Imasu et al.
Progress in Earth and Planetary Science 2023, 10(1):33
https://doi.org/10.1186/s40645-023-00562-2

Progress in Earth andPlanetary Science

Open Access

REVIEW

Greenhouse gases Observing SATellite 2(GOSAT‑2): mission overview
Ryoichi Imasu1* , Tsuneo Matsunaga2, Masakatsu Nakajima3, Yukio Yoshida2, Kei Shiomi3, Isamu Morino2,
Naoko Saitoh4, Yosuke Niwa2, Yu Someya2, Yu Oishi2,6, Makiko Hashimoto3, Hibiki Noda2, Kouki Hikosaka5,
Osamu Uchino2, Shamil Maksyutov2, Hiroshi Takagi2, Haruma Ishida7, Takashi Y. Nakajima8,
Teruyuki Nakajima3 and Chong Shi9

Abstract
The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in October 2018 as a successor to GOSAT(launched in 2009), the first satellite to specialize in greenhouse gas observations. Compared to the GOSAT sensors,
the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR
bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracyof greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern
limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation
data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are
obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover,the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the verticalconcentration distributions of ­CO2 and ­CH4 have been improved drastically. The first function introduced for GOSAT-2
that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field cameraof TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracyof ­CO2 emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system
can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3
of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used
to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbontype aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with ­CO2 and CO
by TANSO-FTS-2.
Keywords GOSAT-2, GOSAT, Carbon dioxide, Methane, Carbon monoxide, Black carbon, Solar-induced chlorophyll
fluorescence, TANSO-FTS-2, TANSO-CAI-2, Intelligent pointing mechanism*Correspondence:
Ryoichi Imasu
imasu@aori.u-tokyo.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2023, corrected publication 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

1 Introduction
The Greenhouse gases Observing SATellite (GOSAT), thefirst satellite dedicated to observing greenhouse gases,
was launched in 2009 (Hamazaki et al. 2005; Kuze et al.
2009). It continues to make observations normally. The
satellite’s main sensor, Thermal And Near infrared Sensor
for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS), has observed dry air column average concentrations of carbon dioxide and methane andtheir upper air concentrations for over a decade. Basedon these data, inversion analyses of these gases on a subcontinental scale have been performed (e.g., Takagi et al.
2011), contributing to the improvement of the estimation
accuracy of the surface fluxes of the gases. Furthermore,
observations of solar-induced fluorescence (SIF), an
index related to the activity of terrestrial vegetation, werefirst conducted (Joiner et al. 2011; Frankenberg et al.
2011a, 2011b). The TANSO-Cloud and Aerosol Imager(CAI), an image sensor onboard the satellite, has begun
to analyze land aerosols that include black carbon (e.g.,
Hashimoto and Nakajima 2017).
Based on these achievements, GOSAT-2, a successorof GOSAT, was launched in October 2018. The signal-tonoise ratio of its main sensor, TANSO-FTS-2, has been
improved considerably in both the Short Wavelength
InfraRed (SWIR) and Thermal InfraRed (TIR) bands.
A new spectral band has been added for observing carbon monoxide (CO). Because GOSAT has the important
shortcoming that the probability of contamination causedby clouds is closely attributable to its large instantaneous
field of view (IFOV), we adopted a new mechanism called
intelligent pointing to concentrate the observation points
in the clear sky region.
In addition, two observation directions (forward andbackward) became available with CAI-2 to better avoid
sun glint areas as CAI suffered from them to perform thecloud detection.
Furthermore, the addition of new spectral channels has
improved the accuracy of observations of highly lightabsorbing aerosols such as black carbon.
Although GOSAT and GOSAT-2 sensors have large
IFOV, they have been accumulating long-term global
observation data on greenhouse gas concentrations in
the atmosphere. On the other hand, for Orbiting Carbon Observatory-2 (OCO-2) (Crisp et al. 2017) andOCO-3 (Eldering et al. 2019), although the observation
period is short, high spatial resolution data have beenacquired. Combining such different types of sensors is
expected not only to compare detection sensitivities, butalso to create data sets with well-defined spatiotemporal
resolution. On the other hand, regarding atmosphericpollutants such as black carbon, the synergy between
GOSAT and GOSAT-2 on-board sensors with a small

Page 2 of 35

instantaneous field of view and sensors such as The
TROPOspheric Monitoring Instrument (TROPOMI)(e.g., Torres et al. 2020) and Ozone Monitoring Instrument (OMI) (e.g., Jethva and Torres 2019), which cansimultaneously measure many chemical substances andaerosols, is expected to bring more detailed knowledge ofatmospheric chemistry.
Chapter 2 of this article presents detailed specificationsof satellites and sensors. The data analysis algorithms of
TANSO-FTS-2 and CAI-2 are described in Chapter 3. In
Chapter 4, calibration of the radiance data observed by
sensors and verification of level 2 data such as gas concentration and aerosol content are described. In Chapter 5, the scientific goals related to the global and regional
carbon cycles, SIF measurements, and air pollution monitoring using GOSAT sensors are described based on the
project’s science plan (Imasu 2019).

2 Satellite and sensors
2.1 Satellite bus system

Like its predecessor, GOSAT, the GOSAT-2 (known as“IBUKI-2”) satellite is dedicated to the observation ofgreenhouse gases. It carries two mission instrumentsthat provide higher performance than those of GOSAT:
Thermal And Near infrared Sensor for carbon Observation-Fourier Transform Spectrometer-2 (TANSO-FTS-2)(“FTS-2”) and Cloud and Aerosol Imager-2 (TANSOCAI-2) (“CAI-2”). GOSAT-2 is flying in a sun-synchronous sub-recurrent orbit. Based on the operation of
GOSAT and the mission requirements for GOSAT-2,
some functions and performances have been improved as
explained below.
The recurrence cycle of GOSAT is 3 days. The distanceto the next orbit is about 900 km on the equator. However, the swath of TANSO-CAI is 1,000 km, so areas
lying somewhere between the two orbits are observedwith large off-nadir angles, which prevents improvementof the aerosol measurement accuracy. Therefore, it was
decided that GOSAT-2 should pass between the GOSAT
orbits so that the areas lying somewhere between two
GOSAT orbits would be observed with a small off-nadir
angle. Furthermore, the recurrence cycle which depends
on the orbit height and inclination was set as 6 days.
Two candidates of orbit heights and lower height orbit of
613 km were selected because the footprint size of FTS-2
would thereby be smaller. The GOSAT orbit was set
in such manner that GOSAT flies over Tsukuba, Japan,
where the Total Carbon Column Observing Network(TCCON) can acquire validation data. However, because
many data acquired at Tsukuba are contaminated byclouds, the GOSAT-2 orbit was set to fly over another
TCCON site, Lamont, where it is possible to anticipate
sunny days.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Table 1 Principal specifications of GOSAT-2 compared with
GOSAT
Items

GOSAT-2

GOSAT

Main body

5.3 m × 2.0 m × 2.1 m

3.7 m × 1.8 m × 2.0 m

Size(Wing Span 16.5 m)(Wing Span 13.7 m)

Total mass

1700 kg

1750 kg

Total power

5.0 kW (EOL)

3.8 kW (EOL)Life time

Five years

Five years

Orbit

Sun synchronous orbit

Sun synchronous orbit

   Local time

13:00 ± 0:15

13:00 ± 0:15

   Altitude

613 km666 km   Inclination

98°

98°

   Repeat

6 days (89 revol.)3 days (44 revol.)Page 3 of 35

In cases where only low power is available, as mightoccur with the breakdown of one solar array paddle,
GOSAT was designed to halt observations in the TIR
band of FTS and CAI. By contrast, GOSAT-2, with its
larger-sized solar array paddle than that of GOSAT, isdesigned to continue operations of all mission instruments even after the breakdown of one solar arraypaddle.
Table 1 and Fig. 1, respectively, present the principal specifications of GOSAT-2 and an artistic image of
GOSAT-2 on orbit.
2.2 TANSO‑FTS‑2
2.2.1 Overview

TANSO-FTS-2 is a Fourier transform spectrometer(FTS), as is TANSO-FTS on GOSAT. The pointing mirror steers the line of sight to the observation area. Afterthe scene flux passes the interferometer, it is divided into
five observation bands by the dichroic filters. Additionally, bands other than the TIR are divided into two polarizations: P and S. They form an image on the detectors by
the imaging optics. Table 2 presents the principal specifications of the TANSO-FTS-2. Also, Figs. 2, 3, and 4,
respectively, present an outline view of the sensor unit of
TANSO-FTS-2, a ray diagram, and details of the divisionof the band.
Fig. 1 Artistic image of GOSAT-2 in orbit

Table 2 Principal specifications of TANSO-FTS-2 compared with TANSO-FTS
Items

TANSO-FTS-2/GOSAT-2

TANSO-FTS/GOSAT

Measured gases

CO2, ­CH4, ­O3, ­H2O, CO

CO2, ­CH4, ­O3, ­H2O

FOV

15.8 mrad (9.7 km)

15.8 mrad (10.5 km)

Spectral ranges (μm) (cm−1)

Band 1: 0.75–0.77 (12,950–13,250)

Band 1: 0.75–0.77 (12,900–13,200)

Band 2: 1.56–1.69 (5900–6400)

Band 2: 1.56–1.72 (5800–6400)

Band 3: 1.92–2.33 (4200–5200)

Band 3: 1.92–2.08 (4800–5200)

Band 4: 5.5–8.4 (1188–1800)

Band 4: 5.5–14.3 (700–1800)

Band 5: 8.4–14.3 (700–1188)
SNR(@standard radiance)

