz vary from CAI-neutral to
Table 5
Minimum andmaximum CAI, LCA, and absorption band reflectance values for selected nonsil
database (Soil Survey Staff, 2007a)
Mineral class Compound Group CAI R2101
Hydroxidea Brucite – −4.4 0.41
Diaspore – −0.6 0.24
Gibbsite – −8.0 0.28
Goethite – −2.3–−1.9 0.49–0.56
Limonite – −2.0 0.25
Oxidea Corundum Hematite −0.2 0.49
Hematite −1.2 0.82
Maghemite 0.1 0.30
Cassiterite Rutile −0.1 0.05
Rutile −0.7 0.64
Magnetite Spinel 0.1 0.05–0.08
Sulfidea Galena – −0.2–0.2 0.05–0.11
Pyrite – 0.0 0.08
Sphalerite – −1.2–0.2 0.18–0.30
Carbonateb Calcite Calcite −2.8–−2.1 0.87–0.92
Siderite −1.5 0.54
Dolomite Dolomite −4.5–−1.5 0.22–0.86
Phosphate Apatite Apatite −0.8–−0.6 0.54–0.82
Monazite – −3.9 0.50
Sulfate Gypsumc – −5.5–−5.4 0.39–0.57
Jarosite Alunite −6.4–−3.6 0.45–0.74
Thenardite – −0.7 0.89
a Undifferentiated iron oxides (goethite, magnetite, hematite) were found in 2923 sampl
b Petrographic analyses report undifferentiated carbonate aggregates in 1708 samples and
carbonates.
c Gypsum was found in 3149 samples using the multiple dissolution method.
all in the NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a); Hsu
(1989) reports that while diaspore is commonly found in bauxite ore
deposits, its presence in soils is uncertain. Goethite is significantly
brighter, is more CAI-negative, and is a common soil mineral (Allen &
Hajek, 1989; Soil Survey Staff, 2007a).
Oxide minerals range from slightly CAI-negative to CAI-neutral in
Table 5 (except certain rare-earthelement oxides which are not
shown). The major differences here are displayed in reflectance data.
Certain oxides, such as magnetite (Fig. 13) and cassiterite, exhibit very
low reflectance values, and thus serve to decrease bulk soil reflectance
and make themmore CAI-neutral (Fig. 20 in Clark, 1999). Magnetite is
icateminerals from Clark et al. (2003b) and occurrence of minerals in he NRCS-NSSC-SSL
LCA ASTER6 Spectra NRCS-NSSC-SSL samples
0.8 0.31 1 1
0.9 0.23 1 0
−1.7 0.12 1 4140
3.9–4.4 0.26–0.54 2 4625
5.0 0.23 1 40
1.3 0.49 1 34
1.6 0.81 1 2515
−0.9 0.31 1 4
0.3 0.05 1 143
−0.4 0.64 1 959
−0.1–0.3 0.05–0.08 2 116
−0.4–−0.1 0.05–0.10 4 3
−0.2 0.08 1 13
1.8–3.4 0.12–0.21 3 12
21.9–24.7 0.82–0.88 2 5859
9.5 0.53 1 18
10.0–11.5 0.36–0.83 3 427
−0.6–1.1 0.55–0.81 3 304
7.3 0.48 1 302
−6.2–−5.6 0.28–0.47 2 69
−15.1–−2.3 0.41–0.70 6 2
−0.5 0.89 1 10
es. Opaque minerals were found in 8319 samples.
217 occurrences of foraminifera. Pressure calicimetry reported 45,682 samples with N0%
reflective (Table 5 and Fig. 13). Hematite is very common in soils,
unlike corundum (Allen & Hajek, 1989; Soil Survey Staff, 2007a).
Undifferentiated iron oxides (goethite, magnetite, and hematite) were
common in soil pedons of the NRCS-NSSC-SSL data base (Soil Survey
Staff, 2007a).
