Nakajima et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0295-9(2019) 6:52

METHODOLOGY

Progress in Earth andPlanetary Science

Open Access

Theoretical basis of the algorithms andearly phase results of the GCOM-C (Shikisai)
SGLI cloud products
Takashi Y. Nakajima1* , Haruma Ishida2, Takashi M. Nagao3, Masahiro Hori4, Husi Letu5, Riko Higuchi6,
Naoya Tamaru6, Naritoshi Imoto6 and Akihiro Yamazaki7

Abstract
This paper discusses the cloud/clear discrimination algorithm (CLAUDIA) and the cloud microphysical properties algorithm(CAPCOM), which are used by the Second-generation GLobal Imager (SGLI) aboard the GCOM-C satellite, launched in
December 2017. Also described are the preliminary results of cloud products’ validation. CLAUDIA was validated bycomparing cloud fractions derived from satellite data against data from whole-sky images captured by ground-based
fisheye cameras. User’s accuracy and producer’s accuracy were mostly high at around 90%, and the resulting overall
accuracy was also high, ranging from 83 to 100% (average of all sites was 90.5%). CLAUDIA has proven to be sufficiently
accurate to apply a cloud mask to measurements and meets the requirements for releasing data for SGLI cloud flag
products (the minimum for a successful GCOM-C mission). CAPCOM was evaluated by comparing cloud properties
obtained by SGLI products against data from MODIS collection 6 products (MOD06). This was done for both ocean andland in the low to middle latitudes (60° N–60° S) from August 22, 2018 to September 14, 2018. The comparison showed
good correlation coefficients for cloud optical thickness, effective particle radius, and cloud-top temperature for water
clouds: 0.88 (0.83), 0.92 (0.88), and 0.94 (0.92) for ocean (land), respectively. CAPCOM data for ice cloud optical thickness
correlated well with data from MODIS products: 0.86 (ocean) and 0.82 (land).
Keywords: GCOM-C, Shikisai, SGLI, Climate change, Cloud properties, Remote sensing, Cloud evolution process, Radiationbudget, CLAUDIA, CAPCOM

Introduction
The Intergovernmental Panel on Climate Change (IPCC)
Assessment Report-5 (AR-5) predicted that the atmospheric temperature will increase in the 2100’s by 0.3 °C –1.7 °C in the RCP2.6 scenario and 2.6 °C – 4.8 °C in the
RCP8.5 scenario, with uncertainties of 0.4 °C and 2.2 °C, respectively. RCP stands for representative concentration
pathway, and 2.6 and 8.5 denote radiative forcings of 2.6
and 8.5 W/m2 in 2100 (IPCC 2013). The large uncertainties
in temperature predictions reflect our lack of knowledge ofcloud properties and related processes such as those of indirect aerosols and this uncertainty will likely drive further
study of such properties. The IPCC also reported that thelargest uncertainties in global atmospheric temperature* Correspondence: nkjm@yoyogi.ycc.u-tokai.ac.jp
1
Research and Information Center, Tokai University, 4-1-1, Kitakaname,
Hiratsuka, Kanagawa 259-1292, Japan
Full list of author information is available at the end of the article

prediction are due to the effects of atmospheric aerosol forcing. Direct effects of aerosols in the atmosphere are bothwarming and cooling, cooling being dominant. But warming is predominant for absorption aerosols such as black
carbon. The indirect effect of aerosols is known as thecloud-adjustment effect.
The injection of aerosols into the atmosphere changes
cloud systems in two ways: by the Twomey effect (Twomey
1977, Twomey et al. 1984), which increases cloud albedo;
and by the Albrecht effect (Albrecht 1989), which increases
the cloud lifetime by increasing the liquid water path
(LWP) in the cloud and thus suppressing precipitation.
Both effects cool the surface temperature by changing theradiation budget. Overall, radiative forcing of cloudadjustment effects is assumed to be − 1.33 to − 0.03 W/m2.
The uncertainty is still large (IPCC 2013), so further investigation is needed. This explains why aerosol and cloud© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

(2019) 6:52

Page 2 of 25

properties have attracted attention as have the transformation of aerosols to rain in clouds and the energy budget.
There are many methods for determining cloud properties: ground-based observatories, aircraft, and satellites for
instance. They all have various trade-offs to consider.
Ground observatories and aircraft observations are poor
for understanding the synoptic scale of atmospheric phenomena, such as aerosols and clouds. Satellite observationis the best method for observing aerosols and clouds on
the synoptic to global scale.
Satellite observations provide data via remote sensing
rather than direct measurement, adding further uncertainty in the retrieved physical parameters. But data ofhigher accuracy can be obtained through better calibrations and validation. One of the best tools is the geostationary meteorological satellite, which can take frequent
observations, although three or more satellites are required to cover the globe. Another way to make fullscale observations is by using polar orbiting satellites. A
single one of these satellites can cover the entire globe.
The clarity of observations is an advantage too, although
2 or 3 days are required for full coverage.
One such polar orbiting sensor is the Second-generation
GLobal Imager (SGLI) aboard the Global Change Observation Mission-Climate (GCOM-C, “Shikisai” in Japanese),
which was launched on December 23, 2017 (Fig. 1). It wasconceived, designed, and developed specifically for this purpose. The SGLI has 19 channels covering the ultraviolet tothermal infrared spectrum and is a refinement of the sensors
used in the GLobal Imager (GLI) aboard the Advanced Earth
Observing Satellite II (ADEOS II, “Midori-II” in Japanese),
which was launched in 2002 (Nakajima et al. 1998). Table 1
summarizes the channel specifications of the SGLI. The
SGLI has some advantages over visible-to-infrared imagers
on other polar orbiters and geostationary meteorological satellites. First, the SGLI’s channels have a 250-m resolution

which is optimal for ground and ocean imagery. Second, it
has two polarization and bidirectional channels in the visible(673.5 nm) and near-infrared (868.5 nm) regions, which are
effective in making measurements of aerosols over land. The
380-nm channel is also useful in monitoring aerosols overland because aerosol reflectance is low at these wavelengths(Nakajima et al. 2005).
This paper focuses on cloud observations using the SGLI.
The SGLI atmospheric group has developed two algorithms
for obtaining information about clouds. One discriminates
between clouds and clear skies and is called the CLoud andAerosol Unbiased Decision Intellectual Algorithm, or
CLAUDIA. The other determines the microphysical properties of a cloudy area and is called the Comprehensive
Analysis Program for Cloud Optical Measurement, CAPCOM. Table 1 summarizes the SGLI channels used bythese algorithms (the two right-most columns). CLAUDIA
uses the VN8, VN11, SW1, SW2, SW3, T1, and T2 channels; CAPCOM uses the SW1, SW3, SW4, and T1 channels. Here, VN, SW, and T indicate visible-to-near-infrared,
shortwave, and thermal wavelengths, respectively.
The next section will explain the theoretical basis of thealgorithms used by CLAUDIA and CAPCOM. The “Resultsand Discussion” section reports preliminary validation data
for SGLI cloud products. Cloudy/clear discrimination was
validated by data obtained from ground-based whole-sky
cameras. For cloud property validation, the paper compares
the SGLI output with that of similar sensors, such as theMODerate resolution Imaging Spectroradiometer (MODIS).
The final section summarizes this paper.

Nakajima et al. Progress in Earth and Planetary Science

Methods/Experimental
Cloud/clear discrimination algorithm, CLAUDIA

SGLI data analysis needs to discriminate between cloudy
and clear pixels before it can determine cloud and aerosol
properties, so correct discrimination is required for valid,Fig. 1 GCOM-C satellite launch on the H-2A rocket from Tanegashima Space Center, Japan, on December 23, 2017

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 3 of 25

Table 1 GCOM-C SGLI channel specifications and channel use by CLAUDIA and CAPCOM. Lmax denotes maximum observed
radiance for each channel
ChannelCenter WavelengthBand width

VNR, SWI: nm

VNR-SWI:
W/m2/sr/μm
TIR:Kelvin

TIR: μm
VNR: Non -Polarization

VNR: Polarization

SWI

TIR

Lmax

VN1

380

10

210

VN2

412

10

250

VN3

443

10

400

VN4

490

10

120

VN5

530

20

350

VN6

565

20

90

VN7

673.5

20

62

VN8

673.5

20

210

VN9

763

12

350

VN10

868.5

20

30

VN11

868.5

20

300

P1

673.5

20

250

P2

868.5

20

300

Spatial Resolution
(m)

✓

✓
1000

SW1

1050

20

248

1380

20

103

SW3

1630

200

50

250

SW4

2210

50

20

1000
250

T1

10.8

0.7

340

12.0

0.7

340

efficient retrievals. The SGLI has several channels that are
effective in cloud/clear discrimination via an algorithm
based on radiance intensities from the visible to infrared.
CLAUDIA, a cloud/clear discrimination algorithm, wasdeveloped by Ishida and Nakajima (2009) for visible-toinfrared imagers and introduced the clear confidence level(CCL), as does the algorithm for the MODIS cloudmasking product, MOD35 (Ackerman et al. 1998), but it
is designed to perform unbiased discrimination. Nakajima
et al. (2011) reported the effectiveness of CLAUDIA in detecting clouds compared to the specifications of sensors
used in MODIS, the SGLI, the multi-spectral imager(MSI) on the Earth Cloud, the Aerosol and Radiation
Explorer (EarthCARE) satellite, the Advanced Very High
Resolution Radiometer (AVHRR, model 2), and the Cloudand Aerosol Imager (CAI) on the Greenhouse Gases
Observing Satellite (GOSAT) satellite. This subsection describes the principles behind the design of CLAUDIA.
The cloud/clear discrimination algorithm

