3.2.3 OTSK13
1

Algorithm Outline
1. Algorithm Code: OTSK13
2. Product Code: SST, QF ST
3. PI names: G-0068 Hiroshi Kawamura
4. Overview of algorithm (Standard level)
The GLI Sea Surface Temperature (SST) algorithm contains the following two process: thecloud detection and the atmospheric correction. The former is a process to find clear, or nocloud-contaminated, pixels in the image. The latter is need to obtain SST of clear pixels fromthe brightness temperatures observed by GLI. Accurate SST retrieval depends on the successof these processes.
As for the cloud detection, the combination of the threshold tests is used to detect clouds. A
pixel, which passes through all the tests, is flagged as clear. The tests need to set coefficientsand thresholds; they are adjusted using the real GLI data after the launch of ADEOS-II.
As for the atmospheric correction, the Multi-Channel SST (MCSST) technique is used. The
radiative transfer model and the match-up dataset are used to determine the coefficients of the
MCSST equation. The methodology follows in that done in OCTS SST retrieval (Sakaida et al.,
1998)

2

Algorithm Description

2.1 Methodology and Logic flow
The whole process is summarized in the following chart (Fig.1). As mentioned above, the GLI
SST products are obtained through the cloud detection and the atmospheric correction. The detail ofeach process will be described later.
As shown in Figure 1, the SST data (SST) and the quality flag data (QF ST) are produced using the GLI data and the climatological SST data. As for the GLI data, the following 10 bands
are used: Band-8(0.545µm), -13(0.678µm), -19(0.865µm), -26(1.24µm), -27(1.38µm), -30(3.7µm),-31(6.7µm), -34(8.6µm), -35(10.8µm), and -36(12µm). The radiances are converted to reflectance,R,for visible and near infrared band (band 8-27), brightness temperature, BT , for thermal infrared band
(band 30-35). CLFLG p (Ackerman’s cloud mask data) is also needed. As for ancillary data, the climatological SST data are needed for the quality control (QC). The specification of QF ST is shown in
the following table (Table 1). QF ST is partly filled in the cloud detection process and the atmosphericcorrection process and is complemented after these processes.

GLI Algorithm Description

Start

GLI L1B/L2A data

Input image and ancillary data
Climatological
SST

Conversion of the VNIR and SWIR band radianceto the reflectance

Conversion of the MTIR band radianceto the brightness temperature

Cloud detection

Atmospheric correction(SST calculation by MCSST method)

Complement of quality flag

Output products

SST,QF_ST

End

Figure 1: The flowchart of the whole process of OTSK13.

Table 1: The specification of the GLI SST flag data.
BIT FIELD

Description Key

Result1

Land/Sea

0=Sea/1=Land

2

Cloud

0=Clear/1=Cloudy3

Lack of observation

0=No/1=Yes

4Large emission angle 10=No/1=Yes

5Out of valid range 20=No/1=Yes

6

Day/Night

0=Day/1=Night7

Sun glint

0=No/1=Yes

8Forward tilt

0=No/1=Yes

9Backward tilt

0=No/1=Yes

10-11

Ackerman’s four cloud class

00 = cloudy
01 = probably cloud
10 = confident clear
11 = high confidence clear

12-16

Spare

1

The threshold of scan angle is 55◦ .

2

The threshold of the difference between GLI SST and climatological SST is standard
deviation of climatological SST.

GLI Algorithm Description

2.2 Cloud detection algorithm
The framework of cloud detection algorithm is to apply up to some threshold tests and then to
identify a pixel as cloud-free when all tests prove negative. Figure 2 shows processing flow of thecloud detection. We use “scheme” to represent the combination of cloud detection tests. Scheme3 (nighttime) cloud detection algorithms are used when the solar zenith angle θ suz is greater thanθa0 = 86.5◦ .
From Previous Process
image and ancillary data input
reflectance and brightness temperature conversion
Parameter file
Set Parameters:
thresfolds, coefficients
upper limit of sun zenith angle  upper limit of reflection angle

calculation of reflection angle 





No

Yes
No

 

Yes
Scheme-2
For daytime,sunglitter case

Scheme-1
For daytime,no sunglitter case

Scheme-3
For nighttime

Go to the next process

Figure 2: The processing flow of the cloud detection.