Sulfides tend to be CAI-neutral to slightly CAI-negative and dark
(Table 5). The only exception to this is cinnabar, which is significantly
brighter, CAI-negative (data available on FTP site), and not reported in
soils (Soil Survey Staff, 2007a). Theseminerals are not common in soils
and are generally only found in reducing environments (Allen & Hajek,
1989; Soil Survey Staff, 2007a). It should be noted that a large number
of opaque minerals in thin-section transmitted-light petrographic
microscope analyses (8319 entries) were found in the NRCS-NSSC-SSL
data base (Soil Survey Staff, 2007a) and that these often include
various hydroxide, oxide, and sulfide minerals.
4.2.2.6. Carbonates, phosphates, and sulfates. The reflectance spectra
of the carbonates, calcite and dolomite have major absorption features
near 2340 and 2320 nm, respectively (Fig. 14), and exhibit negative
CAI values (Table 5). These two minerals are common in soils (Allen &
Hajek, 1989), though mineralogical determinations of dolomite
occurred less frequently in pedon data from the NRCS-NSSC-SSL
data base (Soil Survey Staff, 2007a). Pressure calicimetry estimates
with values over 0% gravimetric carbonate content were found for
45,682 samples (Soil Survey Staff, 2007a). The phosphate and sulfate
minerals are generally CAI-negative and do not vary much between
species (Table 5). Gypsum is the most common sulfate mineral (Allen
Fig. 12. Selected spectra of hydroxide minerals from Clark et al. (2003b). CAI and LCA
values are presented in Table 5.
234 G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
commonly found in beach sands, and was reported by Allen and Hajek
(1989) to be common in soils, even if it was not as commonly
identified in NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a).
Cassiterite was reported by Deer et al. (1992) to be commonly found in
detrital materials resulting from decomposition of tin-bearing felsic
igneous rocks and was found in more instances than magnetite in the
NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a). Other oxide
minerals such as corundum and hematite, are significantly more
Fig. 13. Selected spectra of oxide minerals from Clark et al. (2003b). CAI and LCA values
are presented in Table 5.
& Hajek, 1989; Soil Survey Staff, 2007a), but is rarely found outside
Aridisols and did not occur frequently in mineralogical analyses of the
NRCS-NSSC-SSL data base (Soil Survey Staff, 2007a). However, 3149
soil samples were found to contain gypsum contents N0% as estimated
by the multiple dissolution method (Soil Survey Staff, 2007a). Some
sulfate minerals, such as bloedite, coquimbite, and syngenite, are CAI-
positive, but were not reported in soils by the NRCS-NSSC-SSL (Soil
Survey Staff, 2007a). Bloedite was reported by Allen and Hajek (1989)
in some salt crusts. Phosphate minerals, though somewhat rare (Allen
& Hajek,1989), occurredmore often than sulfateminerals in the NRCS-
NSSC-SSL data base (Soil Survey Staff, 2007a).
Fig. 14. Selected spectra of calcite and dolomite from Clark et al. (2003b). CAI and LCA
values are presented in Table 5.
235G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
4.3. ASTER Lignin-Cellulose Absorption (LCA) Index
4.3.1. Organic compounds
Pure cellulose shows awide range of LCA-positive values from 14.9
to 20.9 (Table 2); lignin shows a narrower range from 5.9 to 7.6.
Humus WP-80 shows a slightly LCA-positive value at 1.5 with a low
reflectivity of 0.19. Black carbon (Clark et al., 2003b) is LCA-neutral and
very dark with a reflectance of 0.01, and activated charcoal is slightly
brighter. Spectra for these components are shown in Fig. 1.
Peat moss LCA is 5.9 and thus falls in the range of crop residues
(Table 2). Dry manures have higher ranges and means than of spring
crop residues (mean values: corn: 6.4, soybean: 6.7, cow manure: 9.0,
chicken manure: 7.2). Wet cow manures yield a wide range from
extremely LCA-negative (−44.7) to slightly LCA-positive (2.8) with a
mean value of 0.4. Green corn canopy ranges fall within the LCA range
of spring corn and soybean residues, with LCA ranging between 2.1
and 4.7. These crop residues displayed similar spectral shapes (Fig. 3),
though green corn is significantly darker than any of the crop residues
measured. The lack of separability between crop residue and green
vegetation helps explain results by Gill and Phinn (2008), who
reported reduced correlations for non-photosynthetic vegetation
cover estimation when significant green cover was present.