The cloudy/clear discrimination algorithm is based on
the radiative properties of clouds and the contrast between imagery of clouds and the ground surface (i.e.,a clear sky). It utilizes (a) reflectance of solar radiance, (b) dependence of reflectance on wavelength, (c)CAPCOM

250

SW2

T2

CLAUDIA

1000

✓

✓

✓
✓

✓
✓

✓

✓

✓

reflectance of solar radiance at the absorption wavelength, (d) emission in the thermal infrared region,and (e) dependence of thermal emission on wavelength. It uses one visible (VN8), four near-infrared(VN11, SW1, SW2, SW3), and two thermal infrared(T1, T2) SGLI channels (Table 1). The details of theradiative properties and how to use them in SGLI
cloudy/clear discrimination will be explained.
(a) Reflectance of solar radiance
In general, clouds with a certain optical thickness
have a higher reflectance of solar radiance than earth
surfaces; therefore, reflectance can be an efficient indicator for differentiating between cloudy and clear
pixels. In particular, absorption by atmospheric molecules in the visible region is small, so a high reflectance is always caused by clouds. Channel VN11
(868.5 nm) is in the near-infrared region but molecular absorption is also low, and the effect of Rayleighscattering is minimal in the near-infrared channels.
Thus reflectance data from channel VN11 is used forcloud discrimination over bodies of water such as
oceans, rivers, and lakes. On the other hand, the reflectance data from channel VN8 (673.5 nm) is more(2019) 6:52

Page 4 of 25

suitable for land reflectance than in the near-infrared,
where reflection from vegetation is large and reduces
the contrast between land and clouds. The reflectancedata from channel SW1 (1050 nm) is also used. Pixels
that are in the swath of SW1 but not VNR are tested
via SW1 only.
The reflectance, Rf, is calculated based on the radiancedetected by the sensor and database of solar insolation
as follows:

the reflectance of deserts tends to increase monotonically with the wavelength, whereas the reflectance ofclouds does not depend on wavelength. The ratio between the reflectance of VN11 (868.5 nm) and SW3(1630 nm) is given by:

Nakajima et al. Progress in Earth and Planetary Science

Rf ¼

πI
 
F0
μ0 2
d

ð1Þ

where I is the radiance measured in a channel, μ0 is the
cosine of the solar zenith angle, F0 is the solar irradianceat the top of the atmosphere, and d is the distance between the sun and the earth in astronomical units. Calculating solar irradiance requires the response function
for each channel. Cloudy/clear discrimination can bedone accurately from using the minimum albedo, theminimum surface reflectance data for each channel over
1 month; these values are taken as the surface albedo. Ifthese data are not available, the minimum albedo can becalculated from MODIS data instead. The reflectance
per used channel is then compared to the minimum albedo. However, it is difficult to discriminate between
clouds and surfaces with higher reflectance (such as
snow-covered areas or desert) using reflectance alone.
(b) Dependence of reflectance on wavelength
The reflectance of clouds changes little with wavelength in the visible region, as clouds are usually white.
In contrast, the reflectance of several types of land surfaces does change with wavelength, so the difference (orratio) of reflectances at different wavelengths can provide a good indication of a cloudy sky. In particular,
clouds over vegetation can be distinguished by the difference in reflectance between the visible and infrared
regions. The normalized difference vegetation index(NDVI) is generally used as an indicator of the level ofvegetation and is given by:

NDVI ¼

R f ð868:5 nmÞ−R f ð673:5 nmÞ
R f ð868:5 nmÞ þ R f ð673:5 nmÞ

ð3ÞThe value of NDVI is high for areas with vegetation,
whereas it is close to 0 for clouds.
Over sandy deserts or areas with sparse vegetation,
the difference between the reflectance in the infraredand visible regions is small and the reflectance islarge, similar to that for clouds. In the near-infrared,

R f ð868:5 nmÞ=R f ð1630 nmÞ

ð4ÞThis can be used for cloud discrimination over deserts,
but it is not so effective for light surfaces such as ice andsnow or icy areas. It is difficult to distinguish such surfaces and clouds based on the dependence of reflectanceon wavelength.
(c) Reflectance of solar radiance at the absorption
wavelength
The reflectance of solar radiance at the absorption
wavelength can be used to detect high clouds, such as
cirrus. Channel SW2 (1380 nm) is located within near awater vapor absorption band. With a clear sky, the intensity of the 1380 nm wavelength reaching a satellite is
low because the atmosphere readily absorbs this wavelength, obscuring the influence of difference between thesurface and low clouds. In contrast, if there is an object
at high altitude in the atmosphere, the radiance isreflected by the object and the absorption is smaller, increasing the intensity detected by the satellite. For thisreason, channel SW2 is particularly useful for detecting
high clouds, even if they are optically thin. However, theradiance measured by SW2 can sometimes be large forclear skies over highlands because of lower absorption
by the atmosphere.
(d) Emission in the thermal infrared region
Because clouds are usually colder than land, radiancein the thermal infrared region can be used for cloud/clear discrimination, especially for high clouds. However,
the radiance of channel T1 (10.8 μm) changes greatly
with the surface temperature, which makes it hard to determine the threshold for detecting clouds. This is why
the authors used the radiance received through T1 onlyin the restoral test as follows: if the brightness temperatureof channel T1 is sufficiently larger than the general (or climatological) surface temperature, the area is assigned asclear sky regardless of the results of other tests.
(e) Dependence of thermal emission on wavelength
Water vapor absorption at the wavelength of channel
T2 (12.0 μm) is slightly larger than that of channel T1(10.8 μm). Channel T2 also provides a larger imaginary
part of the refractive index for ice than does channel T1.

(2019) 6:52

Page 5 of 25

As a result, the brightness temperatures of channels T1
and T2 (called a “split window”) of thin cirrus clouds are
generally greater than that of clear sky and optically thinclouds (Inoue 1987). The authors therefore use the split
window to detect thin cirrus clouds.

To achieve neutral cloud/clear discrimination, Ishida andNakajima (2009) introduced the CCL and categorization by
threshold tests. Two preset threshold values, an upper limit
and a lower limit, are defined for the threshold test rather
than one value, as is typical (Fig. 2). The CCL is determinedby comparing the observed radiance against the two thresholds. A confidence level of 0 means that the area is cloudy
and a level of 1 means that the area is clear sky. A CCL between 0 and 1 indicates an ambiguous area. If it makes
sense to set a range around a certain value as the criterion
for determining cloudy or clear skies, upper and lower
boundaries for the ranges must each be identified by setting
two thresholds, giving a total of four thresholds (Fig. 3).
An overall CCL must be determined, integrating the
CCL of all the threshold tests. For realizing neutral discrimination of cloudy or clear skies, threshold tests are
categorized into two groups: those that tend to identify
clear skies incorrectly as cloudy (group 1), and thosethat tend to identify cloudy areas incorrectly as clear(group 2).
The representative CCL for group 1, G1, is determined
to be cloud conservative:

Nakajima et al. Progress in Earth and Planetary Science

Clear confidence level

Cloud/clear discrimination is done by comparing theradiance from the SGLI to the threshold value, which
defines the boundary between clouds and clear sky.
Several tests have two ends, discriminating as clear or
cloud when the observed value is within a certain
range. This method is called the threshold test. The
cloudy/clear discrimination algorithm consists ofmany threshold tests to improve accuracy because athreshold test that is effective for a certain cloud or
surface type may not be appropriate for another type.
However, it is difficult to define a threshold value accurately in all cases because the optical thickness of acloud changes gradually, and hence the boundary between cloud and clear sky is not well defined. It is inevitable that some ambiguity arises. Several cloudy/
clear discrimination algorithms interpret ambiguousareas as clear (cloud conservative) or cloud (clear
conservative) under a fail-safe determination. The
cloudy/clear discrimination algorithm for the SGLI
has to be unbiased because its results are used toanalyze both cloud and aerosol properties. An algorithm developed by Ishida and Nakajima (2009) for
neutral cloudy/clear discrimination leads to unbiased
results and is used in the SGLI.

pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
G1 ¼ 1− n ð1− F 1 Þ∙ð1− F 2 Þ∙∙∙ð1−F k Þ∙∙∙ð1−F n Þð5Þwhere Fk is the CCL of the kth threshold test in group 1.
Equation (5) means that if any of the threshold tests result in a CCL of 1, G1 = 1 (clear) regardless of the otherthreshold tests of group 1, whereas G1 = 0 (cloudy) only
when all the Fk values are 0.