In the daytime, scheme-1 algorithms apply to the exterior of the sunglitter; scheme-2 are used tosunglitter region. To specify the sunglitter region, the reflection angle, θr , is used. θr is calculated by,cos θr =cos θ suz + cos θ saz,
2 cos ω(1)

where φ saz is satellite zenith angle. ω can be derived by,cos 2ω = cos θ saz cos θ suz− sin θ suz θ saz cos(φ sua − φ saa ),(2)

where φ sua and φ saa are the azimuthal angles of the sun and the satellite, respectively. This geometry
GLI Algorithm Description

Sun

z

N
θsat

Satelliteθsun θ
ωωsurface facet

x

φsun

φsat

y

Figure 3: The reflection geometry between the sun, the satellite, and the surface facet.

is shown in Figure 3. The limit value θr0 for specifying sunglitter region is 30 degree. It is determined
empirically as will be explained later.
Table 2 shows the cloud detection tests used in GLI SST. These tests are classified to four groups
as follows.
The first group is called as a gross cloud test. A pixel is flagged as cloudy when the brightness
temperature of band-35 (10.8µm) is lower than the thresfold value. Tanahashi et al. (2000) used
quadratic function of latitude as the thresfold value for S-VISSR/GMS. We adopted their forms withcoefficients derived from GLI data empirically. Constant thresfold is used in conjection with quadratic
function to identify clouds in high latitude regions.
The second tests use visible and near-infrared reflectance (R). These tests are widely used for
retrieving SST from visible and infrared radiometer (e.g., Saunders and Kriebel, 1988; Ackerman
et al., 1997; Sakaida et al., 2000). Reflectance ratio tests utilize the difference in reflection fromcloud versus earth surface in wavelength above and below 0.72µm (Ackerman et al., 1997). Early
studies used the 0.87µm reflectance divided by 0.66µm reflectance (R0.87 /R0.66 ). Based on GLI data,
it is found that R0.865 /R0.545 (band-19 reflectance divided by band-8 reflectance of GLI) is more
efficient for classifying cloud and ocean than R0.865 /R0.678 (band-19 reflectance divided by band-13
reflectance). Figure 4 shows the scatter diagrams of R0.865 /R0.545 and R0.865 /R0.678 from GLI data. It
reveals that the former can distinguish the clear ocean from cloudy even in the sunglitter region. In
order to avoid the potential for masking clear pixels in sunglitter region, the linear theresfold function
and sunglitter region (θr < 30degree) are defined empirically. Reflectance test for band-19 (R.865 ) isused additinally and the thresfold is determined as a function of reflection angle empirically.
The third tests are brightness temperature difference (BTD) tests. The test using BTD between
11µm(band-35:10.8µm) and 12µm (band-36) is widely known as split window method, which isuseful for detecting relatively thin clouds. It is known that the BTD depends on the brightness temperature of 11 µm (BT 11 ). Saunders and Kriebel (1988) defined the thresfold as a function of satellite
zenith angle and BT 11 for AVHRR/NOAA. Stowe et al. (1999) suggested the 5-th order function of
BT 11 as the thresfold for cloud classfication from AVHRR/NOAA. Based on these reserches and the
GLI data, the thresfold function for GLI is determined as exponential function of BT 10.8 (band-35)
empirically. In addition, to avoid the striping noise and masking the frontal region, BTD is averaged
GLI Algorithm Description

