Class ProjectMapping of Cr.docx

Class Project
Mapping of Crop Residues Using Hyperspectral Data:
· Techniques and indexes for quantification,
· Data sources and
· Unsupervised classification for tillage systems

Table of contents / INDEX
Topic
Page
1.
Problem / application
3
2.
Working hypothesis
3
3.
Project outcomes
4
4.
Literature review
4
5.
Data sources
8
6.
Methods
10
7.
Results
16

8
Issues and learning
20
9
Conclusions and future works
21
10.
Annex 1. Corrected bands and columns
22
11
Annex 2. Copy of in-running matlab code for de-striping
23
12
References
25
2

1. PROBLEM / APPLICATION
Agriculture is a widespread, basic activity around the world,
which main purpose is to harvest food, fiber or/and energy.
After every growing season residues are left in fields. It is
important to quantify the amount and cover of agricultural
residues for enhancing the understanding in global
biogeochemical cycles, and for applications such as their role
for preventing soil erosion and their contribution in carbon
sequestration. However, it is not completely understood yet how
to estimate crop residues cover, their discrimination under
tillage or no tillage cropping systems, and its seasonal
variability as well as their temporal changes. This class project
proposes to explore the estimation and mapping of crop residues
by remote sensing techniques using hyperspectral image data.
2. WORKING HYPOTHESIS
Crop residues cover and amount can be accurately estimated by
remote sensing techniques. A wide range of crop species and
their residues can be studied in the near future and they might
be even differentiated by spectral classification. Future work
might include description of temporal patterns upon analyzing
hyperspectral data (EO-1 Hyperion) in complement with
multispectral data (Landsat 7 ETM+ and EO-1 ALI).

3
3. PROJECT OUTCOMES
This class project will generate an estimation of crop residues
cover in agricultural fields in Central Indiana in Tipton County.
In addition, the amount of crop residues will be approximately
calculated based upon yield/residues ratio assumptions. Also,
unsupervised classifications for different tillage management
(two classes: tilled areas and no-tilled areas) in agricultural
fields in Tipton County. Finally, by this study we expect to
integrate/use three different data sources (Landsat 7 ETM+, EO-
1 ALI and EO-1 Hyperion) and to calculate Cellulose
Absorption Index on hyperspectral data.
4. LITERATURE REVIEW
Crop residues are any portion of crop plants that is left in the
field after harvest. Crop residues cover is a relevant topic to be

studied because of three main reasons: they are widespread in
the landscape of agriculture in the Midwest, they represent one
of the most important organic inputs for soil carbon
sequestration estimating input, and also they relate to soil
conservation and reduction of soil erosion (Lal, 2002 & 2004).
Remote sensing techniques are also a potential for these
methods for monitoring compliance with conservation measures
or perhaps in the future carbon credits.
Agricultural management practices such as tillage and crop
rotation (West, 2002) are driving factors for crop residues
accumulation in field. In any case, authors such as Nagler et al
(2003) have pointed
4
out that remote sensing methods would be useful for providing
wider area coverage in a regional landscape studies as well as
estimation of spatial structure/variability of residues cover in
agricultural settings. Spectral variability is another important
outcome from remote sensing methods, which in general is
overlooked in manual methods for crop residues determination.
One of the first studies assessing crop residues estimation by a
remote sensing method was by Biard and Baret (1997). They
appealed to multiband reflectance under an algorithm called

Crop Residue Index Multiband (CRIM) which is based on any
set of wave bands and consists on a linear mixing model of soil-
residue complex and on soil and residue lines. However the
same authors found dependency of the near infrared and middle
infrared domains according to the aging state of the residues,
which may be due to decomposition of key macromolecular
compounds such as lignin and cellulose.
At about the same time as Biard and Baret (1997), Su et al
(1997) also proposed another way to assess crop residues by
SAIL model. It consisted in Scattering by Arbitrarily Inclined
Leaves, which simply simulated the residue reflectance in wheat
in near infrared band, and then it was possible to study the
agreement between field measured reflectance and the simulated
reflectance. They concluded that SAIL model was a promising
technique for crop residues determination at that point in time.
Daughtry et al (1997) studies recorded potential problems that
must be addressed to implement the fluorescence technique in
the field,
5
which are: adequate excitation energy must be supplied to
induce fluorescence, and the fluorescence signal is small
relative to normal, ambient sunlight. The technique must be

developed to either shield the system from sunlight or extract
the fluorescence.
Nagler et al (2003) studied the use of Cellulose Absorption
Index (CAI) as a way to quantify the plant litter cover after
harvest even at low cover percentage (around 10 %). Their
research conducted analysis from 0 % cover (bare soil) up to
100 % plant litter cover and using reflectance spectra (0.4 to 2.5
μm) for four crops: corn, soybean, rice and wheat; plus two tree
species.
Following Nagler et al (2003) work, Daughtry et al (2004)
established that the spectra of dry crop residues displayed a
broad band absorption feature near 2100 nm due to absorption
by cellulose/lignin compounds. Therefore, these authors
proposed the combination of Cellulose Absorption Index (CAI)
and Normalized Difference Vegetation Index (NDVI) base upon
shortwave infrared reflectance in order to estimate crop residues
cover. Also, it seems to be important to make a difference
between dry residues and wet crop residues under this approach.
However, once again these authors concurred in the usefulness
of remote sensing methods in regional landscape surveys for
crop residues.
Continuing with their work on CAI, Daughtry et al (2006) used
multi/hyperspectral data (Landsat and Hyperion data) to
distinguish two classes in corn and soybean residues. Landsat
data did not yield good fit between field measurements and
remote sensing analysis. Conversely,
6