Fig. 2 Determining the CCL in the cloud discrimination process. The horizontal axis represents the values for the threshold test. In this case, theobserved value is large when clouds are present. Two threshold values, the lower limit and upper limit, are set, creating an ambiguous area
between the two limits that is expressed as a CCL from 0 to 1

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 6 of 25

Fig. 3. Same as Fig. 1, except that the threshold testing uses two clear-sky ranges on both sides of a value

In contrast, the representative CCL for group 2, G2, isdetermined to be clear conservative:
pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
G2 ¼ n F 1 ∙ F 2 ∙∙∙ F k ∙∙∙F nð6Þwhich implies that if any of the threshold tests result ina CCL of 0, G2 = 0 (cloudy) regardless of the otherthreshold tests of group 2, whereas G2 = 1 (clear) only
when all the Fk values are 1. Finally, the overall CCL, Q(which corresponds to Equation 2 of Ackerman et al.
1998), is determined from the representative values forthe two groups as
Q¼

pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
G1 ∙G2

ð7Þ

Q is the output product of the cloudy/clear discrimination algorithm; that is, Q = 1 (0) indicates a clear (cloudy)
sky, and 0 < Q < 1 indicates ambiguity. The cloudy/clear
discrimination algorithms are designed to be used in parallel, not in cascade; that is, all applicable threshold tests
are performed independently, without considering the results from the other tests, with the exception of the one
restoral test with T1 mentioned above.
The SGLI cloud mask product contains additional information and has greater utility. Horizontal inhomogeneity is sometimes a useful indication of broken
clouds, especially over water. The relative standard deviation of reflectance at 673.5 nm (land) or 868.5 nm(water) within 3 × 3 pixels around a central pixel is used
as an index of the inhomogeneity.
Heavy aerosols and especially dust tend to be incorrectly assigned as cloudy, since aerosols are usuallydifficult to detect. Dust in the atmosphere tends to havea larger reflectance at shorter wavelengths (such as blue)
but a smaller split window value than clear sky, and to
be more horizontally homogeneous than clouds. Thus
the authors have designed a simple test for suspected
dust based on the optical characteristics of dust at solar
and thermal infrared wavelengths. Cirrus probability isdiagnosed from Rf (1380 nm), which is sensitive to highclouds as explained above. The phase of cloud particles(water, ice, or mixed) is roughly distinguished by thecloud-top temperature, which is inferred from T1 (10.8μm) and the split window, with thresholds that are empirically determined based on the CloudSat CLDCLASSLIDAR and MODIS-AUX products.
Data flow in the algorithm

This section explains the data flow in the cloudy/clear
discrimination algorithm. Figure 4 is a flow chart of thealgorithm.
(a) Input information
Input information used for cloudy/clear discrimination
is categorized as follows: (Ia) measurement data; (Ib)
scan geometry information; (Ic) algorithm-specific information; (Id) geophysical information; and (Ie) ancillary
information (Table 2).
The (Ia) data includes the calibrated radiance data foreach pixel. The (Ib) information includes the solar zenith
angle, satellite zenith angle, relative azimuth angle, latitude
and longitude, and date and time of observation. Threshold values for threshold tests are categorized as (Ic) information. The (Id) information is required for calculating

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 7 of 25

Fig. 4 Flow chart for the cloud/clear discrimination algorithm

reflectance, such as the solar irradiance data, considering
the SGLI instrument function. The (Ie) information consists of ancillary information, such as the database of surface minimum albedo for channels VN8 and SW1, andland/water flag data, which are derived from the Shuttle
Radar Topography Mission water body data.
(b) Preprocessing
Before the algorithm performs cloudy/clear discrimination, it determines whether it is night or day, water orTable 2 Input information for the cloud discrimination
algorithm
Input information

Description(Ia) Measurement data

Radiance(Ib) Scan geometry
information

Solar zenith angle, satellite zenith angle,
relative azimuth angle,
latitude and longitude, date and time ofobservation(Ic) Algorithm-specificinformation

Threshold values, etc.

(Id) Geophysical information

Solar irradiance data, etc.

(Ie) Ancillary information

Surface minimum albedo in channels
VN8 and SW1 Land/Water flag

land, or areas are sunglint from the input data. The sequence of applied threshold tests is arranged according
to conditions. The determinations of night or day isbased on the solar zenith angle: when the angle is 85° or
more, it is assumed to be night. Note that this paper focuses on remote sensing in the daytime, although the
GCOM-C product operated by JAXA also does cloudy/
clear discrimination at night using thermal infrared
bands only. Water, which includes lakes and rivers aswell as the ocean, is distinguished from land by comparing the latitude and longitude of the pixel position
against the 1 km land/water flag data. When the latitudeis 66.6° or more north or south, the area is regarded as apolar region. If the cone angle between the solar incident
and satellite direction is lower than 35° over the water
surface, this area is identified as sunglint. In addition,
the possibility of snow cover is evaluated based on thenormalized difference snow index (NDSI):NDSI ¼

R f ð530 nmÞ−R f ð1630 nmÞ
R f ð530 nmÞ þ R f ð1630 nmÞ

ð8ÞOver land with an altitude above 2 km and a latitude greater (smaller) than 30° (− 30°), if NDSI > 0.4,
Rf (1630 nm) < 0.02, Rf (530 nm) > 0.15, Rf (868.5

(2019) 6:52

Page 8 of 25nm) < 0.02, and brightness temperature Tb (10.8 μm) <280 K, then the “Snow/Ice Probability Flag” in the bit field
is set to “yes.” For water areas with latitudes greater (orsmaller) than 40° (− 40°), the threshold setting for this flagis the same as for land, except for Rf (1630 nm) < 0.01.
Note that this flag is for quality assurance and does notaffect cloudy/clear discrimination.

given in Table 4. For angles between those in thetable, the increase is determined by linear
interpolation. Finally, the overall CCL (Q) is determined by integrating the CCLs of the threshold tests.

Nakajima et al. Progress in Earth and Planetary Science(c) Determining the CCL for each threshold test
Table 3 summarizes the threshold values for eachthreshold test applied in the cloudy/clear discrimination. The thresholds of reflectance Rf (673.5 nm) andRf (1050 nm) for land depend on the surface minimum albedo (denoted by Rf surface in Table 3(b)) of
the pixel position. If a pixel over water is judged to
be in a sunglint area, the threshold for the reflectanceis increased according to the cone angle. The relationship between the cone angle and the increase in
the threshold, denoted by Rf sun-glint in Table 3(a), isTable 3 Thresholds for cloud screening with SGLI. a: Overwater, b: Over land, and c: Over polar regions. Rf and Tb arereflectance and brightness temperature, respectively
Threshold test

Lower limit

Upper limit(d) Outputting the cloud/clear discrimination results
Output from the cloudy/clear discrimination algorithm is expressed in 16 bits, which include the overall CCL and other miscellaneous data. Table 5 shows
the bit fields of an output file. The overall CCL isexpressed in a bit field with three bits as shown in
Table 6, and the cone angle is expressed by two bits
as shown in Table 7. After deriving the overall CCL,
additional information is derived, which (including
the possibility of snow cover) does not influence theresult of cloudy/clear discrimination. The horizontal
inhomogeneity flag is set to “yes” if the relative standard deviation is greater than 0.25 for land or 0.1 forwater. Referring to NOAA/NESDIS/STAR (2010) andMouri et al. (2016), the heavy aerosol flag over land
is set to “yes” if the horizontal inhomogeneity flag is“yes,” the snow/ice probability flag is “no,” anda. Water regions

R f ð1380 nmÞ < 0:035

ð9aÞ

R f ð412 nmÞ=R f ð443 nmÞ < 1:16

ð9bÞ

1

Rf(868.5 nm)

0.195 + Rf sun-glint

0.045+ Rf sun-glint

1

NDVI

Smaller End

-0.10

-0.22

R f ð412 nmÞ > 0:2

ð9cÞ

Larger End

0.22

0.46

R f ð1630 nmÞ < 0:3

ð9dÞ

R f ð868:5 nmÞ < 0:3

ð9eÞ

Tb ð10:8 μmÞ−Tbð12:0 μmÞ < 0 K

ð9fÞ

1

Rf (1050 nm)

0.195 + Rf sun-glint

0.045+ Rf sun-glint

2

Tb(10.8 μm)-Tb(12.0 μm)

3.0 [K]2.6 [K]2

Rf (1380 nm)

0.015

0.005

-

Tb(10.8 μm)

297.5 [K]-(*1)

b. Land regions

Equation (9a) excludes cirrus clouds. The thresholdtests for water are the same as for land, but the threshold settings are:1

Rf (673.5 nm)

0.195+ Rf surface

0.045+ Rf surface

1

NDVI

Smaller End

-0.1

-0.22

Larger End

0.22

0.46

R f ð1380 nmÞ < 0:01

ð10aÞ

1

Rf (868.5 nm)/ Rf (1630 nm)

1.05

1.00

R f ð412 nmÞ=R f ð443 nmÞ < 1:25

ð10bÞ

1

Rf (1050 nm)

0.195+ Rf surface

0.045+ Rf surface2

Tb(10.8 μm)-Tb(12.0 μm)

3.0 [K]2.6 [K]

0:15 < R f ð412 nmÞ < 0:25

ð10cÞ

2

Rf (1380 nm)

0.040

0.030

R f ð1630 nmÞ < 0:25

ð10dÞ

-

Tb(10.8 μm)