Band 1: > 400 (P@13,050 ­cm−1)

Band 1: > 300

Band 2: > 300 (P@6,200 ­cm−1)

Band 2: > 300

Band 3: > 300 (P@5,000 ­cm−1)

Band 3: > 300> 300 (P + S@4,250 ­cm−1)

Band 4: > 300

Band 4: > 300 (@1,300 ­cm−1)
Band 5: > 300 (@700 ­cm−1)
Observation mesh

160 km (5 points in the CT direction)

160 km (5 points in the CT direction)

Scan duration

4 s/interferogram

4, 2, 1.1 s/interferogram

Sampling resolution

0.2 ­cm−1

0.2 ­cm−1

Effective aperture size

Φ73 mm

Φ64 mmGain steps

16

2

Avoidance of the cloudIntelligent pointing–Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Fig. 2 Outline view of FTS-2 the sensor

2.2.2 Improvements from TANSO‑FTS on GOSAT

The increase in signal-to-noise ratio (SNR) and the addition of carbon monoxide measurements were necessary
to satisfy mission requirements such as anthropogenic
emission estimation over mega cities. Furthermore, theeffective aperture size was extended from TANSO-FTS.
The band range of the 2.0 μm band was extended to
2.3 μm.
To minimize the change of design from TANSO-FTS to
the greatest degree possible, the basic design of the interferometer was retained. The aperture size was extended
to the maximum size, which is available with the basic
design of TANSO-FTS to increase the signal level. Based

Fig. 3 Ray diagram of TANSO-FTS-2

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on GOSAT operations and the mission requirements, thespectral range of each band was optimized to decrease
the noise level.
The range of each band was changed from that of
GOSAT. The range of Band 1 was shifted slightly basedon results of GOSAT data utilization. That of Band 2 was
reduced to increase SNR by cutting the range which has
not been used. That of Band 3 was extended to 2.3 μm to
observe the carbon monoxide. TIR band was divided into
two bands, Bands 4 and 5, to increase the SNR. Details
about SNR for each spectral band are described by Sutoet al. (2021, 2022) as a function of radiance intensity. For
reference, comparison of SNR of detectors in the same
wavenumber range for TANSO-FTS and TANSO-FTS-2
is shown in Tables 2, 3. Basically, apodization processing has not been applied to the spectra of all bands inthe retrieval analysis. The IFOV is 15.8 mrad, which isthe same as that of GOSAT, but the footprint size wasdecreased from 10.5 to 9.7 km because of the lower
GOSAT-2 altitude.
The Instrument Spectral Response Function (ISRF)
is almost same as that of TANSO-FTS on GOSAT. The
ISRF was evaluated using the Instrument Line Shape(ILS) target on TANSO-FTS-2, which is an integrated
sphere with laser diodes of 768 nm and 1543 nm, respectively, for Bands 1 and 2, and a gas cell on the ground. The
ISRFs of Bands 1 and 2 were evaluated on orbit using the
ILS target. The FWHMs of Bands 1 and 2 were proved tobe maintained at almost the same value as that obtainedImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 5 of 35

Fig. 4 Details of the division of the band

Table 3 Signal-to-noise ratio (SNR) of detectors of TANSO-FTS-2
and TANSO-FTS (obtained on the ground before satellite launch)
Band

Wavenumber­(cm−1)TANSO-FTS-2

TANSO-FTS

B1P

13,050

561

345

B1S

13,050

462

247

B2P

6200

459

322

B2S

6200

390

258

B3P

5000

326

412

B3S

5000

252

287

B3P

4250

257

–

B3S

4250

303

–

B4

1300

1512

298

B5

700

941

–

during ground testing by evaluation done for the commissioning phase. The ISRF of Band 2 measured on orbit
is shown in Fig. 5.
Except for Band 1, for which the sampling resolution isdifferent from those of other bands, all bands have almost
the same FWHM. Details about ILS and its spectral
dependences are described by Suto et al. (2021, 2022).
In addition, the pointing angle in the along track direction increased from ± 20 degrees to ± 40° to extend thesun glint observation area. GOSAT observes the standard mesh points, which are arranged with regularity. Up
to 1500 points, a day can be replaced with specific points.

By contrast, GOSAT-2 is capable of observing all points
as specific points. One can select the observation points
freely within the capabilities of the pointing mirror moving speed.
The observation points are stored in the ground system as an observation table that is assigned to one recurrence cycle of 6 days. The ground system can hold up to
five tables. The observation points in the table will be
replaced with new specific points such as points requiredby users including PIs. The table of one-day observation
points is uploaded to GOSAT-2: GOSAT-2 can then holdtables for two days. TANSO-FTS on GOSAT can selectthe scan duration, which is the time to select one interferogram from a menu of choices: 1.1 s, 2 s, and 4 s. However, the SNR of the data taken with the durations of 1.1 s
and 2 s is relatively low. Therefore, TANSO-FTS-2 on
GOSAT-2 makes observations with only 4 s scan duration. It is difficult to optimize the gain to fit the scene
because TANSO-FTS has only two steps of the gain.
TANSO-FTS-2 has 16 steps of the gain to take advantage of the dynamic range, accommodating very different
radiance.
2.2.3 Intelligent pointing

Although TANSO-FTS with a point detector observes
discretely distributed points, any clouds in the scene,
however small, render the data invalid. TANSOFTS-2 has a function of avoiding the clouds in theImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 6 of 35

Fig. 5 ISRF of Band 2 measured on orbit

scene. This function operates as follows: The imageof an 50 km × 30 km area around the planned observation point is obtained using the RGB camera for which
the line of sight (LOS) is adjusted coaxially with that of
TANSO-FTS-2. Once clouds in the image are detected,
the LOS will be shifted to the cloud free area by steering

the pointing mirror. This sequence of actions is completed within about 650 ms turnaround time. A detailed
sequence of the intelligent pointing is presented in Fig. 6;
also, Fig. 7 presents actual pictures acquired by the monitor camera in orbit. It is possible to switch the intelligent
pointing on and off.

Fig. 6 Detailed sequence of intelligent pointing. After completion of the preceding observation, the line of the sight (LOS) is steeredto the next scheduled observation point. After the steering was stopped completely, after about 250 ms of the start of the steering,the image around the scheduled observation point is taken by the monitor camera (about 150 ms duration of ). Then, the image is transferredto the processing part; the clouds are detected with about 50 ms duration). Based on these results, the cloud-free area is determined. The LOS
is steered to that area (about 50 ms duration of ). The next measurement starts at 650 ms after completion of the preceding observation

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

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This pointing mechanism including the algorithm wasconfirmed by trial manufacturing and flight test, combined with a FTS for airplanes. The intelligent mechanism was confirmed to have functioned as expected atabout 45% of the approximately 3600 observation points.
However, the RGB camera used for this experiment on
the airplane had had about one-hundredth of the IFOVof the camera equipped with TANSO-FTS-2. Therefore,
cloud detection was easy. For that reason, 45% seems to
be larger than the actual FTS-2 case.
According to the simulation using the monitor camera
data obtained by TANSO-FTS on GOSAT, the number ofvalid data is expected to increase to about two times thatof GOSAT. The model used for the flight test of the intelligent pointing and photographs taken before and after cloud
avoidance are presented, respectively, in Figs. 8 and 9.
2.3 TANSO‑CAI‑2

Fig. 7 Images acquired by the monitor camera at the planned
steering and after the steering of the LOS. a Planned observation
point. The solid circle shows the planned observation point. The
dotted circle shows the determination of no clouds and shows what
can be observed. b Actually observed point. This area is the sameas that of the dotted circle of a

Fig. 8 Model used for flight testing of intelligent pointing

TANSO-CAI-2 is a push-broom-type imaging radiometer using a linear array sensor and band pass filter,
similarly to TANSO-CAI. The LOS of TANSO-CAI-2
is tilted ± 20° in the along track direction relative to
TANSO-CAI, for which the LOS points to the nadir
direction. Also, TANSO-CAI-2 consists of five lens systems. Each lens system has two FOVs of forward andbackward viewing.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

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view of the sensor unit of TANSO-CAI-2 without insulation, multilayer insulation (MLI), and a cross sectionof the lens system.