Hyperion yield linear relationship for CAI. In addition,
classification accuracy resulted in 80 % when working just in
two classes of tillage (conventional: reduced + intensive, and
conservational tillages). Consequently, it seems that an
advanced multispectral or hyperspectral data is needed when
assessing crop residues in a regional landscape scenario.
In this sense, Bannari et al (2006) also concurred with Daughtry
etal (2006) when pointing out that a hyperspectral data (e.g.
Probe-1) or anadvanced multispectral data (e.g. high spatial
resolution IKONOS data) is needed for precise evaluation of
surface crop residues cover because of better spectral band
characteristics, specially when looking at lignin/cellulose
absorption features. In other words, Probe-1 hyperspectral data
outperformed the IKONOS data because of the characteristics of
the spectral bands in the hyperspectral sensor.
Lately, South et al (2004) compiled and analyzed a very
interesting comparison of classification methods based upon
spectral reflectance signatures for mapping senescent crop
residues in Eastern Cornbelt soils. They concluded that out of
five different methods (including parametric an non parametric
ones) two spectral angle methods (spectral angle mapping and
cosine of the angle) were the ones with higher performance,
specifically the cosine of the angle concept algorithm had the
highest accuracy (97.2 %) and kappa value (0.959). In contrast,
minimum distance, Mahalanobis classifier and maximum
likelihood had user’s accuracy below 84 % and producer’s
accuracy around those values. Bannari et al (2006) also
established a validation for their supervised classification with

a divergence D of 0.86.
7
Utilizing aircraft and satellite image data (IKONOS
hyperspectral data), Martin (2002) developed both unsupervised
(using ISODATA algorithms) and supervised classifications
(using ECHO: extraction and classification of homogenous
objects and maximum likelihood algorithms) for different
tillage and crop rotation treatments in a location in the
Midwest. This author found that to compare tillage systems as
classes is the best method to estimate residue cover. Four
different treatments were investigated and those tillage systems
were indicative of the amount of residue present within a given
date but also that the total amount of residue differs during
seasons of the year due to decomposition.
5. DATA SOURCES
Location of the study is between Windfall City and Tipton City
in Tipton County IN, respectively. Coordinates are between
40º21’44” to 40º10’32” N and 85º57’25” to 86º1’11” W. This
narrow area was defined by the EO-1 Hyperion stripe (about 7.7

km wide).
Images data are Landsat 7 ETM+ (a single scene corresponding
to path 21 and row 32 radiometric and geometric corrected),
EO-1 Advanced Land Imager ALI (a single stripe radiometric
correcte), and EO-1 Hyperion (radiometric corrected). Landsat 7
and EO-1 correspond to the AM constellation as shown in
Figure 1. They were acquired in the same date (April 12th,
2003) under a zero cloud cover. All three image were subset to
extract about the same narrow area (sensor have different cover;
ALI does not cover all the Hyperion view), corresponding
8
to a specific geographic location in the center of this county in
order to study agricultural fields. Figure 2 shows the three
subsets. The data sets are already downloaded in the Oxisol
hard drive computer at LARS.
AB

Figure 1. AM Constellation including Landsat 7 and EO-1
spaceborne platforms (A) and swath with for three different
sensors: Landsat 7 ETM, ALI and Hyperion (B).
ABC
Figure 2. Three original image data subsets showing field areas
between two cities: Tipton City in the southwest and Windfall
City in the northeast. Landsat 7 ETM (A), EO-1 ALI (B) and
EO-1 Hyperion (C). Source: www.indianaview.org

9
The images in Figure 2 where prepared using RGB
representation of Landsat 7 bands: 4 (NIR 0.76 – 0.90 μm), 3
(red 0.63 – 0.69 μm) and 2 (green 0.52 – 0.60 μm).
Consequently, in case of ALI data bands 5 and
6 were averaged, and for Hyperion data average were prepared
as B42-
45, B28 – B33 and B18 – B25, respectively.
In addition to image data, we accessed data from the Annual
Crop Residue Management Survey 2002
(www.conservationinformation.org). It contains field data about
crop residues and tillage systems. Therefore, it allowed us to do
a comparison between image analysis and field data.
6. METHODS FOR DATA PROCESSING
Pre-processing include two main steps: de-striping and
conversion/corrections.

