GCOM-C/SGLI Land Atmospheric Correction Algorithm
Hiroshi Murakami,
JAXA/EORC
March. 2020 (v2.000: Main revisions of Ver. 2 is underlined)1

Overview of Algorithm
Definition of GCOM-C atmospheric corrected land surface reflectance (RSRF) product1.1•

Surface reflectance of solid or liquid structures on the ground which includes canopy, snow, and water.
Light scattering and absorption of atmospheric molecules and aerosol particles are corrected from clear
sky top of atmosphere (TOA) reflectance.

•

Directional dependency is not corrected for the snapshot (one-day) product and corrected for the 8-day
and monthly products.

•

Slope effect correction, which normalizes solar irradiance change due to the land-surface slope, will be
investigated in the post-launch evaluation.
Development strategy (discussed in the atmospheric correction workshop in Sep. 2012)

1.2•

The algorithm is mainly developed by JAXA/EORC through integration of knowledge about theatmosphere (aerosol scattering and absorption, and radiative transfer modeling), the land surface(spectral reflectance and bidirectional reflectance), and cloud (and snow cover) area detection incollaboration with PI groups.

•

The algorithm considers consistency (e.g., candidate aerosol model and BRDF) with downstream
products (land and atmosphere products) and sensor calibration characteristics including vicarious
calibration by using other satellite sensors and in-situ observation, and calibration corrections.

•In-situ validation data (BRDF, solar irradiance, and aerosols) will be obtained by land-PIs (Honda,
Chiba Univ., Nasahara, Tsukuba Univ., and so on). Atmospheric parameters will be obtained
simultaneously as much as possible in collaboration with the atmosphere PIs.
Function and characteristics

1.3•

Output land surface reflectance by subtracting atmospheric molecules and aerosols from TOA radiance(reflectance) in the daytime clear sky areas.

•

Reflectance is one at the satellite-solar geometric condition by consideration of land-surface BRDF byusing multiple-day’s data.
Interface in the GCOM-C processing flow

1.4•

Multiple day (32 days; the day number will be evaluated after the launch) precise geometric corrected
tile-mapped TOA radiance (reflectance) data (LTOAQ) is used as the input.

•

Dynamic look-up table (LUT) is used to use previous day’s BRDF estimation for the aerosol correction.

•

Output surface reflectance data (RSRF) is averaged for 8-day and monthly in the level-3 processing.

1

Vegetation

250m TOA radiance (LTOA)
32daysNDVI, EVI, Shadow index
FAPAR, LAI

Land atmospheric correction

Direction & Biomass

Land surface reflectance (250m)

Above-ground biomass (1km)
Veg. roughness index (1km)Dynamic LUT

Thermal analysis
Land surface temperature

250m tile synthesis
8-day

Cloud properties
month
1/24deg binning
1-day

8-day

Aerosol properties by NP
Aerosol properties by P

monthFig. 1 Operation flow around the GCOM-C Land Atmospheric Correction

New development

1.5•

The land atmospheric correction is the new function from ADEOS-2/GLI in which aerosol was notcorrected. This BRDF estimation scheme using the multiple day data has been developed by several
studies by MODIS (Lyapustin et al., 2012) and higher-resolution sensors (Hajj et al., 2008, Hagolle et al.,2008), however it is not usual being the standard product because of estimation instability and longprocessing time.
Development history

1.6•

2012/9: development strategy was discussed in the atmospheric correction workshop•

2015/1/13：Deriver the first version code•

2015/07/20: bug fix in lut_d/RSRF/*BRDFQ output and observation time handling•

2015/09/03: bug fix in dataset name: Sensor_***_TI -> Sensor_***_IR•

2016/01/29 (v005): change NG to Poor when n_in==0•

2017/01/31 (v006): revision of the multi-day estimation module, and reduce the processing time,
including correction of center-wavelength shift and slope effect•

2018/07/31 overall revision and bug-fix of main code:• tuning of cloud detection thresholds• lut is made by pstar4; ALOS DEM files is added for LTOAF• adjustment to the post-launch LTOAQ and LTOAF values and format• processing line-block is changed to 160 lines• saturation recovery & saturation recovery flag
• Obs_time_IR is deleted as same as LTOAQ/K
2