3 Retrieval methods
3.1 TANSO‑FTS‑2 SWIR
3.1.1 Overview

Fig. 9 Photographs taken before and after cloud avoidance for twocases (left and right). Green circle represents observation points.
a Scheduled pointing, b processing the cloud and cloud free area
(white circles are scheduled pointing position and red presents
the cloud area), and c re-pointing to the cloud free area

Based on requirements to improve the measurement accuracy of aerosols, three new observation
bands were added to TANSO-CAI-2 in addition to thefour observation bands of TANSO-CAI. Furthermore,
three bands are available for observation by forward
and backward viewings. The swath of the 1.6 μm bandof TANSO-CAI is 750 km, which is 25% narrower thanother bands. Therefore, the swath of the 1.6 μm band of
TANSO-CAI-2 was matched with those of other bands.
Table 4 presents the principal specifications of
TANSO-CAI-2. Also, Figs. 10 and 11 depict an outline

From TANSO-FTS-2 SWIR Level 2 processing,“TANSO-FTS-2 SWIR L2 Chlorophyll Fluorescence andProxy-method Product” and “TANSO-FTS-2 SWIR L2
Column-averaged Dry-air Mole Fraction Product” areobtained.
The TANSO-FTS-2 SWIR L2 Chlorophyll Fluorescence and Proxy-method Product includes the six individual retrieval results processed under the assumptionsof cloud-free and aerosol-free conditions. Six wavenumber ranges, two each from the three TANSO-FTS-2
SWIR bands, correspond to six independent retrievals. From TANSO-FTS-2 Band 1, data of solar-induced
chlorophyll fluorescence (SIF) and surface pressure are
retrieved, respectively, using Fraunhofer lines in thewavenumber range, where the O­ 2 absorption and the ­O2
A-band are negligibly small. From TANSO-FTS-2 Band2, ­XCO2 and ­XCH4 are retrieved, respectively, from the
1.6 µm ­CO2 band and 1.67 µm ­CH4 band. From TANSOFTS-2 Band 3, X
­ CO2 and ­XH2O, and XCO, X­ CH4, and­XH2O are retrieved, respectively, using the 2.08 µm C
­ O2band and 2.3 µm CO band. SIF is weak radiation emitted
by chlorophylls during photosynthesis. It appears as thevariation in fractional depth (filling in) of the Fraunhofer

Table 4 Principal specifications of TANSO-CAI-2 compared with TANSO-CAI
Items

Spectral range (nm)TANSO-CAI-2/GOSAT-2

TANSO-CAI/GOSAT

Forward Viewing

Backward Viewing(+ 20°)(– 20°)

Nadir Viewingb 1: 333–353

b 6: 370–390

Band 1: 370–390

b 2: 433–453

b 7: 540–560

Band 2: 664–684

b 3: 664–684

b 8: 664–684

Band 3: 860–880

b 4: 859–879

b 9: 859–879

Band 4: 1555–1645

b 5: 1585–1675

b 10:1555–1645

Spatial resolution

Except Bands 5, 10 Bands 5, 10

Except Bands 5, 10 Bands
5, 10

Bands 1–3 Band 4(@nadir)

460 m (700 mrad) 920 m (1400 mrad)

460 m (700 mrad) 920 m(1400 mrad)

500 m (750 mrad) 1500 m (2250 mrad)

Swath (FOV)

920 km920 kmBand 1–3 Band 4

SNR

b 1: > 200 (45/0.158)

b 6: > 200 (48/0.152)

b1: > 200 (47/0.149)

1,002 km (72°) 786 km (60°)(@spectral-radiance/albedo)b 2: > 200(79/0.144)

b 7: > 200 (65/0.125)

b2: > 200 (45/0.104)(W/m2/sr/μm)

b 3: > 200 (46/0.106)

b 8: > 200 (46/0.106)

b3: > 200 (29/0.108)

b 4: > 200 (30/0.112)

b 9: > 200 (30/0.112)

b4: > 200 (7/0.101)

b 5: > 200 (7/0.101)

b 10: > 200 (7/0.101)

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Fig. 10 Outline view of the sensor unit of CAI-2 without MLI

lines in the satellite-observed spectrum. Using this filling-in signal, SIF can be retrieved from satellite observed
spectra (Frankenberg et al. 2011a, 2011b). Aside from SIF
retrieval, five other retrievals use atmospheric absorption
bands. Because these retrievals are processed under theassumption of cloud-free and aerosol-free conditions, theretrieved column-averaged dry-air mole fractions include
some effects from optical path length modifications. If
the column-averaged dry-air mole fraction of some gas
species (Xgas) area is obtained with high accuracy using amodel-based calculation or other methods, then that gas
is useful as an optical path length proxy by comparing theretrieved Xgas to prior knowledge. Because the degree of
optical path length modification is regarded as similar to

Fig. 11 Cross section of the lens system

Page 9 of 35

the nearby wavelength, the evaluated optical path lengthproxy is then useful to target gas retrieval. This method
is designated as a “proxy method.” The ­CO2 retrieved
from the 1.6 µm C
­ O2 band is often used as the optical
path length proxy to obtain ­XCH4 from the 1.67 µm ­CH4
band (e.g., Frankenberg et al. 2011a, 2011b; Parker et al.
2011). Furthermore, the difference between the retrieved
and a priori values of surface pressure obtained under
the assumption of cloud-free and aerosol-free conditions
is expected to reflect the degree of optical path length
modification because of clouds and aerosols. Therefore,
the TANSO-FTS-2 SWIR L2 Chlorophyll Fluorescenceand Proxy-method Product includes retrieval results for
all TANSO-FTS-2 SWIR observations.
The TANSO-FTS-2 SWIR L2 Column-averaged Dryair Mole Fraction Product includes simultaneously
retrieved ­XCO2, ­XCH4, ­XH2O, and XCO considering
clouds and aerosols and using the several wavenumber
ranges. This retrieval is designated as the “full-physics
method” after the mode of the treatment of the optical
path length modification. In the full-physics method, theoptical path length modification attributable to clouds
and aerosols is described by atmospheric scattering processes using the forward model. Being different from theproxy method, the full-physics method has the important benefit that no limitation exists for the gas species
to retrieve. However, its retrieval error depends strongly
on how accurately the forward model can represent the
optical path length modifications attributable to clouds
and aerosol scattering. Therefore, TANSO-FTS-2 SWIR
observations that are affected strongly by clouds and/or
aerosols are not processed using the full-physics method.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 10 of 35

Fig. 12 Examples of column-averaged dry-air mole fraction products of ­CO2 (top), ­CH4 (middle), and CO (bottom). The product data version
is V01.07. Values are averaged in each 2.5° mesh area for March 2019

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 11 of 35

Retrieval results for converged cases in the retrieval processing are stored in the TANSO-FTS-2 SWIR L2 Column-averaged Dry-air Mole Fraction Product.
Differences in the wavenumber range of observed spectra, state vector elements, and treatment of clouds andaerosols can be treated as differences in retrieval settings.
The Maximum A Posteriori (MAP) retrieval method(Rodgers 2000) is used for all retrieval processing.
Figure 12 presents examples of column-averaged dryair mole fraction products of ­CO2, ­CH4, and CO as averaged values in each 2.5° mesh area for March 2019.

In that equation, subscript i denotes the number of
iterations, K = ∂F(x)/∂x is a Jacobian, D is a diagonal
matrix introduced to stabilize the iteration process, andλ represents a coefficient of which the value is adjusted sothat the value of the cost function decreases in each stepof the iteration.
Here, the posterior error covariance matrix S and theaveraging kernel matrix AK are given, respectively, by
Eqs. (3) and (4).
−1
−1
S = K T · S −1
(3)
ε · K + Sa

3.1.2 Maximum posterior (MAP) analysis method

According to the MAP analysis method, the optimal estimate of state vector x, which represents unknown parameters to be retrieved, is the value of x which minimizes
cost function J(x), as expressed in Eq. (1).

J (x) = [y − F (x, c)]T · S −1
ε · [y − F (x, c)]+ (x − xa )T · S −1
a · (x − x a )(1)

Therein, y represents the observed radiance spectrum, F(x, c) denotes a forward model, i.e., the spectrum
computed with the state vector x and other necessary
variables c as inputs, xa is the a priori state vector, Sa represents the variance–covariance matrix of xa, and Sε isthe variance–covariance matrix of observation error andforward model.
In TANSO-FTS-2 SWIR L2 processing, solution x is
obtained iteratively from Eq. (2).
−1−12xi+1 = xi + K T
i · Sε · K i + Sa +  · D

·


−1
−1
AK = K T · S −1·
K+S
K T · S −1
ε
a
ε ·K

(4)

As described earlier, the elements of state vector x
and the wavenumber range of the observed spectrum ychange for each retrieval process, but the same a priori
values are used for each type of state vector element(Table 5). The column-averaged dry-air mole fraction oftarget gas species Xx is obtained by the vertical weighted
integral of the gas concentration profile xx obtained using
MAP analysis (Eqs. 5 and 6). Here, subscript x denotes
the elements corresponding to the gas species to be
retrieved. To describe the gas concentration profile, we
use the layer-averaged (partial column-averaged) dry-air
mole fraction of a gas species in a vertical grid that evenly
divides the atmosphere by atmospheric pressure.

−1−1
K Ti · S −1
ε · [y − F (x i )] − S a · (x i − x a )X x = hT · x x(5)wdry,j(h)j = hj = 
j wdry,j(6)(2)

Table 5 A priori value of state vector for TANSO-FTS-2 SWIR L2 processing
State vector elements

A priori value

Note

CO2 profile

NICAM-TM simulated value

Partial column-averaged dry-air mole fraction

CH4 profile

NICAM-TM simulated value

Partial column-averaged dry-air mole fraction

H2O profile

JRA-55 reanalysis value

Partial column-averaged dry-air mole fraction

CO profile

MOZART climatological value

Partial column-averaged dry-air mole fraction

Aerosol profile

SPRINTARS simulated value

Optical thickness

Surface pressureJRA-55 reanalysis value

SIF

Zero

Surface albedo

Calculated from maximum value of the observed radianceunder the assumption of Lambert surface with no-scattering and noabsorption atmosphere

For land surface case

Wind speed

JRA-55 reanalysis value

For water surface caseImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 12 of 35

Therein, subscript j denotes a vertical grid; also, wdry is apartial column amount of dry air.
The posterior error σx2, column averaging kernel ax,
and degrees of freedom for signals (DFS) with respect toXx (DFSx) are given, respectively, by Eqs. (7–9).

σx2 = hT · S xx · h

(7)

 1

(ax )j = ax,j = hT · AK xx ·
j hj

(8)

DFSx = trace(AK xx )(9)

In those equations, Sxx and AKxx, respectively, represent the sub-matrices of S and AK, with regard to theretrieved gas species.
3.1.3 Pre‑launch evaluation results based on the designed
value of TANSO‑FTS‑2

Compared to TANSO-FTS, TANSO-FTS-2 SWIR
bands show higher SNR values. Its spectral coverage
is expanded and optimized for CO and SIF observation (Table 6). This section presents our investigation of
the effects of these differences in TANSO-FTS-2 SWIR
design on the posterior error (Eq. 7), a column averaging
kernel (Eq. 8), and DFS (Eq. 9).