Problems with calibrations of the Hyperion hyperspectral
sensors results in stripes in the band images (which translates
into “bad columns or bad detectors”). They may show up as
dark stripes or clear stripes in vertical position.
Since in order to calculate the CAI index, one may need 9 SWIR
bands: (183, 184, 185, 195, 196, 197, 204, 205, 206). After
opening the bands and performing Gaussian and Linear 2%
enhancement, the bands were examining and found that bands
183, 184, 185 needed stripe correction. Figure 3 A show one
example (B196) of a band with any problematic columns, so de-
striping was not run on those other six
10
bands. Problematic columns were visually identified in the first
three bands. Annex 1 shows a list of bands and columns that
were corrected.
De-striping was performed by iterated running a MATLAB®
code (cor_str written at U. Texas) on the data. Annex 2 shows a
copy of the in-running code as example. The code uses 32
adjacent pixels from the previous columns in order to fix the

striped columns and bring it to a normal distribution. Figure 3
shows how the de-striping code yield positive results when
removing the stripes from bands 184 and 185. However, the
striped B185 was still noisy and during the index calculation, it
was excluded.
ABCDE
Figure 3. Hyperion Hyperspectral Bands: 196 (A), 184 before
(B) and after (C), 185 before (D) and after (E).
In order to analysis the effect of de-striping in noise reduction,
we performed minimum noise fraction (MNF) transform.
Generally MNF is used to reduce the dimensionality of
hyperspectral data; however a forward transformation can
remove the noise from data and in that way determine which
bands contain the coherent images by looking at the

11
actual images or to the eigenvalues. After identifying the noisy
bands an inverse MNF can be run on a spectral subset including
just the good bands. Figures 4 and 5 show the
eigenvalues/eigenvector/linear transformation before and after
the running de-striping code. It is evident that at least one of
the linear combinations has gotten better after the de-striping
pre-processing of the three bands (B183-185) as shown by a
decrease in the seventh eigenvalue. Here, the eigenvalues or
eigenvectors are the result of linear combinations. In fact
Figures 4 and 5 are also showing that one of the linear
combinations (the first) is containing the most statistic
information. In other words, one “new band” by linear
combination through MNF transform has represented a large
portion of the information containing originally in the nine
bands. It is common that hyperspectral bands are correlated
each other introducing redundancy and MNF is in fact a way to
deal with this difficulty by reducing dimensionality.
20

Before De-striping
Eigenvalue
15
After De-striping
10
5

8
9
Eigenvalue Number
Figure 4. Eigenvalues for nine different linear combinations.
Figure 5. Image representing three of the linear combinations
before and after the de-striping.
12
The second main step in pre-processing was the conversion and
correction needed for the index calculation. Most of the crop
residues
models and indexes are built on reflectance units. Therefore,
digital number (DN) needed to be converted into radiance and

then into
2
reflectance. The conversion to radiance [ W / (m sr µ) ] for
Hyperion data was achieved by dividing the VNIR 50 bands
(B8-57) by the scalar 40 and the SWIR bands (B77-224) by 80.
Then, the conversion from
radiance into reflectance in order to normalize for the incoming
radiation was done in FLAASH model.
ABCD
Figure 6.Hyperion RGB images for average bands (Red: B183-
184, Green: B195-197 and Blue: B204-206) in digital number
(A), radiance (B) (units= W·sr-1·m-2), reflectance from
FLAASH without any aerosol/water corrections (C) and
reflectance from FLAASH with both corrections aerosol and

water retrieval by absorption band 1.1 μm (D).
Fast Line-of-sight Atmospheric Analysis of Spectral
Hypercubes (FLAASH) is an atmospheric correction modeling
tool in ENVI 4.3 for retrieving spectral reflectance from
hyperspectral radiance images. FLAASH incorporates the
MODTRAN4 radiation transfer model to compensate for
atmospheric effects. FLAASH is capable of doing aerosol
correction as well as H2O vapor corrections.
We run both aerosol correction and water vapor correction. For
H2O vapor corrections, the model has the choice of three
different water absorption bands/features: 1.1, 0.9 and 0.8 μm. .
We evaluated the first and the third features in Hyperion data
resulting in better scenes and lower water retrieval in the
feature 1.1 μm. The water retrieval in water absorption bands
770 - 870 nm resulted in 0.227 cm, while in the band 1050 -
1210 nm water retrieval was 0.120 cm. Both results can be
compared to FLAASH output without application of water
retrieval detected: 1.302 cm H2O vapor in atmosphere of scene.
In other words, FLAASH detected a total of 1.302 cm of water
average in the atmosphere of the scene, and then the two
absorption bands were able to drastically reduce the water
vapor; however, the 1.1 μm features was more efficient to
remove water vapor, so we keep using it for our correspondent
analysis. The image was taken in a no cloudy day; however,
FLAASH made it slightly better; besides it was an interesting
exercise to do. The best way to correct from radiance to
reflectance would be to set spot on the ground with different
material (white paper, water, etc...) right in the moment when
the airborne or spaceborne sensor is doing the readings, and

then do the correction based on this data. It will also help to do
atmospheric correction.
14
7. DATA PROCESSING: INDEX CALCULATION
It is important to understand how CAI works. The Cellulose
Absorption Index is a result from absorption of the plant
macromolecules that are cellulose (≈ 40 %) and lignin (6 – 14
%). Those plant parts exhibit absorption at around 2100 nm (as
Figure 7 shows); therefore, most crop residues on soil surface
also show this absorption feature. This index needs as input 30
nm-width spectral bands in reflectance (R). Those three bands
are centered at 2015 nm (R2.0), 2106 nm (R2.1) and 2195 nm
(R2.2). Because of the spectral resolution These 30 nm bands
for CAI are averages over three Hyperion bands each one for
R2.0: B183, B184, B185*; R2.1: B195, B196, B197; and R2.2:
B204, B205, B206. Note*: in this project Hyperion band B185
was excluded because of severe striping and it was very noisy.
The average can be done in Band Math by given more weight to
the central bands as follow: Average Reflectance =
0.25*B1+0.50*B2+0.25*B3. Then CAI can be calculated with