Table 2: Cloud detection tests for GLI-SST. The tests checked by ◦ are used in each scheme. Scheme1: Daytime, outside the sun glint region. Scheme-2: Daytime, in the sun glint region. Scheme-3:
Nighttime. BTα :Brightness temperatures at αµm, Rα :reflectance at αµm, φ: latitude, θr : reflection
angle, ∇mn : Max-Min difference in 3x3 box, ∇mv : Max-Center pixel value difference in 3x3 box.
Overline on BT 10.8 − BT 12 denotes the mean in 3x3 box except for maximum value. FR: Full Resolution product, LR: Low Resolution product.
Group
Gross test
Reflectance test

Cloud detection test

1

2

3

BT 10.8 ≤ −0.007φ2 + 283

◦

◦

◦

BT 10.8 ≤ 269.15

◦

◦

◦

R0.865 /R0.545 > 1.05 − 0.019θr

×

◦

×

R0.865 /R0.545 > 0.48

◦

×

×

R0.865 > 30.0 − .50θr

×

◦

×

R0.865 > 15.0

◦

×

×

◦

◦

×

BT 8.6 − BT 10.8 > −0.5

◦

◦

◦

BT 10.8 − BT 12 > exp (0.176BT 10.8 − 50.5) + 1.45

◦

◦

◦

BT 10.8 − BT 12 > 4.3

◦

◦

◦

1.5BT 3.7 − 2.5BT 10.8 + BT 12 > 3.5

×

×

◦

1.5BT 3.7 − 2.5BT 10.8 + BT 12 < −2.5

×

×

◦

0.6BT 3.7 − 0.6BT 8.6 + BT 10.8 − BT 12 < 1.8

×

×

◦

BT 3.7 − BT 12 ≤ exp(0.0345BT 10.8 − 9.375) + 1.

×

×

◦

∇mv BT 10.8 > 1.5

◦

◦

◦

∇mn R1.24 > 2.5

◦

◦

×

∇mn BT 3.7 > 1.25(FR) 2.0 (LR)

×

×

◦

R1.38 > 0.2
BTD test

Uniformity test

SchemeandR0.865 /R0.545 > 0.4

and∇mn (BT 10.8 − BT 12 ) > 2.5

over 3x3 pixel box except for maximum value. The test using BTD betwenn 8.6µm (band-34) and10.8µm (band-35) is also useful to detect cloud. It is known that this BTD indicates ice cloud (Strabara et al., 1994). Since the dependency on the BT 11 is weak, the thresfold of this test is determined as
constant value. For nighttime observation, BTDs between 3.7µm (band-30) and other bands are used
as cloud detection tests. Stowe et al. (1999) used BTD between 3.7µm and 12µm with a thresfold
function of BT 11 for nighttime test. Sakaida et al. (2000) and Guan et al. (2003) used the combinationof BT 3.7 , BT 11 and BT 12 to detect cloud in nighttime. The combination of BT 3.7 , BT 8.6 , BT 10.8 andBT 12 is useful for detectinl low-level cloud in high latitude regions. The coefficients of these tests areadjusted to GLI data.
The forth tests are uniformity tests, which is based on the fact that brightness temperatures andreflectances on clear ocean are spatially homogeneous compared with cloudy regions. While these

GLI Algorithm DescriptionReflectance Ratio (ch.19/ch. 13)

Reflectance Ratio (ch.19/ch. 8)

1.25

Cloud

1.00

0.75

0.50
Ocean
0.25

1.25

Cloud

1.00

0.75
Ocean
0.50

0.25

Total: 1.1x1008 Pixel
Max: 276332 Pixel
40
50
60

0.00
0

10

20

30

Total: 1.1x1008 Pixel
0.00
70

0

10

Reflection Angle
0

Figure 4:

20000

40000

60000

80000

20

30

Max: 386138 Pixel
40
50
60

70

Reflection Angle
100000

0

20000

40000

60000

80000

100000

Scatter diagram of reflectance ratio and reflection angle based on GLI data. (Left)