Table 6 Spectral coverage and SNR of SWIR bands of TANSO-FTS
and TANSO-FTS-2
TANSO-FTS

Band 1−1Band 2

Band 3Spectral coverage ­[cm ]12,900–13,200

5800–6400

4800–5200

SNR

343

316

410

Spectral coverage ­[cm−1]

12,950–13,250

5900–6400

4200–5200

SNR

562

509

379

The SNR is defined under the diffusive reflectivity of 0.3 and the solar zenith
angle of 30°. The SNR of TANSO-FTS is the value in the final performance test.
The SNR of TANSO-FTS-2 is the designed value

For the evaluation, the three cases shown in Table 7 areconsidered. The effectiveness of the improved SNR andspectral coverage is evaluated, respectively, by comparing
cases 1 and 2, and cases 2 and 3. To make the comparison conditions uniform, the actual observation geometricconditions of TANSO-FTS are used; also, the state vector
elements and a priori values used to derive the TANSOFTS- SWIR L2 product version 02 (Yoshida et al. 2011,2013) are used. However, the variance–covariance matrix
Sa for the a priori value of the gas profile is a diagonal
matrix. Also, the diagonal elements are set as (10%)2 for­CO2, ­CH4, and H­ 2O, and (100%)2 for CO. Furthermore,
the variance–covariance matrix for observation error Sεis a diagonal matrix. For simplicity, its diagonal elements
are assumed to consist only of shot noise, which is proportional to the square root of the total number of photons incident on the detector. Under these conditions,evaluation is conducted using 15,751 observation geometric conditions of TANSO-FTS in January, April, July,and October 2011.
Figure 13 presents an example of the averaging kernel
matrix (Eq. 4) and the column averaging kernel (Eq. 8).
The averaging kernel matrix and the column averaging kernel, respectively, represent the sensitivity of theretrieved profile and Xgas to the actual (“true”) profile oftarget gas. With the improvement of SNR (cases 1 and 2)
and the expansion of the wavenumber range (cases 2 and3), the averaging kernel matrix is changed, but its effecton the column averaging kernel is small. As the SNR
improves, the column averaging kernel tends to approach
unity, albeit slightly. In any case, the column averaging
kernel shows a value that is close to unity over the wholeatmospheric layer, indicating that it has sufficient sensitivity to Xgas.
Figure 14 portrays histograms of DFS and posterior
error. With the improvement of SNR and the expansion of the wavenumber range, DFS increases and posterior error decreases. Regarding ­XCO2, almost no
difference exists between cases 2 and 3 because thewavenumber range added to case 3 does not include

Table 7 Setting of pre-launch evaluation condition
Case 1Case 2

Case 3

Target gas species

CO2, ­CH4, ­H2O

CO2, ­CH4, ­H2O

CO2, ­CH4, ­H2O, CO

Wavenumber range for retrieval ­[cm−1]

12,950–13,200

12,950–13,200

12,950–13,200 ­(O2)

6180–6380

6180–6380

6180–6380 ­(CO2, ­H2O)

5900–6150

5900–6150

5900–6150 ­(CH4, ­H2O)

4800–4900

4800–4900

4800–4900 ­(CO2, ­H2O)

TANSO-FTS final performance test

TANSO-FTS-2 designed value

TANSO-FTS-2 designed value

4200–4300 ­(CH4, ­H2O, CO)
SNR condition

Gas with an absorption band within the wavenumber range is shown in the column of case 3

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 13 of 35

Fig. 13 Example of the averaging kernel matrix (colored lines) and the column averaging kernel (black line). They are calculated under the diffusivereflectivity of 0.3 and the solar zenith angle of 30°. It corresponds to cases 1, 2, and 3 from the top, and ­CO2, ­CH4, ­H2O, and CO from the left. The
vertical axis corresponds to the layer number of the atmospheric layer. It is counted from the top-of-the-atmosphere toward the surface. Colorsof the averaging kernel matrix also correspond to the layer number of the atmospheric layer, in the order of purple (top-of-the-atmosphere), blue,
green, orange, and red (surface)the ­CO2 absorption band. Because DFS exceeds unity
in most cases, it can be confirmed that the design resultof TANSO-FTS-2 has performance that is sufficient for
retrieving Xgas. The improvement of SNR contributes
greatly to reduction of the posterior error. The posterior
errors of ­XCO2, ­XCH4, and ­XH2O are suppressed, respectively, to about 0.85, 0.55, and 0.80 times for GOSAT-2(case 3) compared to GOSAT (case 1). However, because
this evaluation is based on the designed value of TANSOFTS-2, there is a simplified aspect to the evaluation. It isappropriate to regard it as a result obtained under ideal
conditions. It is necessary to make a more realistic evaluation using actual characteristics of TANSO-FTS-2.
3.2 TANSO‑FTS‑2 TIR
3.2.1 Greenhouse gases and temperature

We have adopted a nonlinear MAP method with linear mapping (Rodgers 2000) to retrieve vertical profilesof greenhouse gases and temperature from the thermal infrared (TIR) bands (Bands 4 and 5) of TANSOFTS-2 on GOSAT-2, as is true also for the operational
data processing of the data of TANSO-FTS on GOSAT(Saitoh et al. 2009, 2016). Regarding retrieval, we haveintroduced linear mapping between vertical layer grids
for radiative transfer calculation (full grids) and grids for
retrieval calculation (retrieval grids). The retrieval grids,
which are defined as a subset of the full grids, are fixed
for all retrieval processing.
The main target gases of the TIR bands of TANSOFTS-2 are C­ O2, ­CH4, ­H2O, ­O3, and ­N2O. Vertical profilesof ­CO2, ­O3, and temperature are retrieved simultaneously
from TIR Band 5 spectra at around 15 μm (690–750 ­cm−1
and 790–795 ­cm−1), 10 μm (930–990 ­cm−1), and 9 μm
(1040–1090 ­cm−1) absorption bands. Vertical profiles of­CH4, ­H2O, and ­N2O are retrieved simultaneously fromTIR Band 4 and 5 spectra at around 7 μm and 8 μm
absorption bands (1140–1370 ­cm−1).
A priori and initial values of meteorological parameters (temperature and water vapor) and the ozone fieldused in TIR retrieval were referred from the Japan Meteorological Agency (JMA) Japanese 55-year Reanalysis(JRA-55). Profiles of a priori and initial concentrationsof ­CO2 and ­CH4 were taken from outputs of the Nonhydrostatic ICosahedral Atmospheric Model (NICAM)based Transport Model (TM) (Niwa et al. 2011), which
are the same as those used for SWIR retrieval. Profiles

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 14 of 35

Fig. 14 Histograms of the DFS (left) and posterior error (right) for 15,751 cases in January, April, July, and October 2011. They corresponds to ­XCO2,­ CH4, ­XH2O, and XCO from the topXof a priori and initial concentrations of ­N2O are takenfrom outputs of the Atmospheric Chemistry Transport
Model (ACTM) (Ishijima et al. 2010). A priori covariancematrices for these target parameters are set as diagonal
matrices with fixed values as elements throughout a vertical profile. Similarly to GOSAT, variation with a standard deviation of approximately 2.5% for ­CO2 is assumed
for an entire vertical profile (Saitoh et al. 2016). For TIR

retrieval, we used the Line-By-Line Radiative Transfer
Model (LBLRTM) (Clough et al. 2005) and the Highresolution Transmission Molecular Absorption Database(HITRAN) (Rothman et al. 2005, 2009, 2013) for lineby-line calculations of optical thickness for each atmospheric layer of the full grids.
Figure 15a shows typical averaging kernel functions (Rodgers 2000) of C
­ O2 and C­ H4 retrievals from

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 15 of 35

3.2.2 Other minor constituents

Fig. 15 a Averaging kernel functions of GOSAT-2/TANSO-FTS-2 TIR(left) and those of GOSAT/TANSO-FTS TIR (right). Upper panels show
results for ­CO2; lower panels show results for ­CH4. All functions arecalculated under the same atmospheric conditions

TANSO-FTS-2 TIR spectra for atmospheric conditions at low latitudes. For calculating the averaging
kernels, we have assumed signal-to-noise ratio (SNR)
values for Bands 4 and 5 of TANSO-FTS-2, whichwere evaluated at the TANSO-FTS-2 Critical Design
Review (CDR). The C­ O2 and C­ H4 averaging kernel
functions for the TANSO-FTS TIR band calculated
under the same atmospheric conditions are depicted
in Fig. 15b for reference. As depicted in Fig. 15, theTANSO-FTS-2 TIR spectra include more information
related to C
­ O2 and ­C H4 concentrations than TANSOFTS. The much higher sensitivity to ­C H4 concentration of TANSO-FTS-2 is attributable to the high SNRvalues of TANSO-FTS-2 Band 4 (1045 at 1300 ­cm−1).