•

2018/10/23 (1.00 000.00)• revision of NDVI->ref relationship by Fuji Hokuroku in 2016-2018• revision of the conversion weights• Byte size of BRF1,2 coefficients of lud_d file was changed from 2byte to 1byte (to reduce thelut_d files)• Attribute: Photosynthetically Active Radiation -> Daily Photosynthetically Active Radiation• Attribute: Surface reflectance of VN08 co-registered for VN08 -> Surface reflectance of VN08
co-registered for PI01
• Attribute: Surface reflectance of VN11 co-registered for VN11 -> Surface reflectance of VN11
co-registered for PI02
• allow LTOA version -0~-5 as the input files•

2018/11/06 (1.01 001.00) [Ver.1]• Set error values for the saturation of VN04 and VN06
• if(slopr0(nb,k).ne.slopr(nb,k)) then-> if((slopr0(nb,k).ne.slopr(nb,k)).or.(offsr0(nb,k).ne.offsr(nb,k))) then•

2019/06/17 (1.01 002.00)• JMA ozone data is used•

2019/08/27 (1.01 002.01)• wrong explanation, " (from the earth surface)" is deleted in Data_description of zenith angles•

2020/01/04 (2.00 000.00)• Flag definition is revised including the additional tests for the polarization channels• BRDF of VN08P and VN11P is calculated with PI01 and PI02 respectively• bug fix: rea->abs(rea) for BRF kernel calculation• hotspot of BRF function is moderated (hspot=1.0 -> 5.0)
• vical coeffs are revised to adjust with the Ver.2 L1B which is consider the temporal gain change
derived from the moon calibration•

2020/02/27 (2.00 000.01)• bug fix: uninitialized parameter "vgi" in the subroutine "brdf_multiday"• file list is input from the "main" to the "brdf_multiday"• Sortwave irradiance (/Image_data/SWR) is stored in output file• revise ocean surface reflectance (rs2) recovery for the "cal_par"•2020/03/25 (2.00 000.02) [Ver.2]• add nrt processing with today’s lut_d ($3="-2")• bug fix: add brfk recovery under clouds for reflectance of the daily processing• Absorption calculation for SWR is revised in "cal_par"• Set input lut_d information in attribute of /Processing_attributes/LUT_file• 2018/12/31 lut_d is used for the first day 2018/01/01 data
• use input days for Processing_result (ninlst>ndyl1*0.9;qidx='Good';else;qidx='Fair')
3

2

SGLI Land Atmospheric Correction Algorithm
Algorithm flow

2.1•

Input precise geometric corrected TOA reflectance data (LTOA) of 32 (variable) days. Both 250-m
LTOA (LTOAQ) and 1-km LTOA (LTOAK) can be inputted (LTOAK input is not for the standard
processing)•

Previous day’s BRDF LUT (dynamic LUT) is inputted, and current day’s BRDF LUT is outputted.

•

Current day land surface reflectance (RSRF) is produced by the current day LTOA and current day
BRDF LUT.

•

The outputted RSRF is averaged for 8-day and monthly in the level-3 processing.

•The 8-day data is used for the atmospheric product processing

Figure 2 Overall flow of SGLI land atmospheric correction

2.2

Radiance to reflectance
TOA radiance LTOA is converted to TOA reflectance TOA for each SGLI channel ()TOA() =  LTOA() d2 / (F0() cos(0))where d is distance between the sun to the earth (1: 1AU), F0() is SGLI-channel weighted solar
irradiance (calculated by using Thuillier 2003), 0 is solar zenith angle.
Table 1 SGLI-band weighted center wavelength (c) and solar irradiance (F0)
4

band
VN01
VN02
VN03
VN04
VN05
VN06
VN07
VN08
VN09
VN10
VN11
PL01
PL02
SW01
SW02
SW03
SW04
TI01
TI02

2.3

c (nm)F0 (Wm-2m-1) Min c (nm) F0 (Wm-2m-1) Max c (nm) F0 (Wm-2m-1)
1092.14379.801093.97380.241090.67
1712.15412.111710.45412.681712.86
1898.32
442.96
1891.31443.57
1906.811938.46489.601937.76490.391941.65
1850.96529.471850.33530.091852.37
1797.13565.761797.75
566.561796.60
1502.55
671.731503.26
672.611500.801502.30
671.891502.87672.671500.65
1245.45762.351247.82763.581243.79956.34866.11955.99867.48956.52956.62866.40956.29867.90956.711503.59671.891503.60
673.101499.84956.92865.85956.68866.63957.13646.54NANA
NA
NA
361.24
NA
NA
NA
NA
237.58
NA
NA
NA
NA
84.25
NA
NA
NA
NA
0.00
NA
NA
NA
NA
0.00
NA
NA
NA
NA