4.3.2. Minerals and mineral groups
Unlike CAI, LCA shows much more variability in the range of LCA
values, from strongly LCA-negative to strongly LCA-positive. In some
cases, common soil minerals, e.g., carbonates, are more LCA-positive
than crop residues. Furthermore, Serbin et al. (unpublished data)
showed that certain soils are indeed LCA-positive and even in the
range of crop residues. Situations like this can result in poor dis-
crimination between soil and crop residue, limiting the applicability of
LCA for crop residue cover estimation.
4.3.2.1. Nesosilicates, sorosilicates, and cyclosilicates. Nesosilicates
vary from LCA-negative to LCA-positive (Table 3). Olivines vary from
LCA-neutral to slightly LCA-negative. Staurolite is both LCA-neutral
and low albedo. The garnet group (Fig. 4) shows greater variability,
with hydrogrossular being the most LCA-negative mineral in this
group, as was the case with its CAI value. This is mainly due to the
absorption affecting ASTER band 6. Zircon (Fig. 5) yields a slightly
negative LCA. Topaz (Fig. 5) is LCA-positive (Table 3), with the range
extending into those for corn and soybean residues (Table 2).
For sorosilicates (Table 3), all minerals of the epidote group are
strongly LCA-positive, with values frequently exceeding pure cellu-
lose, due to strong absorptions affecting ASTER band 8 (Fig. 5).
Cyclosilicates (Fig. 6) vary from LCA-negative to LCA positive. The
tourmaline group are all LCA-negative (Fig. 6) due an absorption in
ASTER band 6, whereas beryl is LCA-positive and within the range of
crop residues.
4.3.2.2. Inosilicates. Pyroxenes vary from LCA-negative to LCA-
positive, with hypersthene exhibiting the lowest LCA value. Notably,
hypersthene also extends slightly into the LCA-positive range
(Table 3). Like with CAI, LCA values are affected by changes in band
reflectance as a function of spectral distance to the broad dioctahedral
Fe2+ absorption centered between 1750 and 1950 nm. Enstatite shows
the highest LCA value (Table 3).
All amphiboles are strongly LCA-positivewith the exception of
cummingtonite, which is only slightly LCA-positive (1.6, not shown in
Table 3). Most amphiboles LCA values are similar in value to the upper
range of crop residues and pure cellulose. These are due to an ab-
sorption feature affecting ASTER band 8 wavelengths, as can be ex-
emplified by Augite WS588 shown in Fig. 7.
4.3.2.3. Phyllosilicates. Clay minerals (kaolinite, illite, and mon-
tmorillonite groups) are all LCA-negative due to absorptions affecting
ASTER band 6 (Fig. 8); of these, halloysite is the most strongly LCA-
negative. Serpentines, pyrophyllite, and talc are all strongly LCA-
positive (Table 4) with values in the range of and exceeding those for
cellulose. This is due to absorptions affecting ASTER band 8.
Chlorites are generally strongly LCA-positive (Table 4) with values
in the range of those for pure cellulose due to absorptions affecting
ASTER band 8 (Fig. 9), with a few exceptions. b30 µm fractions of
thuringite and prochlorite yield significantly lower LCA values at 2.65
and 2.21, and cookeite is the only LCA-negative chlorite.
Micas exhibit a wide range of LCA values (Table 4). Biotite ranges
from slightly LCA-negative to slightly LCA-positive and lacks any
major absorption features, unlike muscovite, which is very strongly
LCA-negative due to an absorption feature affecting ASTER band 6
(Fig. 10). Glauconite is LCA-positive due to an absorption affecting
ASTER band 8. Phlogopite is very strongly LCA-positive due to an
absorption at ASTER band 8 wavelengths, with some values exceeding
that of pure cellulose. Vermiculite is strongly LCA-positive and within
the range of values for pure cellulose.