R0.865 /R0.545 (band-19 reflectance divided by band-8 reflectance), (Right) R0.865 /R0.678 (band-19 reflectance divided by band-13 reflectance). Color shows the frequency of appearance.

tests are very powerful, its reliability is low in the coastal and frontal regions. Since the tendency
for cloudy condition is colder than clear condition, uniformity of BT 10.8 (band-35) is confirmed by
the brightness temperature difference between maximun in 3x3 pixel box and the center pixel value,
which is used in Rossow and Garder (1993). In addition, the uniformity of BTD (BT 10.8 − BT 12 )is combined to avoid masking the frontal region as cloud. The uniformity of BTD and reflectanceare calculated by maximum and minumum difference in 3x3 pixel box. For nighttime, uniformity of
BT 3.7 (band-30) is used. Thresfold for BT 3.7 uniformity test is changed as FR (full-resolution) andLR (low-resolution), since noise at GLI band-30 is so large that the spatial homogeneity of BT 3.7 inclear region of LR is less than that in FR. These thresfolds are determined empirically.

2.3 Atomospheric correction
0.7

In the GLI SST product, the MCSST method is used toretrieve the SST in clear pixels. The MCSST equation is the

RMSE (K)same form as one used in OCTS/ADEOSS SST algorithm(Sakaida et al., 1998) as follows:
SST= a1 + a2 BT 10.8 + a3 BT 10.8 − BT 12 + a4 BT 10.8 − BT 8.6+a5 BT 10.8 − BT 12 (1/ cos(θ saz ) − 1.0)

(3)

+a6 BT 10.8 − BT 8.6 (1/ cos(θ saz ) − 1.0)
where θ saz is a satellite zenith angle.
The above equation contains BT 8.6 , which is not observedby the split-window bands. The reason of this is that theuse of this band is possibly advanced in the accuracy of SST
GLI Algorithm Description

0.6
2 3 4 5 6 7 8 9 10
Array size

Figure 5: Residual of regression as afunction of array size where brightness
temperature differences are averaged.

estimation according to the study of OCTS and the numerical simulation (Moriyama et al., 1999). To
reduce the noise effects, brightness temperature difference terms, BT 10.8 − BT 8.6 and BT 10.8 − BT 12 .
are averaged for NxN pixel array. The following chart (Fig. 6) shows the SST calculation processwith the detail of this averaging.
Using GOES data, Wu et al. (1999) pointed out that the optimal box size of averaging brightness
temperature differences is as small as 3x3 array for representative of a buoy measurement, since the
RMSE (root-mean-square-error) of satellite-estimated and buoy-measured SSTs is minimum using
3x3 array averaging. From GLI data, RMSE between GLI-estimated and buoy-measured SST (observation points are shown in Figure 8) monotonically decreases with array size (Fig.5). Since RMSE
difference of 7x7 array and 9x9 array is small, it is suggested that 7x7 array is optimal for GLI SST.
Figure 6 shows the chart of SST calculation process.
From Previous Process
image, scan geometry and cloud mask
Parameter file
Set Parameters:
coefficients of MCSST equationarray size (N1, N2)

SST calculation process for a pixel,PC,
which is a center of NixNi(i=1,2) array
Yes

Is Pc clear?

No

Calculation of BTD 34-35
average using only clear
pixels in N1 x N1 array

Calculation of BTD 35-34
average using all pixels
in N1 x N1 array

Calculation of BTD 35-36
average using only clear
pixels in N2 x N2 array

Calculation of BTD 35-36
average using all pixels
in N2 x N2 array

SST calculation

Go to the next process

Figure 6: The SST calculation process with the detail of this averaging.