λ (μm)2.02.12.2
bare soil
residue coverReflectance
Figure 7. Reflectance at bands in the Cellulose Absorption
Index.
The CAI uses the bands at 2000 nm and 2200 nm in order to
normalize the 2100 nm wavelength absorption feature. As
Figure 7
15
shows when the 2100 nm feature gets deeper, then it implies
more absorption (in other words less reflectance is happening),
more crop residues are present, and as a result one obtains
higher index.

Figure 8. Cellulose Absorption Index image for central Tipton
County.
Image data results from the CAI (as a single band) for the
selected agricultural areas (central Tipton County) shows
greater abundance of crop residues as bright areas, and bare soil
as dark areas as can be seen in Figure 8. There are also mixing
pixel with intermediate colors. This can later on be classified

into conservative and conventional tillage and crop residues
management practices of the agricultural fields. The index
image also shows the two cities (Tipton and Windfall) and some
winter crops (C3 grasses and/or winter wheat) as bright areas
16
because of the presence of trees and growing vegetation in both
of them; therefore cellulose absorption would be present.
After running the CAI in the selected Hyperion data subset for
central Tipton County (without band 185), the next step was to
register the image to the panchromatic Landsat subset (15 m
spatial resolution) as shown in Figure 8. This was accomplished
by 20 ground control points and a RMS error of 0.511m.
7. RESULTS
Unsupervised classification by ISODATA algorithm was
performed on CAI image (previous Figure 8), and then crop
residues classes were assigned as crop management classes.
Three classification classes were assigned: one with less than

30% crop residues, conservation management, one with more
than 30% crop residues, conventional management, and a class
with other areas which corresponded to urban areas of the two
cities, small houses farms scattered in the image, infrastructure,
roadways and growing crops. The result of ISODATA is
presented in Figure 9.
Figure 9. ISODATA classification on CAI image for central
Tipton County with Tillage systems classes

17
Then the areas corresponding to the two tillage classes were
extracted from ISODATA statistic output and finally normalized
to 1. By comparing the classification results, table 1, with data
from the Crop Residues Management Survey
(www.conservationinformation.org) could a statistical
comparing be performed by a Z- test, z ≈ (df: 1) = 7.56, P-value
< 0.05. This showed that it were significant so both sources
behave differently. However, in numerical terms, the data
behave consistently similar for the tillage systems. The
difference is just 3 %. It is very likely that the test statistic is
not powerful enough to detect differences (z-test is a
conservative statistic test), specially when having such limited
amount of degree of freedom. In this case, we different just two
classes conservative and conventional tillage; however, more
classes can be differentiated, and then more detail and more
powerful test might identify conclusive similarity.
Table 1. Classification results vs. Crop Management Survey
field data.
Group /
CRM

CAI ISODATA
Category
Survey
Classification
No tillage
0.373
0.338
Tillage
0.627
0.662
Agricultural land
1.000
1.000

In addition to the previous analysis it was explored supervised
classification by minimum distance on CAI image and maximum
likelihood in ALI image. It is relevant to clarify that Envy 4.2
software requests two bands to perform a parametric supervised
classification by
maximum likelihood as well as mahalanobis needs two or more
bands, while the calculated index corresponds to a single band.
Minimum distance can be performed in a single band; however,
results were consistently poor as can be seen in Figure 10. Even
after trying several times using different training sets. Minimum
distance had an overall accuracy of = 90.8 % (1241/1366) and
Kappa Coefficient = 0.82, which are good values; however, it
totally confused the urban areas and the growing crops with no
tillage areas yielding a bias result of 69 % of surface no tillage
systems and 31 % under tillage systems. These results are
totally different than field data collected during the Crop
Residues Management Survey. Minimum distance classifier is
way to simple to do a good job in this case, so we disqualified
this classification result.

Tilled
No-tilled
Other areas
Figure 9. Minimum distance classification on CAI image for
central Tipton County with Tillage systems classes

19
Because of the impediment of the Envy 4.2 software (requesting
two or more bands to perform maximum likelihood or
mahalanobis), we decided to use ALI image with spectral bands
5 & 6 (average), 4 and 3 as RGB (as shown in Figure 2) to
perform classification by maximum likelihood with training sets
for three classes and 1100 to 1200 pixels per class. Results were
better than in minimum distance because cities were
discriminated as well as growing crop. Maximum likelihood had
an overall accuracy of = 98.8 % (3440/3480) and Kappa
Coefficient = 0.98, which are good values; however, it
underestimate the field under conventional tillage with 43.3 %,
while the no tillage or conservational tillage system was 57 %.
These values are closer than minimum distance on CAI image;
however, not closed to the Crop Residues Management Survey.
One has to remember that this classification was done not on the
CAI image but in ALI RGB image. Even so, some agricultural
fields were clearly identified by this classification.