4.3.2.4. Tectosilicates. Feldspars (Table 4) are all slightly LCA-
negative; a few (not shown) are more strongly LCA-negative (perthite
at −2.72) to LCA-positive (bytownite plagioclase at 0.81 and celsian at
4.06). The feldspars (Fig. 11) are almost spectrally featureless except
slight absorption features affecting ASTER band 6. Pure quartz is
slightly LCA-negative, but chalcedony quartz varies from strongly LCA-
negative to slightly LCA-positive. Opal also varies greatly from strongly
to slightly LCA-negative. Most zeolites are LCA-positive andwithin the
range of spring crop residues, with some even in the range of fall crop
residues. Only natrolite is LCA-negative.
4.3.2.5. Hydroxides, oxides, and sulfides. Hydroxide minerals
(Table 5) show some variability in LCA-value. Brucite and diaspore
are both slightly LCA-positive, while gibbsite is slightly LCA-negative
and displays low reflectance. Goethite and limonite are both LCA-
positive and in the range of crop residue. This is due to absorptions
affecting ASTER band 8 (see goethite spectrum, Fig. 12).
The oxide minerals (Table 5) range from slightly LCA-positive
(e.g., corundum and hematite, Fig. 13) to slightly LCA-negative
(e.g., maghemite). Sulfide minerals (Table 5) all have low reflectivity
and were generally LCA-neutral, except for sphalerite which is LCA-
positive.
4.3.2.6. Carbonates, phosphates, and sulfates. The carbonate miner-
als (Table 5) are all strongly LCA-positive. Siderite is within the
range near those of spring crop residues; dolomite has LCA values
around those of the high end of the fall crop residue ranges, due to a
strong absorption in ASTER band 8. Calcite LCA values exceed those
of pure cellulose, due to absorptions in both ASTER bands 5 and 8
(Fig. 14).
Apatite minerals vary from slightly LCA-negative to slightly LCA-
positive (Table 5). Monazite is LCA-positive and within the range of
most crop residues, due to an absorption in ASTER band 8.
Sulfate minerals (Table 5) such as jarosite range from very strongly
LCA-negative to LCA-negative. Gypsum is LCA-negative, and thenar-
dite is LCA-neutral.
4.4. Individual mineral bias on bulk soil reflectance
Fig. 15 shows two similar spectra, one of Kaolinite CM3 (Clark et al.,
2003b) and the other of a Clifton series Ultisol (clayey, mixed, mesic
TypicHapludult)AP clay loamhorizon fromNorthCarolina (Brownet al.,
2006; Soil Survey Staff, 2007a). TheClifton series soil sample has CAI and
LCA values of −9.8 and −5.1, respectively; Kaolinite CM3 CAI and LCA
values are −8.3 and −4.5, respectively. Soil mineralogical analyses of the
Clifton series sample using X-ray diffractometry showed the presence of
kaolinite followed by goethite and vermiculite; the pedon description
236 G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
alsomentions the presence of 11%mica flakes (Soil Survey Staff, 2007a).
Irrespective of the otherminerals, the spectral properties of kaolinite are
dominant for this particular soil sample in the SWIR region towhich CAI
and LCA are sensitive.
5. Discussion
5.1. Spectral mixing of materials
The spectral mixing of heterogeneous materials in a scene is
dependent on a number of factors, which are discussed in depth by
Fig. 15. Selected spectra of Kaolinite CM3 from Clark et al. (2003b) and Clifton series AP
horizon from Brown et al. (2006).
Salisbury and Hunt (1968) and Clark (1999). Clark (1999) described
four general classes of spectral mixtures: linear (areal) mixtures,
intimate mixtures, coating mixtures, and molecular mixtures. All four
conditions can coexist under different circumstances, though in this
case we use linear mixing to resolve soils and overlying crop residues
from one another.
When looking at a scene, soil and residuemix in a linear fashion for
a given spectral reflectance band Rλ according to:
Rλ;mix ¼ fresRλ;res þ 1−fresð ÞRλ;soil ð3Þ
where 0≤ fres≤1 and denotes the residue cover fraction, and the
subscripts mix, res, and soil of Rλ denote spectral mixture, residue, and
soil reflectances, respectively. Eq. (3) was shown to be valid for cases
where crop residues lay on top of soil at nadir by Daughtry and Hunt
(2008) and for aircraft acquired remote sensing imagery by Serbin et al.