The coefficients of Eq.(3) are derived by the pre-launch numerical simulation (see Section A).
The MCSST equation (pre-launch) is written as,
SST= 2.276 + 0.9966BT 10.8 + 1.946BT 10.8 − BT 12 − 0.2106BT 10.8 − BT 8.6+0.507BT 10.8 − BT 12 (1/ cos(θ saz ) − 1)(3-a)+0.2481BT 10.8 − BT 8.6 (1/ cos(θ saz ) − 1)
After the launch of the ADEOS-II, the match-up data are collected. Using the match-up data, the
GLI Algorithm Descriptioncoefficients of Eq.3 are also derived. The equation is written as,
SST= −2.35069 + 1.019241BT 10.8 + 1.863587BT 10.8 − BT 12 − 1.11811BT 10.8 − BT 8.6+1.020815BT 10.8 − BT 12 (1/ cos(θ saz ) − 1)(3-b)+0.272058BT 10.8 − BT 8.6 (1/(cos θ saz ) − 1)
In the calculation of this equation coefficient, the match-up data in 3/19-22 and 4/2-7 are used. The
number of the match-up data is 546. In the match-up data used to derive Eq.(3-b), the time difference
between the satellite and in-situ observation is within 2 hours.
Using the same data that Eq.(3-b) is derived, RMSE and bias are calculated for the equations (3-a)
and (3-b). The RMSE of Eq.(3-b) is 0.56K. For Eq.(3-a), the RMSE is 0.66K and the bias is -0.16K.
The RMSEs and biases of the equations (3-a) and (3-b) are also calculated using the data independentof the coefficient calculation. Using the 1732 match-up data in 5/28-31, Eq.(3-a) has the RMSE of
0.69K and the bias of -0.16K and Eq.(3-b), the RMSE of 0.62K and the bias of 0.06K. Therefore, we
adopt Eq.(3-b) as the equation for the GLI SST retrieval.

3

Validation
For validating the SST product from OTSK13, mutchup data of GLI-estimated SST and buoy-measured SST are used. Buoy observation is quality checked using climatological SST. The mutchup
is collected from 22 days (randomly selected from March to September), if time difference between
buoy and GLI observations is less than 5 hours. Figure 7 shows that the GLI SST residual decreases
with an increasing number of clear pixels around the buoy. This is consistent with the fact that thecloud contamination and atmospheric correction failure are occasional near cloud area. Through thequality control in which more than 8 pixels in 3x3 array around the point are clear, number of mutchup
is 4225.
2.5

RMSE (K)

2.0

1.5

1.0

0.5
0

1

2

3

4

5

6

7

8

9

10

Number of clear pixels in 3x3 box

Figure 7: RMSE as a function of the number of clear pixels in 3x3 box.

Figure 8 shows the mutchup measurements points and scatter diagram of GLI and buoy SST.
The bias and RMSE of the difference between GLI and buoy SST are -0.08K and 0.672K, respecGLI Algorithm Description

tively. They are -0.06K and 0.669K for daytime, while they are -0.17K and 0.69K for nighttime. The
seasonal or temporal change of statistical features is not found.
310

60˚
40˚

Day: 3/19, 3/20, 4/8, 5/28, 6/4, 6/5, 6/10,
6/19, 6/20, 6/30, 7/2, 7/5, 7/10,
7/15, 7/19, 7/20, 7/25,
7/30. 8/4, 8/10, 8/19, 9/19

300

GLI SST

20˚
0˚
-20˚

290

-40˚
-60˚

280

0˚ 30˚ 60˚ 90˚ 120˚ 150˚ 180˚ 210˚ 240˚ 270˚ 300˚ 330˚ 360˚
270

280

290

300

270
270

280

290

Buoy SST

300

310

Figure 8: (Left)Location of the mutchups. Color denotes the buoy measurement SST. (Right) Comparison of GLI estimated SST and buoy measurements.