297.5 [K]-R f ð868:5 nmÞ < 0:3

ð10eÞ

(*1)

c. Polar regions1

Rf (673.5 nm)

0.14+ Rf surface

0.06+ Rf surface

1

NDVI

Smaller End

-0.1

-0.22

Larger End

0.22

0.46

2

Rf (1380 nm)

0.060

0.030

-

Tb(10.8 μm) (*1)

297.5 [K]-(*1)Restoral test

Table 4 Increase in the threshold values for reflectance tests onsunglint areas
Cone angle (°)

Increase in the threshold

35

0

25

0.013

15

0.075

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 9 of 25

Table 5 Bit fields of the output files for the cloud discrimination
algorithm

Table 7 Expressions of cone angles in bits
Cone angle (°)

Bit

Bit
field

Description

Bit

0–15

00

0

Cloud mask algorithm flag

0 Not determined, 1
Determined

15–25

01

25–35

10

35–45

11

3, 2, 1

Clear confidence level flag

Table 6

4

Day/Night flag

0 Night, 1 day

5

Land/Water flag

0 Water, 1 Land

6

Snow/Ice probability flag

0 Yes, 1 No

8, 7

Sun-glint cone angle flag

Table 7

9

Heavy aerosol flag

0 Yes, 1 No

10

Cirrus probability flag

0 Yes, 1 No

11

Horizontal inhomogeneity flag

0 Yes, 1 No

13, 12Cloud phase flag

00 Uncertain, 01 Liquid
10 Ice, 11 Mixed

14

Cloud shadow flag (TBD)

0 Yes, 1 No

15

VN data availability flag

0 Not available, 1 AvailableTb ð10:8 μmÞ−Tbð12:0 μmÞ < 0:5 Kð10fÞ

Tb ð10:8 μmÞ > 268:15 K

ð10gÞ

The cirrus probability flag is set to “yes” if Rf (1380 nm)> 0.035. The cloud phase flag is set according to cloudthermodynamic phase discrimination as mentioned in thenext subsection. In addition, the bit field includes the VN
data availability flag, which is set to “not available” if the
VN bands (380–868.5 nm) are out of swath, because theswath of VN (1150 km) is narrower than those of SWIR
and TR (1400 km).
Algorithm for the cloud thermodynamic phase

The SGLI cloud flag product includes cloud thermodynamic phase information for day and night derived fromSGLI thermal infrared channels (Table 5). One of the best
choices for cloud-phase discrimination is a combination of

Table 6 Expressions of overall CCLs in bits

the 8.5- and 10.8-μm channels used in the MODIS L2
cloud product because of the larger difference between imaginary indices of refraction for water and ice over 8 to 12μm (Baum et al. 2000). However, the SGLI does not havean 8.5-μm channel, so a combination of the 10.8- (T1)
and 12-μm (T2) channels is used. Figure 5 shows themost frequent cloud phases (represented in color) as
a function of the brightness temperature (Tb) of the
10.8-μm channel and the difference between thebrightness temperatures of the 10.8- and 12-μm channels extracted by a combined use of the CloudSat
CLDCLASS-LIDAR and MODIS-AUX products. They
and their documents are distributed by the CloudSat
Data Processing Center (http://www.cloudsat.cira.colostate.
edu). Figure 5 has the following cloud-phase classes that we
extract from the CloudSat CLDCLASS-LIDAR product:
single-layer liquid-phase cloud (LIQ); single- or multi-layer
ice- or mixed-phase cloud (ICE); and multi-layer cloud(MUL), in which an ice-phase cloud lies above a liquidphase cloud. Both ICE and MUL have ice-phase clouds atthe cloud top. For simplicity and robustness, the SGLI
cloud phase algorithm discriminates cloud phases using thetwo boundaries shown by dotted lines in Fig. 5: The cloudphase flag is set to “ICE” if Tb (10.8 μm) – Tb (12.0 μm) >0.08 Tb (10.8 μm) − 21 and Tb (10.8 μm) < 265 K, whereas
this flag is set to “LIQ” if Tb (10.8 μm) – Tb (12.0 μm) <0.08 Tb (10.8 μm) − 21, and the other flags are labeled as“MUL.” Although the response functions of the SGLI T1
and T2 channels are slightly different from those of MODIS
bands 31 and 32, we apply the thresholds with no corrections. Figure 6 shows images of (a) the color composite of
the SW4, SW3, and SW1 channels, and (b) the cloudphase flag. The images were created from the GCOM-C/
SGLI observations over the Indian Ocean on March 23,
2018, which captured Tropical Cyclone Marcus. The labels
in Fig. 6 match the labels in Fig. 5. Clear pixels identified by
the CLAUDIA are classified automatically into the class“Uncertain (UNC).” And, cloudy pixels are classified into
the classes “LIQ,” “ICE,” and “MUL” as shown in Fig. 5.

Overall clear confidence level

Bit

0.00

000

0.00–0.17001

0.17–0.33010

0.33–0.50

011

0.50–0.67

100

CAPCOM: algorithm for retrieving cloud properties

0.67–0.83

101

CAPCOM is an algorithm for retrieving cloud properties
from the visible to infrared imager radiances. This algorithm has been used by the Global Imager (GLI) on
ADEOS-II, CAI on GOSAT, AHI on Himawari-8, and

0.83–1.00

110

1.00

111

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 10 of 25

Fig. 5 The most frequent cloud phases as a function of the brightness temperature (Tb) of the 10.8-μm channel and the difference between thebrightness temperatures of the 10.8- and 12-μm channels extracted by a combined use of CloudSat CLDCLASS-LIDAR and MODIS-AUX products.
The color represents most frequent cloud phases: the cloud-phase flag is set to “ICE” if Tb (10.8 μm) – Tb (12.0 μm) > 0.08 Tb (10.8 μm) – 21 andTb (10.8 μm) < 265 K, whereas this flag is set to “LIQ” if Tb (10.8 μm) − Tb (12.0 μm) < 0.08 Tb (10.8 μm) − 21, and the other flags are labeled
as “MUL”

SGLI on GCOM-C, and will be used for MSI on the EarthCARE satellite, which will be launched in 2021. CAPCOM
was developed by Nakajima and Nakajima (1995) based on
the principle shown in the Nakajima-King diagram (Fig. 7).
It was applied to two AVHRR scene images for retrieving
warm-water cloud properties. Kawamoto et al. (2001)
applied a water vapor correction function to CAPCOM to allow the algorithm to retrieve cloud properties globally. Scattering models of the spherical Mie
particles and the non-spherical Voronoi particles(Ishimoto et al. 2010) are used to develop the look-up
table (LUT) for CAPCOM to retrieve water-cloud andice-cloud properties (letu et al. 2012, 2016, 2018), respectively. This subsection describes the operating
principles of CAPCOM.
CAPCOM uses the LUT iteration method to retrieve
the target geophysical parameters from satellite-derived
radiance data. CAPCOM uses a non-absorption channel(SW1), an absorption channel (SW4 primary, SW3 secondary), and a thermal channel (T1) to derive cloud optical thickness (τc), cloud effective particle radius (re),and cloud-top temperature (Tc). Newton-Raphson
methods were used to obtain τc, re, and Tc, simultaneously. In addition to these radiance data, some ancillary
input data, such as the vertical profile of thetemperature, pressure, water vapor, and ground

albedo, are also used to calculate related geophysicalparameters, and cloud top height (Zc) and cloud-top
pressure (Pc) are retrieved by comparing cloud-top
temperature (Tc) and a temperature profile from ancillary data (objective analysis data).

Principles of the algorithm

CAPCOM uses the following LUTs: cloud-reflectedradiance in channels SW1 and SW4; transmissivities
and reflectivity in channels SW1 and SW4; and transmissivity in channel T1. Figure 8 illustrates the flowof the analysis. The Newton-Raphson method is used
to iterate the main loop in the program. For watercloud analysis, the cloud geometrical thickness Dc
was obtained using the liquid water path (LWP) andliquid water content (LWC). For the ice-cloud analysis, Dc = 1 km was assumed since LWP = 2/3 τcre
is not correct for ice clouds. For water-cloud analysis,
stratocumulus clouds are assumed for liquid watercontent with a value of 1.28 × 10−6 g/cm-3. The Dc value
was used for water-cloud analysis since the LUT grid system uses Dc. Some related parameters, such as cloud liquid water path and cloud top height (Zc), are also
calculated in CAPCOM with temperature-slicing data(T(z)) from the objective analysis dataset.