380.03412.51443.24489.85529.64566.15672.00672.10763.07866.76867.12671.89866.181054.991385.35
1634.512209.4810792.98
11956.28

Gas absorption correction
Absorption coefficients of H2O, O3, and O2 are calculated by PSTAR4 (Ota et al., 2010) by thefollowing equation.

TOA()’ =TOA() / (tg()am)
tg()=exp{ − ( koz0() +koz1()(oz−oz0) ) (oz−oz0)− ( kwv0() +kwv1()(wv−wv0)) (wv−wv0)− ( kox0() +kox1()(P−P0)) (P−P0) }am=1/cos()+1/cos(0)oz, wv, and P are column ozone (DU), column water vapor (mm), and relative surface pressure(1.0: 1013.25hPa). oz0=343.79 DU, wv0=14.186 mm, P0=1013.25 hPa.  is satellite zenith angle.
Table 2 Gas absorption coefficients of SGLI channels
gas

kwv

kox
(O2)

Channel
VN01
VN02
VN03
VN04
VN05
VN06
VN07
VN08
VN09
VN10
VN11
PL01
PL02
SW01
SW02
SW03
SW04
VN01
VN02

c min01.5445E-06
9.4948E-07
3.1314E-05
1.0517E-05
1.6059E-05
1.1847E-04
5.9118E-05
5.7061E-05
2.1217E-06
8.8784E-05
8.4187E-05
4.7346E-05
8.3032E-05
4.0931E-05-2.5585E-01
4.3975E-03
1.7986E-02
1.6224E-03
4.0746E-05

10.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
1.4719E+00-2.8813E-03-1.1061E-02
0.0000E+00
0.0000E+00

c max
a
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-0.32
0.06
0.08
0.00
0.00

0
1.48620E-06
1.00540E-06
3.20730E-05
1.02670E-05
1.79770E-05
1.25530E-04
5.21940E-05
5.15330E-05
1.39310E-06
7.31440E-05
6.97610E-05
4.21620E-05
7.26590E-05

10.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00

a
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

1.62430E-03
4.28740E-05

0.00000E+00
0.00000E+00

0.00
0.00

5

koz

2.4

VN03
VN04
VN05
VN06
VN07
VN08
VN09
VN10
VN11
PL01
PL02
SW01
SW02
SW03
SW04
VN01
VN02
VN03
VN04
VN05
VN06
VN07
VN08
VN09
VN10
VN11
PL01
PL02
SW01
SW02
SW03
SW04

4.5989E-04
2.1982E-04
1.1559E-03
5.3377E-03
1.1843E-03
1.2444E-03
3.0435E-02
4.3529E-05
4.3813E-05
1.3550E-03
4.2436E-05
8.5098E-03
4.0890E-04
4.9989E-07
6.8205E-08
9.0074E-09
2.4232E-07
2.9846E-06
2.0569E-05
6.5299E-05
1.1405E-04
4.2992E-05
4.2845E-05
6.7584E-06
1.9868E-06
1.9547E-06
4.2834E-05
2.0130E-06
8.0493E-08
3.5094E-09
0.0000E+00
0.0000E+00

0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
2.8313E-01
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00
0.0000E+00

0.00
0.00
0.00
0.00
0.00
0.00
-0.60
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

4.83160E-04
1.77910E-04
1.16790E-03
5.81480E-03
1.72480E-03
1.73980E-03
3.13240E-02
4.53640E-05
4.74650E-05
2.22670E-03
4.23090E-05

0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
2.85120E-01
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00

0.00
0.00
0.00
0.00
0.00
0.00
-0.60
0.00
0.00
0.00
0.00

7.68290E-09
2.59410E-07
3.06890E-06
2.08290E-05
6.61650E-05
1.15180E-04
4.22160E-05
4.21660E-05
6.65850E-06
1.83670E-06
1.79090E-06
4.17610E-05
1.92160E-06