GLI Algorithm Description

Appendix
A Physical and Mathematical aspects of the algorithm based on radiative transfer model
The coefficients of the split-windows equation will be defined from the multivariative regression
using the match-up dataset which has the satellite detected brightness temperature and the scan geometry collocated with the sea surface temperature which are measured by buoys, ships, aircrafts andso on. For the initial definition of the split-window formula, it is desireable to define the function typewhich can make the stable estimation under the real observation.
For the Global mapping, the error of the estimated SST must be independent from the observedbrightness temperature. The following is the mathematical expression of this concept,

δT

=

X df
dT i

δT i

(4)

where δT , f and δT i are the error of the estimated SST, the split-window function and the observation
error of the brightness temperature of ith spectral channel respectively. The partial derivative of the
split-window function is constant when the split-window function is linear, so that the estimation
error of the SST will be independent from the brightness temperature. From this reason, the GLI
split-window function is defined as the linear function.
For the function type definition, the numerical simulation, which is based on the model atmosphere/surface conditions, is described as follows.
• Model atmosphere: 1990 Monthly mean NCEP dataset, Longditudal averaged over 10 [deg.]
latitude• interval (12 x 18 = 216)
• Other gas amount: NRL model• Model SST: T(0) + 1 [K] + normal random variable with 5/3 [K] standard deviation.
• observation zenith: 0, 30, 60 [deg.]• Radiative transfer code: MODTRAN 3.5• Computation item: brightness temperature at GLI 34, 35, 36channels.
• Observation error: Normal random variable with 0, 0.1, 0.2 [K] standard deviation.
• Aerosol type: Rural• Aerosol amount: Surface visibility: 23 [km] (@550nm)• Model emissivity: 1.0, 0.99, 0.98, 0.97
GLI Algorithm Description

From these conditions, the brightness temperature at GLI channels 34, 35 and 36 are computed.
By using the above dataset, the multivariative regression analysis based in the following formulae are
made.
SST

= aBT 10.8 + b(BT 10.8 − BT 12 ) + f

(5)

SST

= aBT 10.8 + b(BT 10.8 − BT 12 ) + c(BT 10.8 − BT 8.6 ) + f

(6)

SST

= aBT 10.8 + b(BT 10.8 − BT 12 ) + c(BT 10.8 − BT 8.6 )(7)+d(BT 10.8 − BT 12 )(1/ cos(θ saz ) − 1.0) + f
SST= aBT 10.8 + b(BT 10.8 − BT 12 ) + c(BT 10.8 − BT 8.6 )+d(BT 10.8 − BT 12 )(1/ cos(θ saz ) − 1.0) + e(BT 10.8 − BT 8.6 )(1/ cos(θ saz ) − 1.0) + f (8)The RMS error of the estimated SST under the no observation error case from the above formulae arelisted below
Eq.

(5)

(6)

(7)

(8)

RMSE(K)

0.559

0.312

0.424

0.291

The Equation (8) show the most accurate result, so that this function type (5 terms formula) isused for the GLI split-window formula.
As the preliminary set of the coefficients are defined from the following scheme.
1. The coefficient sets are defined from the common observation error and the surface emissivity.
2. Compute the RMS error of the estimated SST from the all brightness temperature data basedon each coefficient set.
3. The coefficient set which minimize the RMS error is selected as the preliminary set.
Bias and RMS error from the each coefficient set are listed below.


0.97

0.98

NEdT

0.0

0.1

0.2

0.0

0.1

0.2

RMS(K)

0.634702

1.0413

1.347080.56707

0.816566

0.969785



0.99

1.00

NEdT

0.0

0.1

0.2

0.0

0.1

0.2

RMS(K)

0.582015

0.743614

1.208460.659112

1.0384

1.25814

From the RMS error, the coefficient set for the condition, in which the surface emissivity is 0.99
and the observation noise is 0, are selected. The preliminary coefficient set is as follows. a = 0.9966,b = 1.946 , c = -0.2106, d = 0.5070, e = 0.2481, f = 2.276

GLI Algorithm Description

B

Variables in parameter file

B.1 Cloud detection algorithm
The cloud detection tests shown in Table 2 are rewritten in the program as following inequalities:X