Other than the main targets presented in Sect. 3.2.1,
several species have absorption bands in spectral regions
from 700 to 1800 ­cm−1 obtained from Band 4 and 5 of
TANSO-FTS-2. The species which might be retrieved
from TANSO-FTS-2 are isotopologues of water vapor(HDO and H­ 218O), nitric acid ­(HNO3), ammonia (­NH3),
methanol ­(CH3OH), formic acid (HCOOH), ethene­(C2H4), peroxyacetyl nitrate (PAN), isoprene (­ C5H8), sulfur dioxide (­ SO2), trichlorofluoromethane (CFC-11), anddichlorofluoromethane (CFC-12). The species (except toCFCs) had been retrieved from past and current spaceborne nadir-viewing TIR sounders (e.g., Beer et al. 2008;
Coheur et al. 2009). The products available from one of
the sounders, Infrared Atmospheric Sounding Interferometer are listed in Clerbaux et al. (2009). HDO (Boesch
et al. 2013; Ohyama et al. 2013) and ammonia (Someya
et al. 2020) were retrieved from TIR spectra measured by
TANSO-FTS on GOSAT.
The abundances of the species are much less thanthe main absorbers ­(H2O, ­CO2, ­CH4, and ­O3). Moreover, their absorption signals are generally weaker thanthose of the main absorbers. Retrievals of the species are
subject to interference from these absorbers and fromatmospheric temperature, surface temperature, and surface emissivity because of the overlaps of the absorption bands and water vapor continuum. Therefore, theretrievals must incorporate consideration of these influences. The retrieval algorithms used for earlier studies
particularly addressing the species are typically based on
the MAP solution (Rodgers 2000), which is spectral fitting using the Bayesian theorem, similar to that used forgreenhouse gases. In these algorithms, the interferences
are retrieved simultaneously or separately retrieved inadvance to input the results into the retrieval of the main
target. Because of weak absorption and interference, theinformation contents of the minor species within theobserved spectra are small; also, the retrievals have low
ability to resolve vertical profiles. In fact, TANSO-FTS-2
has the highest spectral resolution among the current
operating space-borne nadir-viewing TIR sounders, withan SNR much higher than that of TANSO-FTS (Sutoet al. 2021, 2022). Therefore, the information contents
and the abilities to resolve vertically for the species are
expected to be enhanced greatly by TANSO-FTS-2.
3.2.3 Clouds and aerosols

The cloud screening process is important for analyzing
gas components such as C
­ O2. For analysis of the TANSOFTS-2 (SWIR) band data, the cloud flag of CAI-2 and thecloud information obtained from the SWIR band dataImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

itself are used together. Similarly, for analysis of TANSOFTS-2 (TIR) band data, the cloud flag of CAI-2 and thecloud information obtained from the TIR band data itself
are used together. However, because CAI-2 is not operated during nighttime, the cloud information obtained
from the TIR data itself will be used alone.
For cloud screening based on the TIR band data, independent methods of three types are used together. The
first is the threshold method for brightness temperature.
In this case, when the value of the difference between theobserved brightness temperature and the ground surface
temperature in the atmospheric window region exceeds
a certain threshold value, it is identified that the field ofview is covered with clouds. At this time, meteorologicalanalysis data are used as the ground surface temperature,
but the surface emissivity estimated from data such as thevegetation index and snow and ice occupancy in the fieldobtained from MODIS data is used. For this method, thecloud top temperature and height in the field of view arederived as products.
The second method is a slicing method using a C­ O2
absorption band. Here, the method described by Someya
et al. (2016) is used, which employs a pseudo-spectral
channel re-constructed by combining spectral channels
having the same peak altitude of the weighting function.
In addition to clouds, this method can detect opticallythick aerosol layers such as yellow sands (Someya et al.
2019). Using this method, the cloud top height in the fieldof view is derived as a product.
The third method is the so-called split window method
proposed by Inoue (1985). Using this method, the cloud
top temperature, the effective cloud particle size, thermodynamic phase of the particles, and their optical
thicknesses are derived as products.
3.3 TANSO‑CAI‑2
3.3.1 Cloud discrimination

The presence of clouds in the instantaneous field of view
(IFOV) of the TANSO-FTS-2 engenders incorrect estimates of greenhouse gas concentrations. Consequently,
an important role of TANSO-CAI-2 is to perform cloud
discrimination to identify and reject cloud-contaminated
TANSO-FTS-2 data. Improving the cloud discrimination
accuracy necessitates refinement of the existing cloud
discrimination algorithm for TANSO-CAI on GOSAT:
the CLoud and Aerosol Unbiased Decision Intellectual
Algorithm used with CAI (CLAUDIA1-CAI) (Ishida andNakajima 2009). A new cloud discrimination algorithm(CLAUDIA3) using a support vector machine (SVM)(Vapnik and Lerner 1963) was developed (Ishida et al.
2018). Primary verification results show that the accuracy of CLAUDIA3-CAI is greater than that of CLAUDIA1-CAI (Oishi et al. 2016). CLAUDIA3-CAI is in the

Page 16 of 35

improvement stage because difficulties to be resolved
were also given in the article. A summary of CLAUDIA3CAI is presented below.
CLAUDIA1 performs cloud discrimination using
thresholds that are set based on experience. CLAUDIA3
uses SVM to decide the thresholds objectively using multivariate analysis. SVM is a supervised pattern recognition method using TANSO-CAI-2 Bands 3–5 (forward)or Bands 8–10 (backward).
First, it determines the following items using training
samples of typical clear and cloudy pixels: (1) a decisionfunction to discriminate between two classifications(clear and cloudy), (2) the thresholds, and (3) the support vectors, which are training samples specified by thedecision function. The support vectors are decided in ahigh-dimensional feature space of the training samples.
Next, it performs cloud discrimination using the decisionfunction, thresholds, and support vectors it determined.
The flow of CLAUDIA3-CAI is depicted in Fig. 16. The
results are expressed as integrated clear-sky confidence
levels (integrated-CCL). Integrated-CCL of 0 expresses
that the pixel is cloudy; 1 signifies that the pixel is cloudfree. Ambiguous pixels between cloudy and cloud-free
are described using numerical values from 0 to 1. For
CLAUDIA3-CAI, the integrated-CCL of 0.5 corresponds
to the separating hyperplane of clear support vectors andcloudy support vectors (Oishi et al. 2017, 2018).
Users can adjust the boundaries which separate cloudand clear-sky areas with the integrated-CCL thresholds.
They can also discriminate the presence of clouds. Figure 17 presents examples of an input CAI Level 1B product image and its cloud discrimination result obtainedusing CLAUDIA3-CAI.
TANSO-CAI-2 has forward- (+ 20°) and backwardlooking (− 20°) bands. Because cloud discrimination will
be performed in each direction, the difference between
forward-looking and backward-looking bands of cloud
discrimination has been discussed (Oishi et al. 2017,2018). The use of the forward and backward cloud discrimination during processing for calculating greenhousegas concentrations (Oishi et al. 2015) is examined.
3.3.2 Aerosol products

TANSO-CAI-2 takes two-directional observations in forward and backward directions. Ten observation bands
and seven wavelengths exist, as presented in Table 3.
The spatial resolution (IFOV) is 920 m at a 1630 nm
wavelength; it is 460 m at the other wavelengths. Actually, TANSO-CAI-2 is characterized by having two
wavelengths in the ultraviolet (UV) region of 340 nm
and 380 nm that are sensitive to aerosol light absorption characteristics. Therefore, the retrieval accuracy ofImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 17 of 35

Fig. 16 Flowchart for CLAUDIA3-CAI, where CCL denotes the clear-sky confidence level, Rwavelength signifies reflectance, and NDVI is the normalized
difference vegetation index

aerosol absorption is expected to be enhanced. Particle
size information can also be estimated using data of thevisible-near-infrared band.
The TANSO-CAI-2 Level 2 aerosol products areintended to derive the aerosol optical thickness at a1630 nm wavelength (AOT1630), which is necessary
for improving the ­CO2 derivation accuracy by TANSOFTS-2. In addition, the retrieved aerosol products are
the aerosol optical thickness at a 550 nm wavelength(AOT550) as an amount of the atmospheric aerosols,
the aerosol Ångström exponent (AE) as particle size
information, ePM2.5 as an equivalent amount of PM2.5
to elucidate the anthropogenic air pollutants, and theblack carbon volume fraction (BCF). The derived resolution of the product is 2 km × 2 km in the Asian region,and 5 km × 5 km otherwise (Fig. 18). Table 8 presents the
TANSO-CAI-2 L2 aerosol property products and therequired accuracy.
3.3.3 Aerosol retrieval algorithm

The multi-wavelength and multi-pixel method (MWPM)(Hashimoto and Nakajima 2017) is applied as the algorithm
to retrieve TANSO-CAI-2 Level 2 aerosol products. The
MWPM was developed for accurate derivation of aerosol
characteristics, even in areas with inhomogeneous surface
reflectance, such as urban areas for which it was difficult

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 18 of 35
R = Ag + πmm0 ωP(�) − (m + m0 )Ag 1 − ωγ +
+m0 ωγ − A2g τ + O(τ 2 )Fig. 17 Example of cloud discrimination result obtained using
CLAUDIA3-CAI. a CAI L1B image (R, Band 2; G, Band 3; B, Band 1). b
Output image from CLAUDIA3-CAI. c Result for an integrated-CCL
threshold of 0.5

to derive aerosol characteristics from satellite images. Thismethod expands the principle of the Kaufman Neutral
method (Kaufman 1987) and simultaneously retrieves
aerosol optical thickness and light absorption characteristics. It does so by simultaneously analyzing the observation
points in the region where reflectance in the spatial direction changes.
The MWPM uses a combination of surfaces with different reflectance on the assumption of an aerosol that is distributed smoothly over the surfaces, as depicted in Fig. 19.
Equation 10 expresses the apparent reflectance (R) by a single scattering approximation on a Lambertian ground surface as shown below.