0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00
0.00000E+00

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Cloud detection
Cloud detection algorithms of GCOM-C algorithms are developed by sharing knowledge of the cloudoptical properties mainly studied by the atmosphere group, and implemented in the each algorithm.
Simple decision tree will be used for the first version of the land atmospheric correction algorithm.
2.4.1 Decision tree
Following decision tree is applied for solar zenith angle 0 < 76 degrees.
if( ( (rc443<rt443) & (rc8680.08) & (sst>-5.0) & (btd<5.0) ) & ( (elv<1) | ( (elv<200) &(vgi<0.1) ) ) ) then→ clear ocean
elseif( (rc443<rt443) & (rc868>0.08) & (rc868>1.1rc443) & (btd<5.0) & (elv>0) ) then→ clear landelseif( (rc4430.25) & (rc1640<rc443rsnow) & (rc1640<rc1050fsnow) & (rc1380/rs1380<r1380) &(sst<273.15) & (btd<5.0) & (sgr<0.01) & (Ta<278.15) ) then→ clear snowelse
→ cloudendif
2.4.2 Parameters
 TOA reflectance after gas absorption, rc443, 673, 868, 1050, 1640, 1380 nm
 bright temperature of 11m band, BT11 bright temperature of 12m band, BT126bright temperature difference, btd
btd = BT11−BT12 air temperature, Ta [K] approximate sea surface temperature, sst [K]sst = csst(1) + csst(2) BT11 + csst(3)  btd + csst(4)  btd plcsst =(/-1.5258, 1.0054, 2.4108, 0.56367/) ! SST by BT for SGLI (by pstar)pl : path length calculated by 1.0/cos()−1.0
 : satellite zenith angle
 approximate column water vapor, wvp [mm]wvp =wvc(1) btd+wvc(2) btd2
wvc =(/11.4821, 5.41933/)! WV by btd for SGLI (by pstar) btd estimated by ancillary column water vapor, bte [K]dte =btc(1) wv+btc(2) wv2
btc =(/0.063033,-0.00037827/) ! btd by WV for SGLI (by pstar)

1380 nm reflectance without water vapor absorption calculated by interpolation of 1050 nm and
1640 nm, rs1380
rs1380 =(rc1050 (1640−1380)+rc1640 (1380−1050))/(1640−1050)

threshold of transmittance of 1380nm, r1380
if( ( |rlat|<snowlat) | ((elv<1500) & (|rlat|<35)) ) thenr1380=0.95-0.90epr2! tropicalelse
r1380 =1.0 −0.90epr2
endif
epr: atmospheric pressure /1013.25 (1.0: 1013.25hPa)
 distribution latitude range of snow areas, snowlat [deg]snowlat=40 −elv0.01
elv: land surface elevation [m] threshold of 1640nm/1050nm ratio, fsnow
fsnow=0.7
 threshold of 1640nm/443nmratio, rsnow
if(lat<-60) then! Antarctic
rsnow=0.10
elseif((elv>1000) & (lat> 60) & (lon>-65) & (lon<-20)) thenrsnow=0.10
elseif( |lat| <snowlat) then! tropicalrsnow=0.20
elseif((elv<1500) & (|lat| <35)) then! tropicalrsnow=0.10
else
rsnow=0.28
endif
lat: latitude [deg]lon: longitude [deg]threshold of 443nm reflectance, rt443
if(rc8680.08) then
rt443=0.35
else
rt443=0.35−vgi0.20
if(rt4430<0.16) rt443=0.16
if(rt443>0.35) rt443=0.35
endif



vegetation index, vgi! GreenLD7vgi=(rc868−rc673)/(rc868+rc673)
2.5

Surface reflectance
2.5.1 Minimum reflectance
Minimum reflectance (a maximum NDVI sample between the 1st and 2nd minimum reflectancesamples to avoid shadows) after the molecular reflectance and transmittance (without aerosols),
rsmin() is used as initial values and index calculations.
2.5.2 Near-UV and blue band reflectance
Near-UV and blue band reflectance, rssim() is estimated by the NDVI calculated from rsmin().
rssim() is supplementary used for candidate rs() in the multi-temporal variation scheme.
rssim()=max(rs1(),rs2())rs1()=a0() +a1() NDVI
rs2()=b0() +b1() NDVILCI = rsmin(868nm) −1.4989 rsmin(673nm) +0.4759 rsmin(443nm)
Table 3 Coefficients of surface reflectance estimation by NDVI [nm]

