To detect long-term climate trends, it is essential to produce long-term and consistent data sets from a variety of different satellite platforms. With current global cloud climatology data sets, such as the International Satellite Cloud Climatology Experiment (ISCCP) or CLAVR (Clouds from Advanced Very High Resolution Radiometer), one of the first processing steps is to determine whether an imager pixel is obstructed between the satellite and the surface, i.e., determine a cloud 'mask.' A cloud mask is essential to studies monitoring changes over ocean, land, or snow-covered surfaces. As part of the Earth Observing System (EOS) program, a series of platforms will be flown beginning in 1997 with the Tropical Rainfall Measurement Mission (TRMM) and subsequently the EOS-AM and EOS-PM platforms in following years. The cloud imager on TRMM is the Visible/Infrared Sensor (VIRS), while the Moderate Resolution Imaging Spectroradiometer (MODIS) is the imager on the EOS platforms. To be useful for long term studies, a cloud masking algorithm should produce consistent results between existing (AVHRR) data, and future VIRS and MODIS data. The present work outlines both existing and proposed approaches to detecting cloud using multispectral narrowband radiance data. Clouds generally are characterized by higher albedos and lower temperatures than the underlying surface. However, there are numerous conditions when this characterization is inappropriate, most notably over snow and ice of the cloud types, cirrus, stratocumulus and cumulus are the most difficult to detect. Other problems arise when analyzing data from sun-glint areas over oceans or lakes over deserts or over regions containing numerous fires and smoke. The cloud mask effort builds upon operational experience of several groups that will now be discussed

Baum, B. A.

Berendes, T. A.

Schaaf, C.

Trepte, Q.

Welch, R. M.