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

a)Page 11 of 25b)Fig. 6 Images of a) the color composite of the SW4, SW3, and SW1 channels and b) the cloud phase flag discriminated by the SGLI cloud phasealgorithm. The images were composed from the GCOM-C/SGLI observation over the Indian Ocean on March 23, 2018, which captured Tropical
Cyclone Marcus

Physical and mathematical aspects of CAPCOM

The solar reflectance method uses a non-absorbing
channel (such as 673.5, 868.5, or 1050 nm) andwater-absorbing shortwave infrared wavelengths (suchas 1630 and 2210 nm) for the simultaneous retrievalof the cloud optical thickness, τc, at a wavelength of
500 nm and the effective particle radius, re. This
paper mainly discusses the solar reflectance method
using the SGLI channels SW1 (1050 nm), SW4 (2210 nm),and T1 (10.8 μm). Despite the inclusion of 10.8 μm in thisanalysis, this will be referred to as the “solar reflectancemethod” since the primary two bands are 1050 nm and2210 nm.
The effective particle radius of the water clouds is defined by:
R∞ 3
r nðr Þdrr e ≡ R0∞ 2
0 r nðr Þdr

ð11Þ

where n(r) is the number size distribution as a function of particle radius r. The effective particle radiusof the ice clouds is defined by:R∞
3 0 V ðLÞnðLÞdL
re ≡ R ∞4 0 AðLÞnðLÞdL

ð12Þ

where L is the maximum dimension of the Voronoi particle. V(L) and A(L) are the volume and projected area of
the Voronoi single particle, respectively. For the satellite
signal simulation, we use an accurate, efficient radiativetransfer scheme (Nakajima and Tanaka 1986, 1988) extended to include the thermal radiative transfer(Stamnes et al. 1988).
We retrieve τc at a wavelength of 500 nm and refrom SGLI channels SW1 and SW4 because thesechannels primarily depend on the cloud optical thickness and the effective particle radius, respectively. Although the concept of retrieval is simple, there are
difficulties in determining the cloud properties fromthe measured SGLI spectral radiance. The solar radiation reflected by the ground must be removed fromthe observed radiance.
Formulations of the radiative components

According to the radiative transfer theory for plane
paral=l layers with an underlying Lambert surface,Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 12 of 25

Fig. 7 Nakajima-King diagram of the SGLI for SW1 versus SW4. Warm water clouds are assumed. The satellite zenith angle (TH1), solar zenith
angle (TH0), and relative azimuthal angle (PHI) are 50°, 30°, and 60°, respectively

any unexpected radiation component, such as thesolar radiation reflected by the ground surface, isremoved from the satellite-received radiance, Iobs , todecouple the radiation component reflected by thecloud layer, I. The unexpected radiation component isexpressed in the second term of Equation (Letu et al.,2016):I ðZ c ; Dc ; τ c ; r e ; μ; μ0 ; ϕ Þ ¼ I obs ðZ c ; Dc ; τ c ; r e ; μ; μ0 ; ϕ Þ−t ðZ c ; Dc ; τ c ; r e ; μÞAg
μ F0t ðZ c ; Dc ; τ c ; r e ; μ0 Þ 0
1−r ðZ c ; Dc ; τ c ; r e ÞAgπð13Þ
where F0 is the extraterrestrial solar flux, μ0 and μ
are the cosines of the solar and satellite zenith angles,and ϕ is the azimuthal angle of the satellite relative
to the sun. Zc and Dc are the top height and the geometrical thickness of the cloud, respectively. Ag is theground albedo, and t and r are the transmissivity andspherical albedo of the cloud. These formulations areexact for monochromatic radiance. We introduce afurther process of averaging of the variables in theformulations with respect to the wavelength. For example, t is averaged with a subchannel response function of SGLI as:
(
)
N
M
N
X
X
X
t¼
φn
ξ n;k  t n;k =
φn
ð14Þ
n¼1

k¼1

n¼1

where φn is the response function of the nth subchannel
wavelength for each SGLI channel, ξn, k is the weight of thekth k-distribution, and tn, k is the transmissivity for the kth
k-distribution at the nth wavelength.

Results and Discussion
Comparison with ground-based whole-sky camera images
Accuracy evaluation strategy

The output of the cloudy/clear discrimination algorithm(CLAUDIA) was compared with whole-sky images(WSIs) acquired with sky cameras located at several
ground stations in Japan (Table 8). One is a sky camera
system consisting of a single-lens reflex digital camera(D7000, NIKON). This is the same system used by Letuet al. (2014) and Kuji et al. (2018), and details can befound in their paper. The other system is a Skyview

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 13 of 25

Fig. 8 Flow chart of the CAPCOM algorithm. “_corr.” denotes correction. LWP and LWC denote liquid water path and liquid water content, respectively.
Warm water clouds are assumed(PSV-100, PREDE), which was also used by Yabuki et al.
(2014). Both systems capture color WSIs in JPEG format. Cloud fractions were first determined from the
WSIs based on the image analysis method described in
the next subsection, and were then compared with thecloud fractions determined by CLAUDIA to evaluate theaccuracies of SGLI cloud flag products based on the validation guidelines for the GCOM-C mission (Hori et al.
2018). SGLI cloud flag products were also compared toevaluate the performance of CLAUDIA with products
derived from other satellite sensors, such as MODIS.

Analysis of WSIs

WSIs acquired with the two different sky camera systems are similar true-color RGB images in JPEG format
with different pixel resolutions (640 × 480 for the Skyview and 4928 × 3264 for the NIKON system). Although
the image sizes are different, the pixel resolution of
the Skyview image is large enough to extract thecloud fraction (percentage) within the fisheye image
circle. The approach described below was used todiscriminate cloudy and clear pixels based on animage analysis technique similar to that used by(2019) 6:52

Nakajima et al. Progress in Earth and Planetary Science

Page 14 of 25

Table 8 Sites and types of sky cameras used for evaluating the CLAUDIA outputs and the amount of training data used for
calculating cloud probabilities in SI-BI space for each site
Location (Lat/Lon)

Sky-camera type

The number of training data
Clear scenes

Cloudy scenes

Ny-Alesund (78.93 N/11.86 E)

Skyview

32

77

109

Sapporo (43.08 N/141.34 E)

D7000

72

70

142

Total

Tsukuba (TKSC) (36.07 N/140.13 E)

D7000

96

113

209

Tsukuba (MRI) (36.06 N/140.13 E)

Skyview

128

131

259

Kumamoto (32.84 N/130.87 E)

D7000

31

50

81

Miyakojima (24.74 N/125.33 E)

Skyview

33

31

64

Syowa St. (69 S/39.59 E)

Skyview

65

80

145

a)

a)

b)

d)

Fig. 9 Method of analysis for the logistic regression approach. a Two-dimensional histograms for clear training data and b cloudy training data. c
An example logistic regression curve derived for the clear and cloudy cases within the bin class of BI shown in red dotted boxes in a, b, and dcalculated clear probability in the BI-SI space(2019) 6:52

Page 15 of 25

Yoshimura and Yamashita (2013). They developed athreshold method using the sky index (SI) and thebrightness index (BI) as calculated using the following equations:

identified clear and cloudy pixels corresponding to clear
or cloudy skies, and also for mixtures of clear andcloudy conditions. Once the fractions of cloudy pixels
within the field of view (image circle located 60° fromthe zenith) of WSIs were obtained, they were compared to the cloud fractions derived from SGLI cloud
flag products (CLAUDIA outputs) at the locations of
the sky camera sites. In this comparison, we defined
the common fields of view (radii) of both SGLI andsky cameras to be 1.75, 10.4, and 17.3 km by assuming that the heights of cloud layers are at 1, 6, and
10 km, respectively which correspond to the low,
middle, and high clouds under the International Satellite Cloud Climatology Project cloud classification(Rossow and Schiffer 1999).

Nakajima et al. Progress in Earth and Planetary Science

SI ¼ ðBlue−RedÞ=ðBlue þ RedÞð15Þ

BI ¼ ðRed þ Green þ BlueÞ=ð255  3Þð16Þ

Here, Red, Green, and Blue are the digital numbers of
the individual colors stored in the JPEG file. Using these
two indices, Yoshimura and Yamashita (2013) identified
cloudy and clear pixels in WSIs with an exponential curve
to define the threshold in the SI-BI space. A similar approach is used here, although instead of using exponential
curve estimates of the cloudy and clear probability, they
were made in the same two-dimensional SI-BI space basedon the logistic regression approach (Fig. 9). Cloudy/clear
probabilities in the SI-BI space were calculated for eachbin, which is defined along the BI axis, using training data
carefully selected for clear and uniformly cloudy cases byvisual inspection (Table 8), and the cloud/clear probabilities were stored in LUTs for all the sky camera sites. For
example, SI data determined for clear and cloudy cases
within a bin class of BI ranging from 0.20 to 0.22 (boxes
drawn in red in Fig. 9a, b) were extracted and used asnominal variables (y), that is, y = 0 for the cloudy
case and y = 1 for clear cases. Then, the nominal
variables are curve-fitted with a sigmoid function as
a function of SI to generate a smoothed clear probability curve as shown in Fig. 9c. Figure 9d shows
the clear probabilities generated for all the bins ofBI from 0.0 to 1.0. Figure 10 shows examples of thecloud/clear discrimination results for the Kumamotosite based on the logistic regression approach, which

Results

Figure 11 compares cloud fractions derived from groundbased sky cameras with those from SGLI observations forall sites for a cloud height of 6 km. Similar results were obtained for the other cloud heights and thus are not shown
here. Most of the data points are concentrated around thelower left (clear sky) and upper right (cloudy sky) corners,
although some are scattered around the lower right andupper left corners. The scattered points around the lower
right corner are due to the spatial heterogeneity of clouds,
including altitude biases, which causes biases in the field ofview of the sky camera and SGLI. In contrast, the points
scattered around the upper left corner are due to themistaken detection of thin cirrus clouds within WSIs
in the SI-BI space-based analysis. In addition, for
Syowa Station (Fig. 11g), the current version of
CLAUDIA fails to discriminate between clear andcloudy conditions correctly when the solar zenith isFig. 10 Examples of raw sky camera images (upper images) and analyzed sky camera images (lower images) for a completely clear sky, b completely
cloudy sky, and c a mixed clear/cloudy sky

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 16 of 25

Fig. 11 Comparison of cloud fractions based on ground-based sky camera observations with those derived from SGLI data using the CLAUDIA
algorithm. Observations taken from a) Ny-Alesund, b) Sapporo, c) Tsukuba (TKSC), d) Tsukuba (MRI), e) Kumamoto, f ) Miyakojima, g) Syowa
Station, and h) all sites

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 17 of 25

Table 9 Summary of cloud detection accuracies
Accuraciesa

Ny-Alesund

SapporoTsukuba (TKSC)

Tsukuba (MRI)

Kumamoto

Miyakojima

Syowa St.