a0

a1

b0

b1

380.1
412.6
443.3
489.9
529.7
566.2
672.0
672.2
763.1
866.8
867.2

0.29840.29510.29210.29120.29380.29760.30400.30410.32380.33190.3318-0.9837-0.9297-0.8969-0.8690-0.8528-0.8430-0.8029-0.8030-0.7209-0.6151-0.61410.03550.04220.04740.0546
0.05960.07510.12560.12570.03740.078
0.0781-0.0272-0.0338-0.0377-0.0445-0.0314-0.0448-0.1237-0.12370.22560.2182
0.2182

2.5.3 Multi-temporal variation scheme
This scheme estimate the surface reflectance Rs() by using the general characteristics of temporalvariation of surface reflectance is small and temporal variation of atmosphere is large.
Rs() is searched as minimizing temporal variation of Rs() at multiple  in about a month by
testing aerosol models (M) and optical thickness (a).

8Figure 3 Flow of the multi-temporal variation surface reflectance estimation algorithm(1) preset several candidate surface reflectance at the base band (blue), Rs(blue)j, and estimate a ateach MRt() / t’ − Rr() = Ra() + ( t0()t1()  Rs() ) / (1 − Sa()  Rs())(1)(2) M is calculated by presetting surface reflectance at the reference band Rs(red)(3) surface reflectance at the other bands Rs()j is calculated by the spectral relationship ofRa(),t0()t1(),Sa() of the loop-up table(4) Optimal Rs() is selected from the Rs()j (j = 1-32 days) to minimize temporal variation ofRs() at multiple bands .
(5) BRDF is calculated from Rs() calculated by Rs(blue) and mean of M
2.6

Candidate aerosol model
Aerosol models, M, are constructed by mixing the tropospheric and sea-salt particle type aerosols. It is
made as look-up tables which include TOA reflectance at solar and satellite geometries, transmittance,
aerosol optical thickness, spherical albedo
The aerosol models (particle size distribution, particle shape, and refractive index) will be replaced tothe models used in the atmosphere aerosol algorithms in the future.

2.7

Other corrections
2.7.1 Altitude correction
td0n=td0(b,1,1)(epr1-1.)
9

td1n=td1(b,1,1)(epr1-1.)
rrL(b)= rrL(b)  Prs! landrrO(b)= rrO(b)  Prs! oceansa(b,t,m)=sa(b,t,m)  Prs
td0(b,t,m)=td0(b,t,m)  td0n
td1(b,t,m)=td1(b,t,m)  td1n2.7.2 molecule scattering correction
rrc(b) = rt(b) − rr(b)rs0(b) = rrc(b) / (td0(b,1,1)  td1(b,1,1) + rrc(b)  sa(b,1,1))
2.7.3 BRF correction
R(0, 1, ) = k0 + k1 F1(0, 1, ) + k2 F2(0, 1, )
k0= Nadir reflectanceF1=(( − )cos()+sin())  tan(0)  tan(1)/(2  ) − (tan(0)+ tan(1)+ )/F2=(4/3/)/(cos(0)+cos(1)) (( /2−)  cos()+sin()) (1+1/(h+  /(1.5/180))) − 1/3
=sqrt( tan(0)2+tan(1)2 − 2  tan(0)  tan(1)  cos() )=acos{cos(0) cos(1) + sin(0) sin(1) cos()}0: solar zenith,
1: satellite zenith,
: ABS(relative azimuth),
: scattering angle
h=5.0 (The parameter h is originally 1.0; it set to 5.0 for the Ver. 2 to be stable regression)
2.7.4 slope correction (not applied for the current version)
Rt/tg = Ra + (rslp t0 t1 As ) / (1 – Sa As)
As = (Rt/tg − Ra) / (rslp t0 t1 + (Rt/tg − Ra) Sa)
rslp = ((1 − rdir) cos(sun ) + rdir cos(slp)) / cos(sun)slp =sin(sun) cos(sun) sin(slp) cos(slp) + sin(sun) sin(sun) sin(slp) sin(slp) + cos(sun) cos(slp)
rdir = tb/tdif
tb =exp(−(m + a ))tdif =exp(− (mb + (bba0+ bba1  )a ))a = a0 (/500nm)