Tilled
No-tilled
Other areas
Figure 10.Maximum likelihood classification on ALI RGB
image for central Tipton County with Tillage systems classes
20
8. ISSUES AND LEARNING
During this project have we had several issues and difficulties
but managed and learn a lot from each. The first difficulties

were to destripe the Hyperion data to get rid of as many bad
columns/stripes and noise as possible. Also the conversion from
Digital Numbers, DN, to radiance and reflectance by the
program FLAASH were not easy managed.
We have also found some limitations for the index, which are:
living plants have also cellulose/lignin that will affect the
results. Also the soil moisture content will change over time
also that different kind of soils reacts in different ways by soil
moisture content.
During this project have we used a number of software and that
include matlab for the destriping code, Minimum Noise Fraction
(MNF), FLAASH, band math, ISODATA algorithm and the
Cellulose Absorption Index (CAI) and also ENVI as the
program we have been working in.
9. CONCLUSIONS AND FUTURE WORKS
The results from this study can we draw the conclusion that the
Cellulose Absorption Index is a very powerful tool for mapping
crop residues with hyperspectral data. The results we performed
could not statistically agree with field data at County level;
however, in numerical they behaved very similar with a
difference of just 3 %.

21
We would like to see future work in some
comparisons/interpretations between hyperspectral data and
multispectral data. Also to compare multispectral data indexes
(Landsat and ALI) with hyperspectral indexes. Some indexes
that could be interesting to investigate in can be Normalized
Differential Index (NDVI), and Normalized Differential
Senecent Index (NDSVI). One step can also be to perform a
detailed supervised classification with more classes, and
compare different algorithms for example Spectral Angle
Mapper (SAM), Cosines angle, Maximum Liklehood, Minimum
distance and Mahalanobis algorithms.
A project with multitemporal studies looking at fresh surface
residues and how the decomposition rate changes with time
would also be very interesting.

22
ANNEX 1. CORRECTED BANDS AND COLUMNS
Band 1 183 OK
[24 36 54 72 112 116 120 124 128 160 180 184 228 240]
[37 55 73 113 117 121 129 161 181 185 229 241] Band 2 184
OK
[2 13 24 40 51 60 69 72 88 90 93 102 104 112 123 126 134 144

152 159
165 176 183 190 196 204 206 216 228 230 240 248 253]
[3 25 61 73 113 124 127 145 153 166 184 185 191 197 198 207
217 229
231 241 249 254]
[114 154 186 199 232 242]
[115 116 117 155 156 157 187 121 122]
Band 3 185 OK
[2 7 12 22 24 32 36 40 52 55 60 62 76 80 84 90 100 112 116
130 134
152 156 170 172 182 187 196 206 216 222 230 234 240 246
252]
[3 13 25 33 37 41 53 56 77 81 85 91 95 101 113 117 131 135
153 157
173 183 188 197 207 217 223 231 235 241 253]
[102]
[103]
[22 94 112 122 180 194 200 246 226]
[23 113 123 195 201 247 227]
[181 196 202 248 228]
[182 203 249 229]
[63 124 172 186 210 246]
[125 211 247]
85 212
86 213
220
87

23
ANNEX 2. COPY OF IN-RUNNING MATLAB CODE FOR DE-
STRIPPING
>> cor_str
File to De-stripe is:
OriginalB9s
Info file is:
B9.info.txt
Methods to correct streaks

[1] Additive
Please wait: creating destriped file.
if you left any band, you can run this code again.
It will not affect those bands that have been corrected
Bands that need to be corrected: put them into like [ 1 2 ], (0 for
all) [3]
Enter your sliding window size, the default window size is 32
pixels
32
Band 3:
Steaked columns: put them into like [ 1 2 ], (0 for all)[12 57
162]
Band 3: 12 57 162
processing column 12
>>

24
8. REFERENCES
Martin, A. 2002. Detection of Crop Residues with four tillage
systems using Remote Sensing Techniques. MS Thesis, Purdue
University. 115 p
Bannari A, A. Pacheco, K. Staenz, H. McNairn, K. Omari.
Estimating and mapping crop residues cover on agricultural
lands using hyperspectral and IKONOS data. Remote Sensing of
Environment 104 (2006) 447–459
Biard F, Baret F. Crop Residue Estimation Multiband
Reflectance Using Multiband Reflectance. Remote Sens.
Environ. 59:530-536 (1997)
Daughtry, C; J. E. McMurtrey III, M. S. Kim, E. W. Chappelle.
Estimating Crop Residue Fluorescence Imaging Cover by Blue.