All

N

53

25

58

25

53

47

33

294

UAcloud

100.0

95.0

91.7

100.0

100.0

82.9

86.7

92.9

PAcloud

92.1

90.5

100.0

100.0

82.9

93.5

96.3

92.2

OA

94.3

88.0

94.8

100.0

88.7

83.0

84.8

90.5

a

For a cloud height of 6 km

a)

b)

c)

d)

Fig. 12 a) True-color RGB combining the VN8, VN5, and VN3 channels of a single SGLI L1B product. b) CCL for the SGLI cloud flag algorithm. c) Truecolor image combining bands 1, 4, and 3 of multiple MODIS L1B products overlapping with the SGLI L1B. d) MOD35 cloud mask result(2019) 6:52

Page 18 of 25

large (80° to 90°). Except for these scattered points,
the satellite-derived cloud fractions are consistent
with those derived from the sky camera. The user’s
accuracies (UA) and producer’s accuracies (PA) were
calculated based on a binary classification in which
the data plots with a cloud fraction higher (less) than
50% are assumed to be cloudy (clear) for both thesky camera and the satellite. The calculated UA and
PA for cloud detection and the overall accuracies(OA) are summarized in Table 9. UA and PA are
mostly high around 90% and the resulting OA is also

high, ranging from 83 to 100% (the average of allsites is 90.5%). Therefore, the SGLI cloud detection
algorithm (CLAUDIA) has sufficient accuracy forcloud masking, which meets the threshold for releasing data from the SGLI cloud flag product, that is,
the minimum success criterion of the GCOM-C mission (Hori et al. 2018). To understand the causes ofthe scattered points that reduce the UAs and PAs,
the accuracies of both sky camera and SGLI-derived
cloud fractions need to be refined further considering,for example, the frequencies of cloud appearance

Nakajima et al. Progress in Earth and Planetary Science

a)

b)

c)

d)

Fig. 13 Same as Fig. 12, but over Japan on March 29, 2018

(2019) 6:52

Page 19 of 25

heights at individual sites measured by micropulse
lidar (Shiobara et al. 2003).

node. However, their swath and repeat cycles are different: the GCOM-C/SGLI VN (IR) instrument has aswath of 1150 km (1400 km) and a repeat cycle of 34
days. It can observe the whole globe approximately inevery 2 days. The Terra/MODIS instrument has aswath of 2330 km and a repeat cycle of 16 days. It cancover the entire Earth’s surface every 1 to 2 days. To
clarify the characteristics of SGLI-CLFG (the CloudFlag), five scenes were selected (Figs. 12, 13, 14, 15,
and 16) with different cloud properties and groundsurface characteristics. Each scene has four panels: (a)the SGLI true-color image combining the VN8, VN5,

Nakajima et al. Progress in Earth and Planetary Science

Comparison with other sensors
Cloud/clear discrimination

The authors evaluated SGLI cloud cloud/clear discrimination (referred to as the SGLI cloud flag [SGLICLFG]) by comparing it with the SGLI visible composite image and the MODIS cloud-mask product(MOD35). The orbits of GCOM-C and Terra aresimilar: both have a sun-synchronous sub-recurrent
orbit with a 10:30 local sun time at the descending

a)

b)

c)

d)

Fig. 14 Same as Fig. 12, but over the Amazon forest on May 21, 2018

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 20 of 25

a)

b)

c)

d)

Fig. 15. Same as Fig. 12, but over the Arabian Peninsula on April 23, 2018

and VN3 channels of a single SGLI level-1b productfor the scene; (b) the CCL image of the SGLI-CLFG
derived by applying CLAUDIA to the SGLI level-1b
product; (c) the MODIS true-color image combining
bands 1, 3, and 4 of multiple MODIS level-1b products overlapping with the SGLI level 1b scene; and (d)the confidence level image of MOD35. The CCL of
the SGLI-CLFG consists of eight gradations (3 bits)
obtained by discretizing the real value of 0.0 to 1.0
derived from Q in Equation (7), whereas the confidence level of MOD35 is one of four categories(cloud, uncertain, probably clear, clear). Figure 12

shows images over Japan captured on June 14, 2018,
which include various clouds over both ocean andland with different top heights, optical thicknesses,
and horizontal scales. Due to the differences in theorbits of the GCOM-C and Terra satellites, sunglint
overlaps with the Japanese archipelago in the SGLI
image, and appears around the western part of the
MODIS image. The confidence levels of SGLI-CLFG
and MOD35 are generally similar, although the SGLICLFG is continuous even in the sunglint area. This
result implies that the sunglint correction shown in“Results and Discussion” section and Table 4 works

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Page 21 of 25

a)

b)

c)

d)

Fig. 16. Same as Fig. 12, but over the Sea of Okhotsk on April 2, 2018

well. The MOD35 tends to classify clear pixels
strongly influenced by sunglint as probably clear. The
other characteristic of the SGLI-CLFG is that the
CCL depends on the apparent optical thickness of thecloud edge and small cumulus clouds because CLAUDIA was designed to allow cloud distinction ambiguity. Figure 13 shows the images capturing the belt ofyellow sand flowing from Asia, traversing the Japan
Sea, and mainland Japan on March 29, 2018. The
CCL values of the SGLI-CLFG in the yellow sand belt
are almost above 0.66, which means probably clear.
However, MOD35 misclassified the same pixels as

cloudy. Figure 14 shows the images capturing the
Amazon forest on May 21, 2018, which include many
clouds of small horizontal extent, comparable to theinstantaneous field of view (IFOV) of the SGLI and
MODIS. The figures clearly show the difference between SGLI-CLFG and MOD35. In Fig. 14d, cloudy
pixels are spread without gaps because MOD35 tends
to be clear conservative. On the other hand, in Fig.
14b, the CCL of the SGLI-CLFG varies depending on
the apparent optical thickness of cloud edges andsmall cumulus clouds because CLAUDIA is designed
for neutral cloud/clear discrimination. Figure 15

(2019) 6:52

Page 22 of 25

shows images of the Arabian Peninsula captured on
April 23, 2018, which include a dust storm in thecenter of the peninsula. Compared with the SGLI
true-color image, the identification of SGLI-CLFG isreasonable. In addition, the CCLs of the SGLI-CLFG
in the dust storm pixels the MOD 35 misidentified as
cloudy are from 0.94 to 1. Finally, Fig. 16 shows an imagearound the Sea of Okhotsk on April 2, 2018. For pixels
covered with snow and/or sea ice, it is generally difficult
to distinguish clear sky and clouds because they have aspectrum similar to clouds, namely, white with high reflectance. Nevertheless, the CCL of the SGLI-CLFG is reasonable compared with the true-color image and MOD35.

(within a 1° × 1° grid) and temporal averaging (during theabove time period) were applied to retrievals. Only data with
τc > 5 were used to evaluate re and Tc of water- and ice-phase
clouds to avoid estimation bias due to contamination fromclear-sky radiance. In addition, only data with Tc > 265 K areused in the evaluation of τc, re, and Tc of water-phase clouds
to avoid contamination from ice or mixed-phase cloud. Notethat the thresholds are applied to the pixel-level retrievals.