2.7.5 center-wavelength correction (not applied for the at-launch version)
The center wavelength is calculated by the center-wavelength shift mode from the ground tests
using relative pixel position which is calculated by the pixel address and telescope information（/Geometry_data/Cross_track_section_flag） and satellite zenith angle.
VNR-NPL:rpix= 9.61898e-01 −3.73884e-02   +3.97139e-05  2

VNR-NPN1 :rpix= −3.73684e-02   −4.63678e-05  2

VNR-NPN2 :rpix= 3.73684e-02   +4.63678e-05  2

VNR-NPR :rpix= −9.61898e-01 +3.73884e-02   -3.97139e-05  2
10

VNR-PL1 :rpix= −8.65696e-03   −8.81878e-05  2

VNR-PL2 :rpix= 8.65696e-03   +8.81878e-05  2

: satellite zenith angle of VNR-NP (deg)c[nm]=c0 +c1*X +c2*X2 +c3*X3 +c4* X4X (= -0.5 ~ +0.5): (pixels - center_pixel)/pixel_length
pixels: 1~pixel_length at a telescope
pixel_length=1500 (VNR-NP 250m),
pixel_length=857 (VNR-PL)
c0: center wavelength at the telescope center (X=0)
Molecular scattering table and gas absorption table of minimum and maximum center-wavelength
are linear interpolated by the calculated center wavelength.
Table 4 Coefficients of SGLI center wavelength shift
Band
VNL01
VNL02
VNL03
VNL04
VNL05
VNL06
VNL07
VNL08
VNL09
VNL10
VNL11
VNN01
VNN02
VNN03
VNN04
VNN05
VNN06
VNN07
VNN08
VNN09
VNN10
VNN11
VNR01
VNR02
VNR03
VNR04
VNR05
VNR06
VNR07
VNR08
VNR09
VNR10
VNR11
VNP01
VNP02

c0

c1

c2

c3

c4

3.7983E+02 -2.0654E-01 4.0953E-01 7.6962E-01 -7.5110E-01
4.1235E+02 1.1515E-01 -7.2607E-02 5.3384E-01 -2.0197E+00
4.4337E+02 9.0581E-02 6.3376E-01 5.5982E-02 7.0385E-02
4.8966E+02 -3.0622E-01 3.3672E-01 4.5020E-01 -3.9824E-01
5.2952E+02 -3.5294E-01 9.6784E-01 -3.1234E-01 1.9387E+00
5.6589E+02 2.4046E-01 8.7851E-01 6.2411E-01 -2.7019E+00
6.7184E+02 -3.4264E-01 7.7027E-01 -6.1871E-01 5.8120E+00
6.7201E+02 -4.1357E-01 6.0967E-01 1.8496E-01 5.3048E+00
7.6327E+02 3.1900E-01 -2.6370E+00 -1.6204E+00 -4.7683E+00
8.6738E+02 1.8538E+00 -1.3750E+01 -1.2301E+01 4.8520E+01
8.6759E+02 1.8948E+00 -1.3941E+01 -1.2742E+01 4.8163E+01
3.8001E+02 1.7104E-01 2.9402E-01 -6.5029E-01 -6.9351E-01
4.1257E+02 1.6869E-01 -2.2033E-01 4.3098E-01 -1.9828E+00
4.4316E+02 -9.9156E-02 7.1108E-01 -1.6487E-02 4.1156E-01
4.8983E+02 2.8187E-01 2.9698E-01 -3.6911E-01 -5.8977E-01
5.2952E+02 3.9348E-01 8.8587E-01 2.7552E-01 2.0914E+00
5.6613E+02 2.9743E-01 7.9222E-01 4.1063E-01 -2.9697E+00
6.7182E+02 3.8590E-01 1.2226E+00 -2.3696E-02 3.8871E+00
6.7194E+02 4.3907E-01 7.5132E-01 2.2127E-02 5.3565E+00
7.6342E+02 -3.1793E-01 -2.5845E+00 1.6389E+00 -4.8441E+00
8.6730E+02 -1.7540E+00 -1.3713E+01 1.2056E+01 4.8410E+01
8.6767E+02 -1.8758E+00 -1.3568E+01 1.2824E+01 4.7018E+01
3.8020E+02 -1.4418E-01 2.5644E-01 6.5340E-01 -9.4438E-01
4.1265E+02 2.0817E-01 -3.2323E-01 3.4006E-01 -1.7034E+00
4.4296E+02 1.0681E-01 8.1284E-01 -3.6629E-02 7.1595E-01
4.9032E+02 -2.2237E-01 1.0639E-02 2.1432E-01 -5.0950E-01
5.2955E+02 -4.1437E-01 9.1150E-01 -1.2109E-01 1.6420E+00
5.6636E+02 3.4432E-01 7.4963E-01 2.2421E-01 -3.3094E+00
6.7177E+02 -4.0534E-01 1.1152E+00 -1.8236E-01 4.7486E+00
6.7198E+02 -5.0520E-01 6.5910E-01 2.4386E-01 5.1359E+00
7.6357E+02 3.0504E-01 -2.5451E+00 -1.5900E+00 -4.8479E+00
8.6725E+02 1.7793E+00 -1.3803E+01 -1.2226E+01 4.9354E+01
8.6760E+02 2.0163E+00 -1.3424E+01 -1.4051E+01 4.8480E+01
6.7191e+02 -3.3244e-01 1.2376e+01 1.2811e+00 -3.2838e+01
8.6620e+02 -1.7438e+00 1.0498e+00 5.1759e+00 -5.6396e+00