Remote Sens. Environ. 60:14-21 (1997)
Daughtry, C., E.R. Hunt Jr., J.E. McMurtrey III. Assessing crop
residue cover using shortwave infrared reflectance. Remote
Sensing of Environment 90 (2004) 126–134
Daughtry C., P.C. Doraiswamy a, E.R. Hunt Jr.a, A.J. Stern a,
J.E. McMurtrey IIIa, J.H. Prueger. Remote sensing of crop
residue cover and soil tillage intensity. Soil & Tillage Research
91 (2006) 101–108
Lal, R. 2002. Soil carbon dynamics in cropland and rangeland
Environmental Pollution 116:353–362
Lal, R. 2004. Soil carbon sequestration impacts on global
climate change and food security. Science 304:1623-1627.
McNairna, H.; C. Duguayb, B. Briscoc, T.J. Pultz. The effect of
soil and crop residue characteristics on polarimetric radar
response. Remote Sensing of Environment 80 (2002) 308– 320
Nagler, P. L., Y. Inoue,1, E.P. Glenn, A.L. Russ,2, C.S.T.
Daughtry, Cellulose absorption index (CAI) to quantify mixed
soil–plant litter scenes Remote Sensing of Environment 87
(2003) 310–325
25

South, S, Jiaguo Qi, David P. Lusch. Optimal classification
methods for mapping agricultural tillage practices. Remote
Sensing of Environment 91 (2004) 90–97
Su, H.; M. D. Ransom, E. T. Kanemasu. Simulating wheat crop
residue reflectance with the SAIL model. Int. J. Remote
Sensing, 1997, vol. 18, no. 10, 2261 - 2267
Thomas J. Jackson Peggy E. O'Neill Remote Sensing of
Environment. Microwave emission and crop residues. Volume
36, Issue 2 , May 1991, Pages 129-136
West, T; Post, W. 2002. Soil organic carbon sequestration rates
by tillage and crop rotation: a global data analysis. Soc. Am. J.
66:1930– 1946.

27
Using Landsat 8 Images for Estimating Crop Harvest Progress
Introduction

Gathering agronomic information from remotely sensed data is
not a new concept. For
decades, efforts to mine information about crop conditions have
been increasing as advancing
technology enables higher quality data to be collected. Badhwar
(1984) developed an automatic
unsupervised classification method to estimate the proportions
of agricultural land in three crop
categories: corn, soybean, and other. The method takes
advantage of the known signatures for
corn and soybeans; specifically the higher ⍴ max of soybeans.
The data were refined to determine
the thresholds for each class. A linear classifier, Ho-Kashyap
algorithm, then used the criterion
function to separate each pixel into one of the classes. This
methodology was developed and
tested using Landsat 5 data over the United States over three
years. It was determined to provide
unbiased estimates of the crop proportions. The method
performed best when the corn proportion
was between 25-50% and struggled with pixels on the borders of
fields. Conese and Maselli
(1990) utilized multitemporal information to improve the
classification of three different Landsat

images. 150 ground reference points were used to determine ten
classes to be used when
classifying the entire image. A maximum likelihood classifier
was used on each individual image
and also on the multitemporal set. The error probabilities were
then estimated using the error
matrix. This information was used for the modified maximum
likelihood classifier and the
process was repeated. Using this approach increased the Kappa
coefficient by 0.09 when
compared to the multitemporal classification. Bruzzone et al
(1997) used a supervised
nonparametric approach to identify land-cover changes over
time. This technique made it
possible to detect changes without classifying each individual
scene. This method also provided
better accuracy than traditional post classification methods.
Training pixels were developed
using ground control points where the ground truth was known
at both time points. A Bayesian
approach was used to determine the probabilities of a land-
cover transition. The accuracy was

assessed using Kappa coefficients and it was determined this
approach was more accurate than a
post classification approach, 0.86 and 0.67 respectively.
Each season, the USDA issues weekly crop progress reports.
These reports give
important details of the progression of the crops growing in the
field. National Agricultural
Statistics Service (NASS) sends questionnaires to 4,000 USDA
extension and FSA agents each
week. The data from these questionnaires are compiled to create
county, state, and national
reports. One of these reports is the harvest progress report. This
report gives the percent of the
crop that has been harvested in each state. These reports are
widely used and can affect the
commodity market prices. In 2013, these reports were
interrupted by the government shutdown
in October. Crop progress reports were not issued for the weeks
of October 7 and October 15.
Some information from those missed reports have been
interpolated; however, the use of satellite
images from those time points could rectify the data from that
time period.

Materials and Methods
Data from Landsat 8 acquired over Southern Indiana during the
months of September,
October, and November 2013 were selected for analysis. Data
were downloaded from USGS
Earth Explorer. The temporal spacing of these images is
important when estimating regional
harvest progress. Because of this, 30 day intervals were
selected. The acquisition dates of the
data selected were September 6, October 8, and November 9,
2013 (Figure 1). The scenes for
each month were chosen based on the quality of the data.
Images with minimal cloud cover were
selected. Since cloud cover is common in the Midwest during
the fall months, the amount of data
available was quickly limited.
A spatial subset of the images was taken to reduce the scenes to
a smaller area of interest.
Decatur County, Indiana was chosen because of its prolific crop
production. There is a large
amount of cropland located in Decatur County in relation to
urban areas. The spatial subset was