English

NASA Technical Reports Server

NASA-CR-204862
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Re_'inted trom _e pfept_nt volume of file Elghttl
Conference on Sate/life Meteorolog), and
Oceanography, 28 Jan - 2 Feb "i996, Atianta. GA by
the AMS. Boston, MA
THE EOS CERES GLOBAL CLOUD MASK
T. A. Bcrcndcs" and R. M. Welch
Institute of Atmospheric Scict_ccs
Soulh Dakota School of Mmcs and Tcchnolog3
Rapid CiD,. SD 577OI-3U95
Q Tropic
/.'
Lockheed Engineering and Sciences Corporation
Hampton VA 23666.
C. Schaaf
Air Force Geoph.,,sics Laboraton
H,mscom AFB. MA O 1731
BA Baum
NASA Langley Rcsearch Center
Hampton, VA 23681-o0o 1.
I. INTRODUCTION
To detect long-term climate trends, it is es-
sential to produce long-term and cotlsistcnt data
sets from a variety of different satellite platforms
With current global cloud climatology data sets.
such as the International Satellite Cloud Clima-
tology Experiment (ISCCP) or CLAVR (Clouds
from Advanced Vet3., High Resolution Radiome-
ter), one of the first processing steps is to deter-
mine whether an imager pixel is obstructed be-
tween the satellite and the surface, i.e., determine
a cloud "mask." A cloud mask is essential to
studies monitoring changes over ocean, hind, or
snow-covered surfaces. As part of the Earth Ob-
serving System (EOS) program, a series of plat-
forms will be flown beginning in 1997 with the
Tropical Rainfall Measurement Mission (TRMM)
and subsequently the EOS-AM and EOS-PM
platforms in following years. The cloud imagcr
on TRMM is the Visible/Infrared Sensor (VIRS).
while the Moderate Resolution Imaging Spectro-
radiometer 0VIODIS) is the i,nagcr on the EOS
platforms. To be useful for long term sit, dies, a
• Contributing ,4 uthor 's. t tkh'ess. lnslitu tc of At-
mospheric Sciences, South Dakota School of Mines
and Technology, 501 East Saint Joseph Street.
Rapid City, SD 57701-3995.
cloud masking algorithm should produce consis-
tent results bct_ecn existing (AVHRR) data, and
future VIRS and MODIS data. The present work
outlines both existing :rod proposed approaches to
detecting cloud using mtdlispcctral narrm_band
radiance data
Clouds gcncrall) arc characterized by higher
albcdos and lox_cr tcmpcralurcs than the underly-
ing surface. Hox_cvcr. there are numerous condi-
tions when this charactcrizatmn is inappropriate,
most notably over snow.and ice. Of the cloud
types, cirrus, stratocumulus and cumulus are the
most difficult to detect. Other problems arise
v,hen analyzing data from sun-glint areas over
occa_ls or lakes, over deserts, or over regions
containing numerous fires and smoke. The cloud
mask effoFt builds upou operational experience of
several groups Ihat will nox_ be discussed.
2. HERITAGE ALGORITHMS
The CERES cloud masking algorithm (Baum
et al. 1994) ',_ill rcl) heavily upon a rich heritage
of both NASA and NOAA experience with global
470 AMERICANMETEOROLOGICALSOCIETY
https://ntrs.nasa.gov/search.jsp?R=19970025054 2020-06-16T02:19:14+00:00Z
data analysis. Initial algorithm design will incor-
porate the approaches used by ISCCP
(International Satellite Cloud Climatology Proj-
ect) (Rossow and Garder 1993), CLAVR (Clouds
from AVI-IKR) (Stowe et al., 1991), and SER-
CAA (Support of Environmental Requirements
for Cloud Analysis and Archive). The ISCCP
.,algorithms are based upon two channels, one in
the visible wavelength region and one in the in-
frared. The CLAVR approach uses all five chan-
nels of the AVHRR instrument. The CLAVR
multispectral threshold approach incorporates
narrowband channel difference and ratio tests,
including dynamic threshold specification with
clear-sky radiation statistics.
The SERCAA (Gustafson et al. 1994) algo-
rithm is operational at the Phillips Laboratory,
Hanscom Air Force Base, and uses all five
AVHRR radiometric channels. The SERCAA is
sponsored jointly by the Department of Defense,
Department of Energy. and Environmental Pro-
tection Agency Strategic Environmental Research
and Development Program.
The International Satellite Cloud Climatology
Project (ISCCP) cloud masking algorithm is de-
scribed by Rossow (1989, 1993), Rossow et al.
(1989) and Seze and Rossow (1991a). Data are
used from the narro_band VIS (0.6 micron) and
the IR (11 micron) channels. The ISCCP algo-
rithm is based on the premise that the observed
VIS and IR radiances are caused by only two
t_ges of conditions, 'cloudy' and 'clear', and that
the ranges of radiances and their variability that
are associated with these two conditions do not
overlap (Rossow and Garder 1993). As a result,
the algorithm is based upon thresholds, where a
pixel is classified as "cloudy" only if at least one
radiance value is distinct from the inferred
"clear" value by an amount larger than the uncer-
tainty in that "clear" value. The uncertainty can
be caused both by measurement errors and by
natural variability. The "threshold" for cloud de-
tection is the magnitude of the uncertainty in the
clear radiance estimates. This algorithm is con-
strutted to be ,cloud-conservative," minimizing
false cloud detections but missing clouds that
resemble clear conditions.
The NOAA CLAVR algorithm (Phase I) uses
all five channels of AVHRR to derive a global
cloud mask (Stowe et al., 1991). It examines
multispoctral information, channel differences,
and spatial differences and then employs a series
of sequential decision tree tests. Cioudfree,
mixed (variable cloud)') and cloud)' regions are
identified for 2x2 GAC pixel arrays. If all four
pixels in the array fail all the cloud tests, then the
array is labeled as cloud-free (0% cloudy); if all
four pixels satisfy just one of the cloud tests, then
the array is labeled as 100% cloudy. If 1 to 3
pixels satisfy a cloud test. then the array is la-
beled as mixed and assigned an arbitrary value of
50% cloud)'. If all four pixels of a mixed or
cloud)' array satisfy a clear-restoral test (required
for snow/ice, ocean specular reflection, and bright
desert surfaces) then the pixel array is re-
classified as "restored-clear" (0% cloudy). The set
of cloud tests is subdivided into da)time ocean
scenes, daytime land scenes, nighttime ocean
scenes and nighttime land scenes. Subsequent
phases of the CLAVR cloud mask, now under
development, will be incorporated as modifica-
tions become available.
SERCAA, Support of Environmental Re-
quirements for a Cloud Analysis and Archive is
the prototype for the US Air Force's new global
cloud analysis model. SERCAA makes use of a
number of algorithms tailored to sensors on both
the polar orbiting and geostationary meteorologi-
cal satellite platforms. The resulting cloud masks
are determined at sensor pixel resolution rather
than a common grid. These algorithms have been
extensively tested at various global locations.
Unfortunately. existing approaches have limi-
tations, notably in detecting cloud shadows, cloud
over snowy- or ice covered surfaces, clouds in sun-
glint areas, fires and smoke from biomass bum-
ing, and dust storms over deserts. An improved
global cloud mask appropriate for the EOS time-
frame is discussed, and examples will be shown
from application of the cloud mask to existing
AVHRR data.