RgErr[nm] Ave[nm]
0.005
0.005
0.004
0.005
0.011
0.008
0.020
0.018
0.008
0.046
0.042
0.005
0.008
0.005
0.005
0.010
0.007
0.022
0.018
0.008
0.045
0.032
0.006
0.006
0.004
0.007
0.011
0.006
0.018
0.017
0.007
0.048
0.034
0.023
0.007

379.85412.31443.43489.69529.62565.93671.98672.13762.96866.83867.02380.03412.52443.23489.85529.63566.16671.97672.08763.12866.76867.12380.21
412.60443.04490.31
529.65566.38671.93672.10763.28866.71867.08672.54866.22

Min[nm] Max[nm]379.80412.11443.37489.60529.47565.76671.81
671.96762.35866.23866.40379.99412.28443.16489.77529.48565.96671.76671.89762.51866.18866.50380.18412.34442.96490.21529.50566.16671.73671.91
762.68866.11866.46671.89865.85379.91
412.38443.57489.81530.08566.12672.61
672.65763.28867.48867.70380.08412.60443.40489.97530.09566.34672.55672.66763.43867.43867.81
380.24412.68443.24490.39530.09566.56672.55672.67763.58867.44867.90673.10866.6311

Coefficients c0~c4, regression error, Average, Min and Max of center wavelengths of VNL (VNR-NP telescope-Left), VNN (VNR-NP telescope-Nadir), VNR (VNR-NP telescope-Right) and VNR-PL. The coefficients were
calculated by EORC using original measurements by SGLI sensor team)

2.8

Mask/Flag
The first 0-1bits are common for the Level-2 products, input

Description

Level-3
mask

data lack and land/water flags. Others are defined as the right0 no data (mask)0table.

1 land (0: ocean, 1: land) (flag)

0

2 coast (flag)0

3 sun-glint >0.005(flag)0

cloud is set by the multiple day tests. Bit-13 is set by

4 sun-glint >0.12 (mask)

1

additional cloud cover tests by polarization channels. Bit-14

5 snow or ice (flag)0(non-polarization) and 15 (polarization) are set when the grid6 cloud (mask)0

7 probably cloud (by multi-day) (flag)

1

8 high tau-a>0.8 (flag)

1

The Level-3 statistics processing will use data with the

9 saturation recovery (flag)

0

QA_flag of bit-4, 7, 8, and 12=0 and the product value is

10 BRF samples<=3(flag)0

within the valid range.

11 stray light (flag)0

12 shadow (mask)

1

13 pol cloud or hi-tau (mask)0

14 recovery by pre-days (flag)0

15 recovery (pol) (flag)0

Cloud masks are set by the target date, and the probably

is recovered by the pre-day’s BRDF table.