achieved by setting the extent to that of our National
Agricultural Statistics Service Cropland
Data Layer (downloaded for Decatur County) at the time of
performing radiometric calibration.
By subsetting the data spatially, computation time was
significantly reduced through all steps of
preprocessing the image data.
ENVI v5.1 was used to calibrate the three scenes. The data was
converted from digital
numbers to reflectance values using the radiometric calibration
tool available in ENVI. This tool
uses the multispectral metadata file to determine the correct
values to use when converting from
radiance to reflectance. Figure 2 shows screen captures of the
settings used for these radiometric
calibrations.
Figure 1. Landsat 8 data acquired for the months of September
(left), October (middle), and November
(right).
Atmospheric calibration was also completed using ENVI v5.1.
This calibration was

achieved using the dark pixel subtraction method. This method
uses the assumption that the
darkest pixels in the image should have a reflectance value of
zero and any difference is assumed
to be the effect of atmospheric scattering. The darkest pixels are
then adjusted to zero. The rest
of the image is then adjusted by the same factor. Figure 3 shows
the settings used in the ENVI
Dark Subtraction tool to achieve this correction.
Figure 3: Screen captures of settings used for radiometric
calibrations
Figure 2: Screenshot of settings used for darkest pixel
subtraction and results for
September image.
After these calibrations were complete, the three scenes were
co-registered. The
registration was achieved using ERDAS Imagine 2014. Ground
control points (GCPs) were
selected near the perimeter of the image and were uniformly
distributed. Five GCPs were used in
each image. Objects chosen as GCPs included building corners,
grain bins, and road

intersections. The scene acquired in November was used as the
reference image and the other
scenes were registered to that image. Figure 4 shows the GCPs
used for registration; the total
RMS was 0.28.
At this point, the data was prepared for classification.
Supervised classification was
preferred over unsupervised classification for the added control
in making adjustments and fine-
tuning the classification. The NASS Cropland Data Layer
(CDL) was used to identify known
soybean and corn fields. The CDL is a raster product made by
the USDA NASS which shows
Figure 4: Image registration. The September image (right) was
registered to the
November image (left). Also shown are the ground control
points and the reported
RMS error.
the type of crops grown across the lower 48 states at a 30-meter
resolution. This dataset was
used to look up whether a given field was soybeans or corn

when training our classifier.
Looking at the three images, it was apparent that the changes
that occur over the course
of the harvest season are substantial. The majority of the image
in September indicates healthy,
live crops that are green. In the October image, much of the
crop has senesced and is darker
brown in color. Finally, in November, most of the crop appears
a gray or tan color which
indicates that it has been harvested. Because of these temporal
effects, classification could not
be performed by using common spectral signatures across all
three images; it was necessary to
treat each image as a separate classification problem.
Beginning in September with no identifiable fields where the
crop had been harvested, 25
area of interest (AOI) polygons were drawn for soybeans field
and another 25 for corn fields. An
additional 25 area of interest polygons were drawn for urban
and forest classes to account for
non-cropland areas that should be excluded from harvest
progress calculations. Although it was
not important to distinguish between forest and urban areas for
the purpose of estimating harvest

progress, their spectral signatures are dissimilar; taking the
mean of the two classes by merging
them would result in a poor classification. The AOI polygons
were used to add spectral
signatures of each class through the Spectral Signature Editor.
Then, for each of the four classes
(soybean, corn, forest, urban), all of the signatures of that given
class were merged together to
generate a single spectral signature class used in the
classification. Classification was performed
with the ERDAS Supervised Classification tool using the
maximum likelihood method and
defaults for the other settings.
For the next image in October, the 75 total area-of-interest
polygons were reused from
the September image where the possible classes were now
standing corn, standing soybeans,
harvested corn, harvested soybeans, forest, and urban. While
the NASS CDL was used to
distinguish corn and soybean fields from one another, no data
was collected to ground truth
fields that had been harvested. However, it is relatively

straightforward to determine those fields
that had been harvested from the imagery by eye; harvested
fields have a light tan or gray
appearance while senesced, standing crops are a dark brown
color.
However, when training the classifier for the AOIs in the
November image, it was
observed that some fields had changed from a tan harvested
color in October back to a dark
brown color. This is likely attributed to tillage that often takes
place in the fall where the post-
harvest residue is worked down into the soil, exposing the moist
soil below which will produce a
dark brown appearance. Additionally, other fields appeared to
have new, green vegetation
growth; this is likely cover crops or weeds that had grown after
harvest took place and further
complicate classification.
Initially, it was observed that the classification struggled with
marginal fields that were
adjacent to forested areas along rivers and streams. Because the
25 AOI polygons used for a
given crop were merged into a single class where the mean
reflectance value is taken, these

marginal fields were far enough from the mean that they were
more closely related to the forest
class. To resolve this, additional classes were created to
capture these marginal soybean and
corn fields. Five AOIs were used for each of these two classes.
Results and Discussion
The classification results for each image were compared to
determine how well the
classifier distinguished corn from soybeans, and then how well
it distinguished standing from
harvested crops. Figure 5 shows the classification of corn,
soybeans, forest, and urban areas.
Overall, the classification appears to be accurate. In September,
fewer pixels were classified as
corn compared to the other months. In November, pixels
previously classified as soybeans in
September and October were being misclassified as corn. Table
1 shows the number of pixels
assigned to each crop class for each month. These values were
converted to acres based on the
30-meter pixel resolution. These estimates were then compared