(10)
Therein, Ag stands for the surface reflectance; m and
m0, respectively, represent inverses of the cosines of incident and scattered zenith angles. τ, ω, and P(Θ), respectively, denote the AOT, single scattering albedo (SSA),
and phase function of the atmospheric air mass at scattering angle Θ. Also, γ+ and γ−, respectively, represent
the downward and upward fractions of light scattering.
The neutral reflectance (Rc) is a case in which Rc = R = Ag
in Eq. 10 corresponding to the middle case of Fig. 19
when a path radiance (first term on the right-hand side,RHS) and surface-reflected radiance (second term of theRHS) are canceled out, irrespective of the value of τ. For
low-reflectance and high-reflectance surfaces, aerosols in
the atmosphere, respectively, increase and decrease theapparent reflectance at the top of the atmosphere (TOA)with Rc as a boundary, depending on the light absorption
characteristics of the aerosol particles. Presuming that
AOT and other aerosol properties in the atmosphere do
not change considerably in the horizontal direction, an
optimal set of aerosol parameters (e.g., AOT, SSA, Ag)is found. Therefore, AOT and SSA are obtained by solving a set of radiative transfer equations (RTEs) using the
spatial differences of surface reflectance (Hashimoto andNakajima 2017).
The optimal method and smoothing constraint foratmospheric parameters are combined to solve theinverse problems for aerosol properties. Bayes’ theorem is applied as an optimal method of problem solving
to maximize the posterior probability density function
P(u|R) as shown below.
P(u|R) =P(R|u)P(u)
P(R)

(11)

Therein, P(u) is a probability density function of eventu. Also, P(u|R) is a conditional probability density function where event u occurs under event R. Then, thesmoothing constraint for atmospheric aerosol properties(i.e., a condition of minimizing the square of the second
derivative of the aerosol spatial distribution with smoothing coefficients) is added to the optimal method.
T −1
φ = [R − f (u)]T S−1
e [R − f (u)] + (u − ua ) Sa (u − ua )
 
T 

+
γix ub,ix + Dix u ub,ix + Dix u
ix

+


ix

T 


γix ub,ix + Dix u ub,ix + Dix u(12)Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 19 of 35

Fig. 18 Image of CAI-2 aerosol product with resolution of 2 km × 2 km for the Asian region corresponding to GOSAT-2 passes 4–35 and frames
13–20, and 5 km × 5 km for other areas

Table 8 GOSAT-2 CAI-2 L2 aerosol products
Product nameAccuracy

Unit

Resolution
2 km × 2 km &5 km × 5 km

AOT5500.1–

AOT1600

0.1

–

AE

0.3

–

BCF

0.1

–

ePM2.5

20

µg/m3

1
1
= Sx =γ�x

 x+�x  2 2
∂ u
dx,
∂x2
x

Rij = [lnRband1 lnRband2 · · · lnRband10 ]T
ij ,

(13)
(14)

for the state and a priori vectors. Actually, τfine , τseasalt ,and τcoarse , respectively, denote fine, sea, and coarse
mode of aerosol optical thickness at 550 nm wavelength: AOT550fine, AOT550sea, and AOT550coarse.
Total AOT550 is the sum of these AOT550s. fBC is thevolume fraction of black carbon (BCF). Ag,bandN is thesurface albedo at band N. Subscript “a” represents thea priori value. Over the ocean, the wind speed at 10 m
above sea level is used instead of the surface albedo.
Five bands of forward or backward viewing are used,
respectively, for analysis to avoid the sun glint region.
Se and Sa represent the covariance matrices of measurement errors and a priori values. B is the boundary condition for the state vector. f(u) denotes a forward model(i.e., radiative transfer model). The forward model
incorporates the radiative transfer model, acceleratedTuij = lnτfine lnτseasalt lnτcoarse lnfBC lnAg,band1 lnAg,band2 · · · lnAg,band10 ij ,

(15)


T
ua,ij = lnτa,fine lnτa,seasalt lnτa,coarse lnfBCa lnAga,band1 lnAga,band2 · · · lnAga,band10 ij ,(16)

In those equations, γ denotes the smoothing factor, x stands for a certain pixel, and ∆x is the distance
between two adjacent pixels to calculate the parameter’s smoothness. Also, R is the measurement vector(i.e., observed apparent reflectance) at each measurement band. In addition, u and ua, respectively, stand

RSTAR using a neural network method (Takenaka et al.
2011). Developed at the University of Tokyo, RSTAR(Nakajima and Tanaka 1986, 1988; Sekiguchi and Nakajima 2008) can simulate radiation fields in the atmosphere–land–ocean system. Both AOT550 and BCF arederived by solving Eq. (12).

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 20 of 35

α=−Fig. 19 Schematic illustration of the multi-wavelengthand multi-pixel (MWMP) method. Satellite-received radiances
for the smoothly distributed aerosol layer above Earth’s surfaceof various reflectance (Hashimoto and Nakajima 2017)

For analyses, the region to be analyzed is divided into
several small regions designated as subdomains (e.g.,
5 × 5 pixels). Aerosol properties at each pixel in the
subdomains are solved sequentially with a boundary
condition.
3.3.4 Aerosol retrieval procedure

Figure 20 portrays the procedure for deriving TANSOCAI-2 aerosol characteristics. First, using the observed
data for one month before and after the productretrieval date (accounting for a total of 11 days), thesurface reflectance is derived by the minimum and second minimum method (Fukuda et al. 2013). Then, using
the surface reflectance and meteorological field data,
aerosol properties such as total AOT550 and AOT1600
and BCF are retrieved by the MWPM. The AOT at eachwavelength including 1600 nm is also derived from theretrieved AOT550s and the assumed aerosol model.
Then, the Ångstrom exponent (AE) and ePM2.5 are calculated using the retrieved AOTs and BCF.
Using the wavelength characteristics of retrieved
AOT at wavelengths of 441, 546, 672, and 865 nm, AE iscalculated as

n

n
n
i=1 ln(τi )i=1 ln(i )i=1 ln(i )ln(τi ) −
,
n
2
n
2
n i=1 {ln(i )} −
ln(
)
i
i=1

n

(17)
where α is the Ångström exponent, τi stands for the total
amount of AOT at wavelength i,  expresses the wavelength, and n represents the number of wavelengths used
to calculate AE.
ePM2.5 is derived from the retrieved BCF, fine mode of
AOT550, and the assumed aerosol particle size as
ePM2.5 = rmax1 
1 − fBC ρsulfate + fBC ρBCmdvfined ln r.
rmin dlnr(18)
Therein, m is the aerosol height information parameterat unit meter. Also, ρsulfate and ρBC , respectively, denotethe sulfate and black carbon particle densities. In addition, r stands for the particle radius; dvfine /dlnr represents the volume size distribution of aerosol particles infine mode.

4 Calibration and validation
4.1 Calibration
4.1.1 Overview

Sensor calibration is performed by pre-flight test (PFT)
before the launch, and again on-board, with vicarious
calibrations after launching. The TANSO-FTS-2 SWIR
band and TANSO-CAI-2 observe sunlight reflected fromthe Earth’s surface. The PFT is performed to evaluate thesensor output to radiance by the integrating sphere lightsource, which is calibrated using a standard radiometer.
Consequently, the observation outputs can be converted
into radiance by application of the radiometric conversion factors determined by the PFT results. Observation
radiance of the TANSO-FTS-2 TIR band is achieved byconversion from the sensor output calibrated with deep
space as a cold target and the onboard blackbody as a
hot target on orbit. In the PFT, it is necessary to obtain anonlinear response of detectors and polarization characteristics of the optics, which is difficult to determine onorbit.
4.1.2 Onboard calibration

Fig. 20 CAI-2 aerosol property product derivation flow

The onboard calibration plan for TANSO-FTS-2 is presented in Table 9. Integrating sphere lasers are carried toconfirm the instrument line shape (ILS) of Band 1 andBand 2. Thereby, the temporal stability on orbit can betracked because of the PFT. Double-sided diffusers forsolar irradiance calibration are operated to evaluate thedegradation of the radiometric response of the SWIR
band by observing both sides alternately. Lunar calibration for radiometric calibration for the SWIR band isplanned for observation at the phase angle of + 7° waning

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 21 of 35

Table 9 Onboard calibration for FTS-2
Onboard calibration

SWIR
B1

TIR
B2

B3

ILS laser sphere

X

X

Solar diffusers (both sides)

X

X

X

Lunar calibration (Park to target)

X

X

X

B4

Purpose, frequency
B5
ILS stability because the PFT (once per 6 days)

Deep space

Radiometric response degradation (one side per orbit, the other side per 3 months)
Radiometric response degradation (per month, + 7° waning near Full Moon)
X

X

TIR onboard radiometric calibration (2 times in dayside, four times in nightside orbit)

Blackbody

XX

TIR onboard radiometric calibration (2 times in dayside, four times in nightside orbit)

Phase transition cell

X

X

Blackbody thermometer verification (if necessary)

FOV camera

FOV confirmation (2 times per sounding in dayside orbit)

Table 10 Onboard calibration for CAI-2
Onboard calibration

Forward-looking Backward(+ 20°)
looking (− 20°)

Purpose, frequency

B1–B5

B6–B10

Lunar calibration (AT scan)

X

X

Radiometric response degradation (per month, + 7° waning near full moon)

Lunar calibration (CT scan)

X

X

Pixel-to-pixel response correction (per 2 months, -7° waxing near full moon)Nighttime calibration