(2008). Because of the simple manner in which CAI and LCA are
calculated, crop residue and soil endmember and scene index valuesmay
be used in place of Rλ and Eq. (3) inverted to solve for residue cover. This
allows for rapid estimation of residue cover fromCAI and LCA should soil
spectral properties be known. This inversion method was also shown by
Serbin et al. (2008) to be more accurate when soil spectral classes were
accounted for than linear regression of pixel CAI vs. line-point transect
residue cover estimates for estimation of crop residue cover.
It should be noted, however, that crop residue can be coated with
soil, which can partially obscure absorption features characterized
using CAI and LCA. In this case, the crop residue endmember index
value will become modified by the proportion of the surface covered
by soil, soil optical properties (reflectance, absorbance, and transmit-
tance), and soil crust thickness.
5.1.1. Effect of intimate mixing of dry solids in soils
Unlike spectralmixingbetweenwhole soils and crop residues,which
is generally very simple as shown by Eq. (3), the spectral mixing of soil
componentswithin the soils ismore complicated. Soils, by and large, are
heterogeneousmixturesofmineral and/ororganicmatter andof varying
particle sizes. They are considered intimate spectralmixtures, as the soil
grains are in intimate physical contact with one another. Particleswhich
have low reflectance at a given wavelength (i.e., absorb most incident
photons at that wavelength) will have a much greater effect on
reflectance per weight percentage than will bright particles which will
either transmit or reflect photons (Fig. 20 in Clark, 1999). Absorption
features are locally darker than thematerial spectral continuumand can
also significantly affect soil reflectance properties, and thus, bias soil CAI
and LCA values. This effect is shown in Fig. 15 with respect to the Clifton
series soil and kaolinite. In some cases, one material can coat other soil
materials,thus having enhanced effects (Fig. 19 in Clark, 1999), such as
humus on soil mineral particles, which results in decreases in soil
reflectance with increase in organic carbon content (McCarty et al.,
2002; Brown et al., 2006; Serbin et al., 2008). Particle size also affects
reflectance, with this phenomenon being controlled by opacity (Salis-
bury & Hunt,1968). Transparentmaterials will increase in reflectance as
particle size decreases, and conversely, opaque materials decrease in
reflectance as particle size decreases. A third class, trans-opaque,
describes materials that are transparent in one part of the spectrum
yet opaque in another, e.g. limonite in the visible region of the spectrum
(Salisbury & Hunt, 1968; Irons et al., 1989; Clark, 1999). Because of these
issues, determination of soil spectral properties from non-spectroscopic
laboratory methods would require reasonably qualitative and quanti-
tative data on soil mineralogy, organic matter content, particle size
fractions, and structure, something that may be prohibitively expensive
and time-consuming. However, knowledge of the major mineralogy
components of a soil can help in determining whether a soil is likely to
have a spectral index value bias. Such is the case with the kaolinite
containingClifton series soil in Fig.15 andother soils described by Serbin
et al. (unpublished data). This allows for targeted sampling using field
spectroscopy.
5.1.2. Effect of liquids, i.e., molecular mixing on soils and residues
Liquid–solid mixtures, such as soil and adsorbed liquid water, are
treated as molecular mixtures because they induce band shifts (Clark,
1999) and modify the index of refraction n by replacing air (n=1) with
water (n=1.33). The index of refraction of water is similar to those of
silicateminerals, ranging between 1.4 and 1.7 (Deer et al.,1992; Lobell &
Asner, 2002;Whiting et al., 2004), thus resulting in decreases in soil and
crop residue reflectance (Nagler et al., 2000; Ben-Dor, 2002; Daughtry &
Hunt, 2008 and references therein; Serbin et al., 2008). Daughtry and
Hunt (2008) showed that as thewater contents of soils and crop residues
increased, overall reflectance decreased and the water absorptions
centered at 1940 nm (Curcio & Petty, 1951; Workman & Weyer, 2008)
became wider, decreasing R2031 the most followed by R2101, effectively
resulting in a decrease in CAI for residues, with wet residue CAI values
equaling that of green vegetation.