to the NASS county estimates for
acres planted of each crop in Decatur County in 2013. The
calculated acres were expected to be
higher than the reported acres because the area classified was
approximately 20% larger than
Decatur County. According to these results, October was the
most accurate classification result
for corn, with acres being underestimated in September and
overestimated in November. The
results for soybeans were accurate and consistent for September
and October, but were
underestimated in November.
Figure 5: Classification results. September 6, 2013 (left),
October 8, 2013 (middle), and November 9,
2013 (right). Pixels classified as corn are green, pixels
classified as soybeans are red, and pixels
classified as urban or forest are black.
When looking at the classification of harvested and standing
crops for each month, the
expected trend is apparent. Figure 6 shows the classification

results of harvest progress of corn
over the three months while figure 7 shows the results for
soybeans. Table 2 compares the results
of the classification to the USDA reports available for 2013. For
October, when reports were not
generated due to the government shutdown, 2011 estimates were
used for comparison. The
results for September and October are very similar to the
progress reported by the USDA. The
results for November are very similar for soybeans, but appear
to be overestimating the corn
harvest progress.
Figure 6: Classification results for standing corn (green) and
harvested corn (yellow) for September,
October, and November (left to right).
Table 1: Comparison of classification results to the 2013 USDA
planting report.
* Area classified is larger than area reported
**2013 Decatur County NASS county
estimates

The results from September showed pixels that should have
been classified as cropland
were being misclassified as urban and forest. This resulted
because of the marginal acres near
forests and rivers, where end of season senescence was not
uniform across the entire field. To
solve this issue, more classes were added to capture the spectral
signatures of these areas. Figure
8 shows the improved classification of an area identified as
difficult to classify when these
additional classes were added.
Figure 7: Classification results for standing soybeans (red) and
harvested soybeans (yellow) for
September, October, and November (left to right).
Table 2: Comparison of monthly classification results to USDA
harvest reports.
In November, the classification results were affected by the
number of different
management practices occurring in the field. Tillage operations
and cover crops caused some
fields to be misclassified. Some pixels that had previously been
classified as soybeans were

classified as corn. Also some fields that had been classified as
harvested in October were
classified as standing in November. Figure 9 shows an area
identified as difficult to classify in
November where several soybean fields were misclassified in
November.
Figure 8: Comparison of classification results for September
(original image, right) when single classes were
used for each crop (middle) and when additional classes were
added (right). Corn pixels are green, soybeans
are red, and urban/forest pixels are shown in black.
Figure 9: Comparison of classification results for October (left)
and November (right). Standing
soybeans are maroon, harvested soybeans are orange, standing
corn is dark green, and harvested
corn is light green, and urban/forested areas are black.
Conclusions
Of the three months classified, October was determined to be
the most accurate.

Classification becomes difficult when crop senescence varies
within fields and also when late
season management practices are occurring. In order to further
improve classification for the
purpose of tracking harvest progress, a more robust
methodology is proposed. Identify the time
point when the separability of corn and soybeans is highest and
use an image acquired near this
time point to create a base classification. Use the results from
this classification to create a mask
for each crop of interest. This mask should then be used to
isolate the pixels determined to be
corn and soybeans for subsequent classifications. This method
will provide an accurate and
stable estimate of acres and will alleviate the error associated
with management practices late in
the season.
A decision tree system, similar to how crops are classified in
the NASS CDL, would also
aid in the multi-temporal, multi-image aspects of this
classification problem. For example, a
decision tree might limit the possible classifications to
“harvested,” “cover crop,” or “tilled, bare

earth” following a classification of “senesced.” Similarly, the
decision tree might prohibit a
classification of “standing” or “senesced” following a
classification as “harvested.” These
techniques would prevent some of these types of problems
experienced in this study.
References
Badhwar,G.D., Automatic corn-soybean classification using
landsat MSS data. I.
Near-harvest crop proportion estimation, Remote Sensing of
Environment, Volume 14,
Issues 1–3, January 1984, Pages 15-29, ISSN 0034-4257,
http://dx.doi.org/10.1016/0034-
4257(84)90004-X.

Bruzzone, L.; Serpico, S.B., "An iterative technique for the
detection of land-cover
transitions in multitemporal remote-sensing images,"
Geoscience and Remote Sensing,
IEEE Transactions on , vol.35, no.4, pp.858,867, Jul 1997.
Conese, Claudio; Maselli, Fabio, Use of multitemporal
information to improve
classification performance of TM scenes in complex terrain,
ISPRS Journal of
Photogrammetry and Remote Sensing, Volume 46, Issue 4,
August 1991, Pages
187-197, ISSN 0924-2716, http://dx.doi.org/10.1016/0924-
2716(91)90052-W.
ERDAS IMAGINE. 2014. Atlanta, Georgia: ERDAS, Inc.
Exelis Visual Information
Solution
s. 2013. Boulder, Colorado: Exelis
Visual Information

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