3. NEW METHODOLOGY
Two new classification methods are used in
this study. Both methods use a set of pairwise
decisions to classify samples. Most classification
methods utilize a small number of features due to
their multidimensional nature. For those meth-
ods, it is not feasible to use more than 10-20 fea-
tures.
The feature vector used in this study consists
of 164 spectral and textural and pseudo-tex'tural
features. The spectral features are created from
the original 5 channels of the AVI-IRR data and a
sixth channel which is the reflectance of channel
3. Spectral ratios, differences, arctangents and
various other functions are computed. Grey level
STMCONF.ON SAT.MET.& OCEAN. 471
difference vector (GLDV) textural features are
computed over a 7x7 mask. Pseudo textures are
computed over a 3x3 neighborhood.
The pairwise classifiers select a subset of fea-
tures from the feature vector. The selected fea-
tures are optimal for distinguishing between pairs
of classes. This reduces the size of the final fea-
ture vector to approximately 20 - 30 features.
The final size of the feature vector is determined
by the number of tests performed for each pair of
classes.
3.1 Training
The classifiers were trained using the Satellite
Imagery Visualization System (SIVIS). SIVIS
allows the user to visualize large satellite images
and select samples from the data. Representative
samples of 23 different classes were selected from
over 40 scenes. The scenes are chosen from three
main climate regimes; polar, Middle-Eastern
desert and South American rainforest during the
burning season. A separate classifier is con-
structed for each regime.
3.2 Paired Rule Classifier
The paired rule classifier takes the 164 ele-
ment feature vector and creates a new feature
vector consisting of the original 164 features and
all possible ratios of the form A/B. This results
in a feature vector with 13694 features.
Next, the divergence for each pair of classes is
computed for each feature. For a given feature F,
the divergence between class i and class j is de-
fined as:
DIV(F)_.) = 1m, - mj I / ( s, + sj ),
where m, and mj are the means for classes i and j,
and s, and sj are the standard deviations for those
classes.
A list of features, sorted by decreasing diver-
gence, is coastructed for each pair of classes. In
this study, the 5 features with highest divergence
are chosen for each class pair. A threshold then
is determined for each of the chosen features. For
a given feature F, the threshold T is used as a test
for discriminating between the two classes i and j
as follows:
T= m, + s, ( mj- mi) / (s, + sj ).
If F < T, then the test returns class i, if F > T the
test returns class j. Each class has 5 tests in this
study. A histogram tabulates the number of tests
satisfied by each class. After all tests for each
class have been completed, the class with the
highest histogram count is chosen.
3.3 Paired Histogram Classifier
The paired histogram classifier takes the 164
element feature vector and constructs histograms
for each pair of classes. For feature F, two histo-
grams Iv and Jr are created for classes i and j.
The histogram ranges are scaled to accommodate
the minimum and maximum values of both
classes and discretized into 256 bins. The histo-
gram values for each class are normalized by the
number of samples in the class. These paired
histograms are analyzed and sorted, based upon
overlap and divergence. Overlap, O, is defined as
follows:
256
off),., = Z
x=l
Divergence is defined above.
The three features with the lowest overlap and
highest divergence for each pair of classes are
chosen. For a chosen feature F, the paired histo-
grams lr and Jr are used for discriminating be-
tween class i and j.
The histograms can be used as discriminators
in a variety of ways. The method chosen for this
study considers the histograms as range specifiers
for the class pairs. Each of the 256 bins of the
normalized histograms lr and Jr are compared
and the following rules are applied:
iflr(x) > Jr(x) then if Iv(x) < Jr(x)
It(x) = 1 Iv(x) = 1
Jr(x) = 0 Jr(x) = l
then
where x = 1.256
The paired histograms lr and Jr now represent
ranges in feature F which correspond to classes i
and j respectively. The three features used for
each class pair produce three pairs of histograms
which are used for classification. This process is
repeated for each pairwise combination of classes.
The following procedure is used to classify a
sample. First, calculate the 164 feature vector for
the sample. Next, compute the histogram bin for
472 AMERICANMETEOROLOG£CALSOCIETY
each set of paired histograms. Retrieve the value
at each computed histogram bin and increment a
voting histogram by that value. Finally, examine
the voting histogram and assign the class with
the highest value
4. RESULTS
The paired rule and paired histogram methods
were both used on three areas of interest; polar,
desert, and South America. These areas were
chosen because they are particularly difficult ar-
eas to classify. Results are preliminary, at this
time, but both classifiers are performing well for
all of the scenes analyzed so far. In particular,
the South American classifier is able to detect
smoke from biomass burning. The desert classi-
fier can detect desert, dust storms and some
sunglint areas in the ocean. The polar classifier
is able to differentiate bet_veen clouds and ice.
We are very encouraged by our results with these
difficult classes.
Color photographs of the classification results
will be presented at the conference.
5. FUTURE WORK
The classification algorithms arc undergoing
continuing revision and enhancement. Work is
also continuing on dcvcloping better features for
classification.
The training sample database is constantly
expanding to include more representative sam-
ples of each class and more comprehensive cov-
erage of the earth.
6. ACKNOWLEDGEMENTS
This work was performed as part of the EOS
Clouds and Earth's Radiant Energy, System
(CERES) program. Two of us (Todd A. Berendes
and Ron M. Welch) were supported under Na-
tional Aeronautics and Space Administration
contract NAS 1-19077.
7. REFERENCES
Baum, B. A., R. M. Welch, P. Minnis. L. L.
Stowe, J. A. Coaklcy, Jr, and colleagues,
1994: Imager clear-sky determination and
cloud detection. Clouds and the Earth's Radi-
ant Energy System (CERES) algorithm theo-
retical basis document. Subsystem 4.1. Post-
script version available via the World Wide
Web at http://asd-wwwlarcnasagov/ATBD
/ATBD.html.
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B.T. Pearson, V.D. Jakabhazy, J.S. Belfiore,
A.S. Lisa, 1994: Support of environmental re-
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(SERCAA): Algorithm descriptions.. Techni-
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Neu, T.J., R.G. lsaacs, GB. Gustafson, and J.W.
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Rossow, W. B., 1989: Measuring cloud proper-
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Rosso_v. W. B. and L. C. Garder, 1993: Cloud
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8TMCONF.ON SAT.MET.& OCEAN. 473


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