X

X

Dark-time output (once per month, night ocean near new moon)near a Full Moon. The appearance of the Moon is half thesize of the TANSO-FTS-2 field of view (FOV). Radiometric calibration of the TIR band is performed on orbit bydeep space and onboard blackbody two times daysideand four times nightside. The phase transit cell is carried
to confirm the performance of the thermometer attachedto the onboard blackbody. The FOV camera is operated
to take photographs before and after intelligent pointing
for the detection of cloud information of each sounding.
The onboard calibration plan for the CAI-2 is shownin Table 10. The TANSO-CAI-2 onboard calibration is operated by lunar calibration and nighttime
calibration; it is not equipped with onboard calibrator. The lunar calibration for radiometry is planned toobserve at phase angle of + 7° waning near Full Moonby along-track (AT) scanning with center pixels. The
GOSAT-2 satellite is targeted to the Moon at a pitch
angle of + 20° for forward-looking bands, and at − 20°for backward-looking bands. The lunar calibration for
pixel-to-pixel response correction is operated by crosstrack (CT) scanning with all pixels at a phase angle of −7° waxing near the Full Moon. Nighttime calibration for
dark-time output is planned to observe the dark night
ocean near a New Moon.
4.1.3 Vicarious calibration

Vicarious calibration after the launch is shown in
Table 11 for the TANSO-FTS-2, and in Table 12 for
the TANSO-CAI-2. A field campaign is conducted insummer at Railroad Valley (RRV) Playa, Nevada, USA,

Table 11 Vicarious calibration for FTS-2
Vicarious calibration

RRV field campaign

SWIR

TIR

B1

B2

B3

X

X

X

X

X

X

X

X

X

X

X

X

B4

Purpose, frequency
B5
Radiometric calibration (once per year, near summer solstice)

Inter-satellite comparison

Radiometric comparison with,X

Geometry

GOSAT/FTS (1–2 days in 6-day revisit)
OCO-2, TROPOMIXCEOS site monitor

X
X

AIRS, IASI, CrISRadiometric monitor in several desert sites of CEOS recommendation (every 6 days)
FOV camera geolocation compared with reference image

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 22 of 35

Table 12 Vicarious calibration for CAI-2
Vicarious calibration

RRV field campaign

Forward-looking

Backward-looking

B1–B5

B6–B10Purpose, frequency

All bands

All bands

Radiometric calibration (once per year, near summer solstice)

B2–B5

B7–B10AERONET OC, VIIRS land desert radiance

B1

B6

MOBY nLw data

B2–B5

B7–B10VIIRS

Cross-calibration method

Radiometric calibration using,Inter-satellite comparison

Radiometric comparison with,
B1

OMPS
B6

OMI

CEOS site monitor

All bands

All bands

Radiometric monitor in several desert sites of CEOS recommendation(every 6 days)

Geometry

B4

B9Absolute geolocation as a standard band compared with reference
image

B1–B3, B5 relative to B4B6–B8, B10 relative to B9Band-to-band registration relative to the standard bandby measuring the surface reflectance coincident withGOSAT-2 target observation for vicarious radiometric calibration of the TANSO-FTS-2 SWIR band andTANSO-CAI-2. The RRV experiment is a collaboration
with NASA OCO-2/-3 team and other greenhouse gas
projects. Cross-calibration for the TANSO-CAI-2 VNIR
band is evaluated at the dark ocean target using AERONET Ocean Color (OC), and at a bright land desert target using VIIRS observed radiance. Those for UV bands 1
and 6 are evaluated using Marine Optical BuoY (MOBY)
water leaving radiance (nLw). Inter-satellite comparisons with data from similar sensors, especially those of
GOSAT, are planned that the TANSO-FTS-2 targets atsome GOSAT TANSO-FTS observation points during
1 or 2 days in the GOSAT-2 6-day revisit, both at close
orbits. The TANSO-FTS-2 SWIR band is compared with
OCO-2 and TROPOMI, and the TIR band with AIRS,
IASI and CrIS using simultaneous observation data. The
TANSO-CAI-2 VNIR band is compared with VIIRS,
whereas the UV band is compared with OMPS for Band1, and with OMI for Band 6. The temporal stability of
the radiometric response is also evaluated by monitoring desert sites recommended by Committee on Earth
Observation Satellites (CEOS).
Geolocation is evaluated by comparison with a reference image after launching. The TANSO-FTS-2 geolocation is evaluated using a FOV camera image. The
TANSO-CAI-2 geolocation is evaluated as a standard
in Band 4 for forward-looking bands, and in Band 9 forbackward-looking bands. Other bands are evaluatedas band-to-band registration relative to the standardband.

4.2 Validation
4.2.1 Purpose and outline of validation

The product data observed using satellite remote sensing
techniques are necessary for evaluation with other independent observation data for their scientific and politicaluses. Level 2 and higher level products will be validated
and compared, respectively, whereas level 1 products will
be calibrated. For validation, the product is evaluatedwith other independent observation data with better precision and well-known bias. For comparison, the productis evaluated with other independent observation datawith equivalent or less precise or model data. During calibration, a physical quantity such as radiance is evaluatedwith independent observation data having better precision. Well-known bias and characteristics of instruments
are assessed using various measurement methods.
A procedure for validation is the following, for example, selection of the validation data, quality check andscreening of validation and satellite data, colocation,summary statistics, correlation analysis, and bias correction. For selection of the validation data, similarity, precision, and bias of data should be considered. Similarity
signifies a similar physical quantity such as the columnaveraged dry mole fraction such as X
­ CO2 or a profile ofthe dry air mole fraction to satellite data. If each groupof data differs in quantity for validation and satellite data,then satellite or validation data conversion should bemade of into same physical units. During quality checking and validation of satellite data, anomalies caused byinstruments, weather conditions, and some phenomena
should be discriminated according to the purpose of thevalidation level, such as preliminary, routine, geophysical,

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

advanced, and special-purpose detection for gas emission using statistical techniques. After quality checking,they should be removed. For colocation, various methods
are used, such as geographical (Inoue et al. 2013), T700
(Wunch et al. 2011a), model-based (Guerlet et al. 2013),
and geostatistical (Nguyen et al. 2014). They are summarized in a report by Nguyen et al. (2014). Then, validationand satellite data are made summary statistics (mean andmedian with or without weight) with or without averaging kernel and prior corrections. Next, for correlation analysis, validation and satellite data are comparedwith or without weights. When weights for both data areconsidered for correlation analysis, the so-called Yorkmethod is used. Characteristics such as bias and scatterare eventually estimated. If there are validation criteria for each product as a satellite mission and statistics
achieve them, the products are recognized as validated.
However, statistical examination using this procedurecan be applied for validation. If possible, satellite data
are recommended to be corrected against their biases
using empirical linear regression. This motivation andvarious methods are summarized in a report by Inoueet al. (2016). Normally, correlation analyses between thesatellite biases and simultaneously retrieved auxiliary
parameters are made. These correlations are used to biascorrect the GOSAT data, thereby removing these spurious correlations.
4.2.2 Overview of the GOSAT‑2 products validation planAs validation sources for X­ CO2, ­XCH4, XCO, and X­ H2O
retrieved from the SWIR spectra onboard GOSAT-2
TANSO-FTS-2, TCCON is a primary validation source.
TCCON is a worldwide network of ground-based FTSs
that provide time series of column-averaged abundancesof various atmospheric constituents. These constituents
including ­CO2, ­CH4, ­N2O, HF, CO, H­ 2O, and HDO are
retrieved from near-infrared solar absorption spectra
similar to GOSAT-2 using a nonlinear least-squares fitting algorithm known as GFIT (Wunch et al. 2010, 2011a,
2011b). The TCCON data are also useful for the validation of satellite vertical profiles of C
­ O2 and ­CH4 afterconversion into the same physical quantities. In addition
to the TCCON data, the obtained Network for the Detection of Atmospheric Composition Change (NDACC)FTS data are useful. The Infrared Working Group(IRWG) represents a network of infrared Fourier-transform spectrometers that is part of NDACC. An overview
of NDACC is described in a report by De Mazière et al.
(2018): It is a multi-national collection of more than 20
high-resolution spectrometers that regularly record theatmospheric absorption spectrum from sites distributed from pole to pole. The absorption spectra are used
to retrieve concentrations of a number of the gaseousPage 23 of 35