α j χ > γaandX

β j χ > γb ,(9)where α j and β j are the coefficients, γa and γb are the thresholds, χ j are variables. α j , β j , γa and γb
are defined in parameter file as XALPHA, XBETA and XGAMMA. Table 3 shows the description ofvariables (data/aux/glisst FR.prm).
Table 3: The variables for the cloud detection. Uniformity A and B are indices to measure the spatial
variability. The 19th, 20th and 21th variables are used as a3 exp(a1 BT 10.8 +a2 ). Overline on brightness
temperature difference (No.24 and No.25) denotes the mean in 3x3 box except for maximum value.
No.

Description

No.

Description

1

Uniformity test A

15

cosine of sun zenith angle cos θ suz

2

Uniformity test B

16

1 minus secant of satellite zenith angle

3

Reflectance of band-8 (R0.545 )4Reflectance of band-13 (R0.678 )5

Reflectance of band-19 (R0.865 )6

Reflectance of band-26 (R1.24 )18

Ackerman’s Cloud Flag

7

Reflectance of band-27 (R1.38 )19

coefficient for exp(BT 10.8 ) equation (a1 )8Brightness temperature of band-30 (BT 3.7 )20

coefficient for exp(BT 10.8 ) equation (a2 )9Brightness temperature of band-31 (BT 6.7 )21

coefficient for exp(BT 10.8 ) equation (a3 )10Brightness temperature of band-34 (BT 8.6 )22

Reflection angle θr

11

Brightness temperature of band-35 (BT 10.8 )23BT 10.8 × BT 1212Brightness temperature of band-36 (BT 12 )24

BT 10.8 − BT 12

13square of Latitude (φ2 )25

BT 8.6 − BT 10.8

14

Longitude

1 − sec θ saz
17

product of reflection angle and R0.545
θr × R0.545

Band used in spatial uniformity tests is defined as XUNI CHN in the parameter file. XUNI CHN A
and XUNI CHN B are for brightness temperature or reflectance tests, and XUNI CHN A0 and XUNICHN B0 are for brightness temperature difference tests. The type of uniformity tests are selected by
XUNI TYP parameters from following lists.

GLI Algorithm Description

Table 4: The type of uniformity tests for R (reflectance or brightness temperature) at i, j pixel. il andjl mean the line and pixel lag (il = −1, 0, 1 and jl = −1, 0, 1). Rmax and Rmin are the maximum andminimum of R in 3x3 box, respectively.
parameter

description

equation1

Difference of maximum and minimum in 3x3 box

R Max − R Min

2Standard deviation3

Spatial gradient

4Laplacian5Difference of maximum and the value at cen-

R Max − Ri, j

6

ter pixel in 3x3 box
Difference of minimum and the value at cen-Ri, j − R Min

7

ter pixel in 3x3 box
Mean in 3x3 box except for maximumP2
2
il, jl Ri+il, j+ jl /9 − ( il, jl Ri, j /9)

P

Maximum component of ∇Ri, j|∇2 Ri, j |P( il, jl Ri+il, j+ jl − R Max )/8

B.2 MCSST calculation
Table 5 explains the variables for MCSST equation in parameter file, in which these variables are
separately defined for each regions (nighttime, daytime/sunglitter, daytime/out of sunglitter).
Table 5: Variables for MCSST calculation defined in parameter file.
Column 1
XMCSST VAR
XARRAYXCOEFF

Description
The number of the terms in the MCSST equation

Value in Ver. 1.0
6array size to average the BTD.
XARRAY1 for BTD of band-34 and band-35

77

XARRAY2 for BTD of band-35 and band-36

77

coefficients for MCSST equation.
XCOEFFα (α = 1, ..., 6) corresponds to aα in Eq. (3).

GLI Algorithm Description(same as in Eq. (3-b))References
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GLI Algorithm Description