Nakajima et al. Progress in Earth and Planetary Science

Cloud properties

The cloud properties retrieved from the SGLI measurementswith CAPCOM (described in “Results and Discussion” section) were evaluated by comparing them to the cloud properties with the MODIS Cloud Product (MOD06) Collection
6.1. Variables that were validated were cloud optical thickness,cloud particle effective radius, and cloud-top temperature forwater and ice clouds. The data used were from over oceans
and land in the middle and low latitudes (60° N–60° S) from
August 22, 2018 to September 14, 2018. To reduce variability
induced by cloud horizontal inhomogeneity and the gap between the IFOVs of the SGLI and MODIS, a spatial averaging(a) Water-phase clouds
Figure 17 shows scatter plots of (a, d) optical thickness, (b,d) effective radius, and (c, e) cloud-top temperature for water
cloud from the SGLI retrievals compared with the MOD06
retrievals. Panels (a, b, c) are for ocean and (d, e, f) are forland. First, the results shown in Fig. 17a, d indicate that the
SGLI τc values over ocean (land) are consistent with the
MOD06 τc values with a sufficiently small bias of 0.7 (0.5), a
reasonable root mean square error (RMSE) of 2.3 (3.8), and ahigh correlation coefficient (R) of 0.88 (0.83). The results
shown in Fig. 17b, e indicate that the SGLI re values overocean (land) are also consistent with the MOD06 re values
with a bias of − 0.0 (− 0.5) μm, RMSE of 1.5 (1.8) μm, and R
of 0.92 (0.88). Finally, the results shown in Fig. 17c (17f) indicate that the SGLI Tc values over ocean (land) are also consistent with the MOD06 Tc values with a bias of 0.4 (0.8) K,

Fig. 17 Scatter plots of a, d optical thickness, b, e effective radius, and c, f top temperature for water clouds from the SGLI retrievals comparedwith the MOD06 retrievals. Panels (a–c) are for ocean and (d–f) are for land(2019) 6:52

Page 23 of 25

RMSE of 2.1 (2.6) K, and R of 0.94 (0.92). The finding that
the SGLI-derived cloud properties are consistent with thoseof MOD06 despite the differences in sensor hardware, algorithm implementation, and wavelengths indicates the validityof SGLI’s radiometric and geometric calibration and the highmaturity of the water cloud retrieval algorithm.

μm, 9.1 (7.7) μm, and 0.47 (0.66). These results canbe explained by single-scattering properties (e.g.,
asymmetry parameter, single scattering albedo, extinction efficiency) of the ice particle habits used in icecloud retrievals of MODIS and SGLI products, as wellas the different particle size distribution functions forthe two satellite products (Letu et al., 2018). The Voronoi model and eight solid column aggregates with severely roughened surfaces (“8-clm-agg”) were used in theice-cloud retrievals for SGLI and MODIS data, respectively. Thus, the single-scattering properties of the Voronoi
model are different from those of the 8-clm-agg model,
which may lead to a difference in ice-cloud retrieval. In
the SGLI ice-cloud retrievals, the log-normal size distribution function was used for ice-cloud properties, which isdifferent from the gamma size distribution function usedin the retrieval algorithm of the MODIS C6 cloud product. Figure 18c and f shows the Tc values from the MODIS
and SGLI retrievals over the ocean (land). The Tc values
from the SGLI retrievals are consistent with resultsfrom the MODIS retrievals, with R of 0.75 (0.65).
However, the SGLI Tc values are larger than thosefrom the MODIS retrievals, with a bias of 7.0 (10.2)
K and an RMSE of 11.1 (14.0) K. In the MODIS-C6
product, the CO2 slicing method is used to obtain Tc(Platnick et al. 2017). In contrast, CAPCOM in thisNakajima et al. Progress in Earth and Planetary Science(b) Ice-phase clouds
Figure 18 shows a comparison of the optical thickness, effective radius, and cloud-top temperature forice clouds from MODIS and SGLI retrievals overocean (top panels) and land (bottom panels). Figure
18a, d confirms that SGLI τc values over ocean (land)
are consistent with the MOD06 τc values. The valuesof τc from SGLI are slightly larger than those from
MODIS, and the biases of τc for the two satellite retrievals are 2.2 and 2.4 in the selected ocean and land
areas, respectively. The RMSE and R values of the retrievals for the ocean (land) are 4.2 (4.6) and 0.86
(0.82), respectively. Figure 18b, e shows the comparison of the re values between the MODIS and SGLI
retrievals over the ocean (land). The re values fromthe SGLI data are larger than those from the MODIS
retrievals over both the ocean and the land. The bias,
RMSE, and the R for the ocean (land) are 2.0 (2.5)

Fig. 18 Scatter plots of a, d optical thickness for ice clouds, b, e effective radius, and c, f top temperature for water clouds from the SGLI retrievals
compared with the MOD06 retrievals. Panels (a–c) are for ocean and (d–f) are for land(2019) 6:52

Page 24 of 25

study uses the single-channel thermal band data and cloud
top properties from the reanalyzed data to retrieve Tc fromthe SGLI data (Nakajima and Nakajima 1995). Nevertheless, the Tc values from the SGLI and MODIS retrievals
for water clouds agreed well (Fig. 17c, f), although the icecloud Tc values of the two products differed with the icescattering model used. One reason for the Tc difference
between SGLI and MODIS is that the effective emission
layers of clouds change with the wavelength used to derive
Tc (e.g., the 10.8 μm brightness temperature will havemore information about the inner cloud rather than theinfrared CO2 band). Another reason is that the CO2 slicing method is more sensitive to high thin clouds than is
the method of the single-channel thermal band used inthis study. Thus, differences in Tc are considered to becaused by the differences in the retrieval method.

(0.88), and 0.94 (0.92) for ocean (land). The ice-cloud optical thickness also showed good correlations between the
SGLI and MODIS products with correlation coefficientsof 0.86 (0.82) over the ocean (land). SGLI gave higher effective particle radii and cloud-top temperatures for ice
clouds than did MODIS owing to the effects of the icescattering model and size distribution function. These results meet the data release criterion defined for the
GCOM-C mission. Standard GCOM-C products havebeen available to the public since data accuracy was validated in December 2018.

Nakajima et al. Progress in Earth and Planetary Science

Conclusions
This paper described the theoretical basis of the algorithms for the GCOM-C SGLI sensor. The SGLI atmospheric group developed the cloudy/clear discrimination
algorithm CLAUDIA and the cloud property retrieval
algorithm CAPCOM. The CLAUDIA-output CCL foreach observation pixel consists of 3 bits (eight gradations). This design allows an ambiguity of cloud coverage for a target pixel to be expressed. The results for
CLAUDIA were evaluated by using the WSIs captured
by ground-based fisheye camera systems. The evaluation
stations were Ny-Alesund (78.93° N/11.86° E), Sapporo(43.08° N/141.34° E), Japan Aerospace Exploration
Agency (JAXA) Tsukuba Space Center (TKSC) (36.07°
N/140.13° E), Meteorological Research institute at
Tsukuba (MRI) (36.06° N/140.13° E), Kumamoto (32.84°
N/130.87° E), Miyakojima (24.74° N/125.33° E), andSyowa Station (69° S/39.59° E). Results showed that theuser’s accuracy and producer’s accuracy were mostly
high at around 90% and the overall accuracy was also
high, ranging from 83 to 100% (average of all sites was
90.5%). Therefore, CLAUDIA had sufficient cloudmasking accuracy and met the threshold for the release ofdata for the SGLI cloud flag product, which is defined tobe the minimum criterion for success of the GCOM-C
mission.
CAPCOM retrieves cloud optical thickness, effective
particle radius, and cloud top temperature for water- andice-phase clouds by using the 1050-nm, 2210-nm, and10.8-μm channels of the SGLI. The outputs were validatedby comparing the retrieved values with the values of the
MODIS standard product MOD06 over ocean and overland at middle and low latitudes (60° N –60° S) from August 22, 2018 to September 14, 2018. The comparison
showed that the correlations of water cloud optical thickness, effective particle radius, and cloud top temperature
were good, with correlation coefficients of 0.88 (0.83), 0.92

Abbreviations
ADEOS-II: Advanced Earth Observing Satellite-II; AVHRR: Advanced Very High
Resolution Radiometer; BI: Brightness index; CAI: Cloud and Aerosol Imager;
CAPCOM: Comprehensive Analysis Program for Cloud Optical Measurement;
CCL: Clear confidence level; CLAUDIA: CLoud and Aerosol Unbiased Decision
Intellectual Algorithm; CLFG: CLoud FlaG; EarthCARE: Earth Cloud, Aerosol
and Radiation Explorer; GCOM-C: Global Change Observation MissionClimate; GLI: Global Imager; GOSAT: Greenhouse Gases Observing Satellite;
IFOV: Instantaneous field of view; IPCC: Intergovernmental Panel on Climate
Change; IR: Infrared; LUT: Look-up table; LWC: Liquid water content;
LWP: Liquid water path; MODIS: Moderate resolution imaging
spectroradiometer; MRI: Meteorological Research Institute; MSI: Multi-spectral
imager; NDSI: Normalized difference snow index; NDVI: Normalized
difference vegetation index; RCP: Representative concentration pathway;
RMSE: Root mean square error; SGLI: Second-generation GLobal Imager;
SI: Sky index; SW: Shortwave; SWI: Shortwave infrared; Tb: Brightness
temperature; TIR: Thermal infrared; TKSC: Tsukuba Space Center; VN: Visibleto-near-infrared; WSI: Whole-sky images
Acknowledgements
The authors are grateful to Dr. Teruo Aoki, Dr. Tomonori Tanikawa, Dr. Masashi
Niwano, and Dr. Sumito Matoba for their contribution in collecting whole-sky
camera images at Sapporo. The skyview camera data for Ny-Alesund and Syowa
Station was provided by Dr. Masataka Shiobara of the National Institute of Polar
Research (NIPR). The skyview measurements at Ny-Alesund and Syowa are performed by the NIPR Arctic Environment Research Center and the Japanese Antarctic Research Expeditions, respectively.
Consent for publications
Not applicable.
Author’s contributions
TYN is a principal investigator (PI) of the JST CREST EMS project and the JAXAGCOM-C science project. He developed CAPCOM. HI developed CLAUDIA. TMN
implemented CAPCOM and CLAUDIA in the JAXA data analysis system. He also
analyzed the early-phase SGLI data. MH is a leader of the JAXA SGLI validationgroup. He performed cloud validation in this paper. HL compiled the singlescattering database for ice clouds. He also contributed to calculating the LUT
for ice cloud retrieval. RH and NT contributed to calculating the LUT for sky
camera image analysis, logistic regression analysis, and analysis of sky camera
images. NI contributed to evaluating the SGLI cloud detection accuracies bycomparing them with sky camera images. AY contributed to collecting thesky camera images at Tsukuba (at the Meteorological Research Institute(MRI)) and Miyakojima. All authors read and approved the final
manuscript.
Funding
This work was supported by JST CREST Grant Number JPMJCR15K4, Japanand by the JAXA GCOM-C science project. This work was partly supportedby the National Natural Science Foundation of China, Grant Number
41771395.
Availability of data and materials
GCOM-C SGLI level-1b and level-2 data will be available via JAXA from
December 2018.