6. Conclusions
Overall, most common soil minerals are either CAI-neutral or CAI-
negative. Some important minerals, such as the clay minerals and
muscovite, are strongly CAI-negative. While some minerals (e.g.,
topaz, tremolite) are somewhat to strongly CAI-positive, they gene-
rally are rare in soils. Furthermore, Serbin et al. (unpublished data) did
not find a single CAI-positive soil. The presence of appreciable
amounts of charcoal, humus, or dark minerals such as magnetite
tend to make soils less CAI-negative and counterbalance the effect of
237G. Serbin et al. / Remote Sensing of Environment 113 (2009) 224–238
any strongly CAI-negative minerals. Heavily manured fields could also
be made CAI-positive if significant amounts of undigested or bedding
material (i.e., crop residue) cellulose were present in the manure.
Furthermore, residue cover assessments would be most accurate for
CAI-negative soils than CAI-neutral soils because of the greater range
of CAI values distributed amongst the crop residue and soil fractions of
a pixel. CAI may not be effective for monitoring crop residues over
high-cellulose soils such as peat moss, due to the small differences in
CAI between this soil and overlying residue. Because soil composition
varies spatially, the use of CAI in conjunction with soil mineralogical
and surface organic matter maps may aid in crop residue cover
estimates (Serbin et al., 2008, unpublished data).
UnlikeCAI,which showsa clear separationof valuesbetweencommon
soilminerals and crop residues, LCAdoes not. Common soilminerals such
as amphiboles, chlorites, iron hydroxides (goethite and limonite), and
especiallycarbonates causedLCAvalues tovarygreatly,withvalues similar
to or greater than those of crop residues. Serbin et al. (unpublished data)
observed instanceswhere the soil LCAvalueswere similar to those of crop
residues,making soil–crop residuediscriminationdifficult. For this reason,
ASTER LCAmayonly be useful in specific environmentswhere carbonates
and other problematic minerals are not prevalent.
The LCA values of crop residue and live vegetationwere also found
to be similar, because ASTER bands do not adequately resolve the O–H
stretching and C–O bending combination absorption feature at
2101 nm. Thus, NDVI images would be useful with LCA in order to
account for exclude pixels with excessive green pixels, e.g., where
NDVIN0.3 (Daughtry et al., 2005). With this in mind, residue cover
estimation using ASTER may still be possible if local variations in soil
composition are accounted for.
Soil mineralogical analyses conducted by the NRCS-NSSC-SSL (Soil
Survey Staff, 2007a) do not adequately separate some mineral groups
into individual compositional endmembers. This is particularly true
for mineral groups that yield wide ranges of CAI or LCA, such as the
garnet and chlorite groups. Furthermore, soil mineralogy and
component spectral properties can vary with particle size fraction,
soil structure, and organicmatter content. Thus, determining the exact
soil mineralogy and particle size fraction data for a regional soil survey
can be prohibitively expensive. The best approach to addressing this
problem is to use existing soil mineralogical analyses as a way to
determine which soils need further spectroscopic analyses in order to
determine their soil CAI and LCA values.
For space-based crop residue and dry vegetation cover mapping,
future multispectral sensors should incorporate bands that capture
the characteristic O–H stretching and C–O bending combination
absorption in cellulose that forms the basis for CAI.
Acknowledgements
The authors would like to acknowledge Humus Products of
America, Inc. (Richmond, TX) for providing a sample of Humus WP-
80 powder for use in spectral measurements. Robert Parry of the
USDA-ARS Hydrology and Remote Sensing Laboratory is acknowl-
edged for his work in helping create the FTP site on which these data
are hosted. Adam Hidey and Michael Bayless are acknowledged for
their assistance in acquiring laboratory spectral data. Gregory W.
McCarty and Jerry C. Ritchie are from the USDA-ARS Hydrology and
Remote Sensing Laboratory and four anonymous reviewers are
acknowledged for their helpful reviews of this paper.
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