3

Table 5 Bit specification of QA_flagbit

Validation plan
3.1

Error budget estimation
3.1.1 TBD

3.2

Validation method after launch
3.2.1 Data release threshold accuracy：0.3 (443nm), 0.2 (>443nm) *It is calculated as root mean square (RMS) difference (RMSD) between the instantaneous satellite
estimates and in-situ measurements where AOT at 500nm less than 0.25
3.2.2 Standard accuracy：0.1 (443nm), 0.05 (>443nm) *Same as the above
3.2.3 Target accuracy: 0.05 (443nm), 0.025 (>443nm) *Same as the above
note: Defined with land reflectance~0.2, solar zenith<30deg, and flat surface
3.2.4 acquisition of validation data
In-situ data will be obtained mainly by the GCOM-C PIs at JAXA super site 500, JaLTER,
12

JapanFlux, PEN sites and so on.

•

Honda of Chiba Univ. （Spectral reflectance (incl. BRDF) data measured by FieldSpec,MS-720, Hyperspectral Camera from UAV, Spectral data measured from UAV. BiRS
simulations will be used for uniform surfaces）、•Nasahara of Tsukuba Univ. （PEN, Spectral data measured by MS-720 and MS-700 fromTower）

3.2.5 Comparison with MODIS BRDF product
Validation of special distribution and stability of the retrievals

4

Remaining issues
4.1

SGLI calibration sensitivities•

center-wavelength correction•

polarization sensitivity correction (applied after post-launch evaluation, especially for 380nm)

4.2

Validation of slope correction
Decision of application after evaluation of the improvement by the correction

4.3

Cloud detection evaluation and improvement
Validation and optimization of the cloud detection including cloud shadow

4.4

Use of LAI BRF model
It will be investigated after validation of the LAI model

4.5

Adjacent effect
The efficiency will be investigated after launch

They will be investigated with application researches in the future.

5

Additional output parameters
5.1

Photosynthetically active radiation (PAR)PAR is included in the output file of the atmospheric correction, RSRF. The PAR algorithm is the sameas the GLI PAR (Frouin 2004; Frouin and Murakami, 2007) and using the SGLI channels VN02, VN 03,
VN 05, VN 08, and SW01.

5.2

Shortwave radiation (SWR)
SWR is included in the output file of the atmospheric correction, RSRF. The SWR algorithm is thesame as the PAR, but the weighting coefficients of transmittance at the channels are optimized for SWR
by using Pstar4 (Ota et al., 2010).

References
Ota, Y., A. Higurashi, T. Nakajima, and T. Yokota (2010), “Matrix formulations of radiative transfer including thepolarization effect in a coupled atmosphere-ocean system,” J. Quantitative Spectroscopy and Radiative Transfer,
111, 878-894.
Maignan, F., F.-M. Breon, R. Lacaze, Bidirectional reflectance of Earth targets: Evaluation of analytical models
using a large set of spaceborne measurements with emphasis on the Hot Spot, Remote Sensing of Environment
13

90 (2004) 210–220.
Alexei I. Lyapustin, Yujie Wang, Istvan Laszlo, Thomas Hilker, Forrest G.Hall, Piers J. Sellers, Compton J.
Tucker , Sergey V. Korkin: Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3.
Atmospheric correction , Remote Sensing of Environment 127 (2012) 385–393.
Mahmoud El Hajj, Agnès Bégué, Bruno Lafrance, Olivier Hagolle, Gérard Dedieu and Matthieu Rumeau, Relative
Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series, Sensors 2008, 8, 2774-2791.
Olivier Hagolle, Gérard Dedieu, Bernard Mougenot, Vincent Debaecker, Benoit Duchemin, et al.. Correction ofaerosol effects on multi-temporal images acquired with constant viewing angles: application to Formosat-2
images. Remote Sensing of Environment, Elsevier, 2008, 112(4), pp.1689-1701.
Frouin, R., Algorithm to estimate daily Photo-synthetically Available Radiation at the Ocean surface (OTSK14) ,Nov.2004, https://suzaku.eorc.jaxa.jp/GLI/ocean/algorithm/ATBD_OTSK14_v210.pdf.
Frouin, R., and H. Murakami: Estimating photosynthetically available radiation at the ocean surface fromADEOS-II global imager data, J. Oceanography, 63,493-503, 2007.

14