Nakajima et al. Progress in Earth and Planetary Science(2019) 6:52

Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Research and Information Center, Tokai University, 4-1-1, Kitakaname,
Hiratsuka, Kanagawa 259-1292, Japan. 2Meteorological Satellite Center, Japan
Meteorological Agency, 3-235, Nakakiyoto, Kiyose, Tokyo 204-0012, Japan.
3
Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5,
Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. 4Earth Observation ResearchCenter, Japan Aerospace Exploration Agency, 2-1-1, Sengen, Tsukuba, Ibaraki
305-8505, Japan. 5State Key Laboratory of Remote Sensing Science,
Institution of Remote Sensing and Digital Earth, Chinese Academy of
Science, DaTun Road, No. 20 (North), Beijing 100101, China. 6Remote Sensing
Technology Center of Japan, JAXA Tsukuba Space Center Small-sized Satellite
Test Building, 2-1-1, Sengen, Tsukuba, Ibaraki 305-8505, Japan.
7
Meteorological Research Institute, Japan Meteorological Agency, 1-1
Nagamine, Tsukuba, Ibaraki 305-0052, Japan.
Received: 28 January 2019 Accepted: 2 July 2019

References
Ackerman SA, Strabala KI, Menzel WP, Frey RA, Moeller CC, Gumley LE (1998)
Discriminating clear-sky from clouds with MODIS. J. Geophys. Res.
103(D24):32,141–132,157
Albrecht BA (1989) Aerosols, cloud microphysics, and fractional cloudiness.
Science 245:1227–1230
Baum BA, Soulen PF, Strabala KI, King MD, Ackerman SA, Menzel WP, Yang P(2000) Remote sensing of cloud properties using MODIS airborne simulator
imagery during SUCCESS 2. Cloud thermodynamic phase. Journal ofGeophysical Research-Atmospheres 105(D9):11781–11792
Hori M, Murakami H, Miyazaki R, Honda Y, Nasahara K, Kajiwara K, Nakajima TY,
Irie H, Toratani M, Hirawake T, Aoki T (2018) GCOM-C data validation plan forland, atmosphere, ocean, and cryosphere. Trans. JSASS Aerospace Tech.
Japan 16:218–223 https://doi.org/10.2322/tastj.16.218
Inoue T (1987) A cloud type classification with NOAA 7 split-window
measurements. J. Geophys. Res. 92:3991–4000
IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor
M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate
Change 2013: The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge, United Kingdom and New York
Ishida H, Nakajima TY (2009) Development of an unbiased cloud detection
algorithm for a spaceborne multispectral imager. Journal of GeophysicalResearch-Atmospheres 114. https://doi.org/10.1029/2008JD010710
Ishimoto H, Zaizen Y, Uchiyama A, Masuda K, Mano Y (2010) Shapemodeling of mineral dust particles for light-scattering calculations using
the spatial Poisson–Voronoi tessellation. J. Quant. Spectrosc. Radiat.
Transfer 111:2434–2443
Kawamoto K, Nakajima T, Nakajima TY (2001) A global determination of cloud
microphysics with AVHRR remote sensing. Journal of Climate 14:2054–2068
Kuji M, Murasaki A, Hori M, Shiobara M (2018) Cloud fractions estimated from
Shipboard Whole-Sky Camera and Ceilometer Observations between East
Asia and Antarctica. J. Meteorol. Soc. Japan 96:201–214 https://doi.org/1
0.2151/jmsj.2018-025
Letu H, Ishimoto H, Jerome R, Nakajima TY, Labonnote LC, Baran A, Nagao TM,
Sekiguchi M (2016) Investigation of ice particle habits to be used for ice
cloud remote sensing for the GCOM-C satellite mission. Atmospheric
Chemistry and Physics 16(18):12287–12303
Letu H, Nagao TM, Nakajima TY, Matsumae Y (2014) Method for validating cloudmask obtained from satellite measurements using ground-based sky camera.
Applied optics 53:7523–7533 https://doi.org/10.1364/AO.53.007523
Letu H, Nagao TM, Nakajima TY, Riedi J, Ishimoto H, Baran AJ, Shang H, Sekiguchi
M, Kikuchi M (2018) Ice cloud properties from Himawari-8/AHI nextgeneration geostationary satellite: Capability of the AHI to monitor the DC
cloud generation process. IEEE Transactions on Geoscience and Remote
Sensing:1–11. https://doi.org/10.1109/TGRS.2018.2882803

Page 25 of 25

Letu H, Nakajima TY, Matsui TN (2012) Development of an ice crystal scattering
database for the global change observation mission/second generation
global imager satellite mission: investigating the refractive index grid systemand potential retrieval error. Applied Optics 51:6172–6178
Mouri K, Izumi T, Suzue H, Yoshida R (2016) Algorithm theoretical basis
document for cloud type/phase product. Meteorological Satellite Center
Technical Note 61:19–31
Nakajima T, Tanaka M (1986) Matrix formulation for the transfer of solar
radiation in a plane-parallel scattering atmosphere. J Quant Spectrosc
Radiat Transfer 35:13–21
Nakajima T, Tanaka M (1988) Algorithms for radiative intensity calculations inmoderately thick atmospheres using a truncation approximation. J Quant
Spectrosc Radiat Transfer 40:51–69
Nakajima TY, Nakajima T (1995) Wide-area determination of cloud microphysicalproperties from NOAA AVHRR measurements for FIRE and ASTEX regions.
Journal of the Atmospheric Sciences 52:4043–4059
Nakajima TY, Nakajima T, Nakajima M, Fukushima H, Kuji M, Uchiyama A,
Kishino M (1998) Optimization of the Advanced Earth Observing Satellite
II Global Imager channels by use of radiative transfer calculations.
Applied Optics 37:3149–3163
Nakajima TY, Sumi A, Imaoka K, Kikuchi N (2005) On the sciences obtained fromADEOS-II mission. SPIE, Sensors, Systems, and Next-Generation Satellites IX. In:
Meynart R, Neeck SP, Shimoda H (eds) Proceedings of SPIE Vol. 5978. SPIE,
Bellingham, p 597802
Nakajima TY, Tsuchiya T, Ishida H, Shimoda H (2011) Cloud detection
performance of spaceborne visible-to-infrared multispectral imagers. Applied
Optics 50:2601–2616
NOAA/NESDIS/STAR (2010) ABI Aerosol Detection Product. https://www.star.
nesdis.noaa.gov/goesr/docs/ATBD/ADP.pdf. Accessed 12 Apr 2019.
Platnick S, Meyer KG, King MD, Wind G, Amarasinghe N, Marchant B, Arnold GT,
Zhang Z, Hubanks PA, Holz RE, Yang P, Ridgway WL, Riedi J (2017) The
MODIS cloud optical and microphysical products: Collection 6 updates andexamples from Terra and Aqua. IEEE Transactions on Geoscience andRemote Sensing 55(1):502–525
Rossow WB, Schiffer RA (1999) Advances in understanding clouds from ISCCP.
Bull. Amer. Meteorol. Soc. 80:2261–2288
Shiobara M, Yabuki M, Kobayashi H (2003) A polar cloud analysis based on Micropulse Lidar measurements at Ny-Alesund, Svalbard and Syowa, Antarctica.
Physics and Chemistry of the Earth 28:1205–1212
Stamnes K, Tsay SC, Wiscombe W, Jayaweera K (1988) Numerically stablealgorithm for discrete-ordinate-method radiative transfer in multiple
scattering and emitting layered media. Appl Opt 27:2502–2509
Twomey S (1977) The influence of pollution on the shortwave albedo of clouds.
J Atmos Sci 34:1149–1152
Twomey S, Piepgrass M, Wolfe TL (1984) An assessment of the impact ofpollution on global cloud albedo. Tellus 36:356–366
Yabuki M, Shiobara M, Nishinaka K, Kuji M (2014) Development of a clouddetection method from whole sky color images. Polar Science 8:315–326
Yoshimura M, Yamashita M (2013) Contribution of ground-based cloud observation
to satellite-based cloud discrimination. J. Environ. Sci. Eng. A 8:487–493

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.