atmospheric constituents, including O­ 3, ­HNO3, HCl, HF,
CO, ­N2O, ­CH4, HCN, ­C2H6, and ­ClONO2. Because the
NDACC FTS data are column abundances and verticalprofiles of constituents, they are useful to validate SWIR
and TIR products of GOSAT-2. In addition to FTS data,
DIAL (e.g., C­ O2, Sakaizawa et al. 2009; ­O3, Uchino et al.
2017), other portable solar light observation instruments
such as portable FTS network called COllaborative Carbon Column Observing Network (COCCON) (Frey et al.
2019), Optical Spectrum Analyzer (OSA, Kobayashi et al.
2010; Kawasaki et al. 2012), fiber-etalon sun-photometer(Kobayashi et al. 2010; Kawasaki et al. 2012), and a laser
heterodyne radiometer (Wilson et al. 2014) are used.
Profiles of ­CO2, ­CH4, ­H2O, and other gases retrieved
from the TIR spectra can be validated using aircraft datasuch as the Comprehensive Observation Network for
TRace gases by AIrLiner project (CONTRAIL) (Machida
et al. 2008), In-service Aircraft for a Global Observing
System (IAGOS) core (Filges et al. 2015), the NOAA
Earth System Research Laboratory/Global Monitoring
Division (ESRL/GMD) (Xiong et al. 2008; Sweeney et al.
2015), the US Department of Energy (DOE) (Biraud et al.
2013; Schmid et al. 2014), and C­ O2 sonde (Ouchi et al.
2019), and AirCore (Karion et al. 2010). Their profiles canalso be useful for validating the SWIR products by conversion into Xgas.
Surface measurements of greenhouse gases (e.g.,
WDCGG 2022) can also provide a useful comparison for
SWIR and TIR products.
Satellite data of greenhouse gases are useful for comparison to GOSAT-2 products. SWIR data from GOSAT,OCO-2, TanSat, TROPOMI are contributed to compare
GOSAT-2 SWIR products. TIR data from AIRS, IASI,
MOPITT, etc., are useful to compare the GOSAT-2 TIR
products.
Solar-induced fluorescence (SIF) provides reliable
information of plant physiological functioning at a large
spatial scale. Actually, SIF can be validated using groundbased (e.g., Julitta et al. 2016) and aircraft (e.g., Garzonio
et al. 2017) SIF data recorded with a hyperspectral spectrometer. Moreover, SIF can be compared with satellite
data provided by GOME-2, GOSAT, OCO-2, TanSat,
FLEX, and others, in addition to vegetation model data.
In situ data and ground-based remote sensing datafrom AERONET and SKYNET (Nakajima et al. 2020a,
2020b) are used to validate PM2.5 and black carbon (BC)from TANSO-CAI-2. Satellite data from MODIS, EarthCARE, and others can also be useful for comparison. As
advanced validation to improve the GOSAT data quality
further, investigation can be made about the effects ofaerosols and thin cirrus clouds on the GOSAT-2 productover Rivulets, Tsukuba, Saga, Burgos, and Lauder using
FTS, lidar, and skyradiometer.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Campaign observation is expected to be important
where validation data are sparse. Under such circumstances, combinations of portable instruments andaircraft and satellite observation are recommended
according to the campaign purposes.
4.2.3 Ongoing validation activities of the GOSAT‑2 products

After starting the L2 processing, we have been conducting validation analysis using TCCON data for ­XCO2,­XCH4, XCO, and X­ H2O. We have made evaluations of
the influences of aerosol and thin cirrus on them using
data obtained from skyradiometer and lidar. We also havestarted intercomparisons of X
­ CO2, ­XCH4, and XCO with
OCO-2, TROPOMI, and model data. The ­CO2 profile has
been compared with those taken from CONTRAIL andmodel data. The SIF data have been compared with thosefrom satellite data from OCO-2, etc. Moreover, the AOT
data from TANSO-CAI-2 have been validated using the
AERONET and SKYNET data; also, BC and PM2.5 havebeen compared with surface BC and PM2.5 data. Someof the validation analyses have been used for review processes in public release.

5 Scientific goals
5.1 Global inversion

At the moment, available estimates of surface fluxes ofgreenhouse gases (GHGs) still have marked degrees of
uncertainty because our understanding of flux mechanisms remains insufficient. Many gaps exist in in situ
observation networks. Satellites, including GOSAT2, that observe atmospheric mole fractions of GHGs
are expected to reduce those uncertainties because of
their wide spatial coverages. Generally, an atmospherictransport model is used to connect surface fluxes withobserved atmospheric mole fractions. Based on thosefindings, an inverse analysis is performed to estimate
surface fluxes. An inverse analysis is useful in that it provides quantitative estimates of spatiotemporal variationsof surface fluxes consistently with observed variations ofatmospheric mole fractions. In a typical global inverse
analysis, surface fluxes are estimated on a much larger
scale (a few 100 km) than a footprint of satellite observations (a few to 10 km), because of the computationally demanding simulations of an atmospheric transportmodel. However, because satellite retrievals represent
partially column-averaged mole fractions and because
they consequently have large representativeness, satellite
observations can be major input data of a global inverse
analysis. Such a global inverse analysis is expected toprovide valuable implications of large-scale (sometimes
interhemispheric scale) spatial patterns and seasonal variations of surface GHG fluxes.

Page 24 of 35

In the GOSAT-2 project, the Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-based Transport Model (NICAM-TM) (Satoh et al. 2014; Niwa et al.
2011) is used to prepare prior estimates of retrieval calculations (i.e., the L2 product). Furthermore, the inverse
analysis based on NICAM-TM, which is designated as
NICAM-based Inverse Simulation for Monitoring (NISMON) ­CO2 and ­CH4, provides surface flux estimates of­CO2 and ­CH4 using each L2 product of GOSAT-2 (i.e.,the L4 products) (Saito et al. 2019). NISMON employs
the 4D-Var method, by which forward and backward
transport simulations are performed iteratively to
optimize fluxes (Niwa et al. 2017a, 2017b). The optimizer is based on a quasi-Newtonian method with the
Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm(Fujii 2005). The L4 products of GOSAT-2 are derived
from NISMON analyses with a horizontal resolution of
ca. 223 km, which is the mean interval of the “glevel-5”
NICAM grid (Fig. 21). It is noteworthy that the NICAM
grid shapes are hexagons or pentagons, which are generated by dividing an icosahedron recursively (Satoh et al.
2014). The temporal resolution of optimized fluxes is set
as monthly. As elaborated in Niwa et al. (2017b), surface
fluxes are optimized with prescribed spatial correlations in the prior errors. In the experiment of reported
by Niwa et al. (2017b) with ground-based observations,the error correlation scales are set as 500 km for terrestrial areas and as 1000 km for oceans. These error correlations act as a smoother and improve flux estimates.
That would be true even for the case with satellite data.
Therefore, although fluxes are optimized at each modelFig. 21 Model grid of the “glevel-5” NICAM. The mean grid interval
is ca. 223 km (Satoh et al. 2014)

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

grid, the practical resolution of the fluxes that the satellite data constrain would be several times as large as themodel grid. This important point must be recognized
before analyzing the L4 products.
Estimated surface fluxes by inversion have theoretical errors named “posterior errors,” which are derived
from diagonal elements of the posterior error covariancematrix. Because that matrix size is n × n, where n denotes
the number of estimated fluxes (i.e., the number of model
grids multiplied by the temporal resolution), it is not atrivial task to obtain posterior errors. However, NISMON
uses a state-of-the-art method developed by Niwa andFujii (2020) to estimate posterior errors. By investigating
the degree to which posterior errors are reduced fromprior errors, one can evaluate the strengths of observational constraints, an example of which is presented inFig. 22. Posterior errors in Fig. 22 are derived using thePage 25 of 35

same settings as those reported by Niwa et al. (2017b),
but the ground-based observations are replaced by the
GOSAT TANSO-FTS SWIR data for 2010. Because
they depend strongly on the settings of prior errors andmodel-observation mismatch errors, which are key tuning parameters of an inversion, the values themselves inFig. 22 might not be valid for a practical satellite inversion. Nevertheless, spatiotemporal patterns give valuable
implications. As shown in Fig. 22, the satellite constraints
are marginal in winter high-latitude areas. This is true
because satellite retrievals are not available where sunlight does not exist. In addition, prior errors of terrestrial
biosphere fluxes are often set as small because of the low
biosphere activity in winter, resulting in weak observational constraints. In summer mid-latitudes to highlatitudes and tropical areas, the satellite constraints are
considerably strong. This fact is especially important for

Fig. 22 Error reduction ratios of ­CO2 flux estimates for each month. They are derived by the same settings of Niwa et al. (2017b), but with theσ −σ
GOSAT-1 SWIR data for 2010. The error reduction ratio is defined as posσpri pri × 100, where σpos and σpri , respectively, represent the posterior
and prior errors. This represents observational constraints. Here, the observation model mismatch uncertainty is assumed as 2 ppm for individual
SWIR dataImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Siberia and tropical areas, where ground-based observation networks are extremely sparse. This picture would
be true also for GOSAT-2.
5.2 Regional targets

Monitoring ­CO2 emissions from large-scale C­ O2 emission sources such as electric power plants and urban
areas with heavy traffic is an important task. To thisend, we have conducted specific observations by
GOSAT targeting urban areas and power plants, particularly, intensive observations of the Kanto Plain,including Tokyo started in 2010. Similarly, we areconducting target observations with GOSAT-2 overmajor megacities around the world. An example of theobserved data is presented in Fig. 23. These observation
data are combined with the ground-based observation
data, after which they are used as input data to regionaltransport models. The ­CO2 emission intensity was estimated using inverse analysis.
By improving the SNR and by reducing the radiancebiases, the estimation accuracy of the boundary layer(mixed layer) altitude obtained from the vertical distribution analysis of the temperature using the TIR bandis improved. Using this information, it is expected thatthe accuracy of the method used for estimating the ­CO2
concentration near the surface layer will be improved

Page 26 of 35

through the combined analysis of the columnar C­ O2
concentration retrieved from SWIR band data and theupper air concentration obtained from the TIR band
(Imasu 2019). This method is particularly effective forurban areas where surface temperatures are high. For
such situations, the thermal contrast, which is definedby the difference between the atmosphere and the surface temperature, is high. For that reason, the resolution of the vertical distribution of air temperature in
the lower atmosphere is expected to be improved.
Another major improvement of TANSO-FTS-2 is thatthe wavelength range of SWIR band 3 for carbon monoxide (CO) measurement has been expanded. Because
CO is a substance of combustion origin, it is expectedthat information related to differences between ­CO2 ofcombustion origin and C­ O2 of vegetation respiratory
origin in urban areas can be obtained (Takekawa et al.
2004).
Hotspots, in a climatological sense, are large carbon
stores and large carbon sinks. These are areas that aresusceptible to climate change and land use changes. They
can be major future sources of greenhouse gas emissions.
Pre-identifying and then monitoring these hotspots continuously will support early detection of signs of carbon
cycle change attributable to climate change.
Specific areas include land areas where global warming effects are expected to be greater at high latitudes,
especially areas where ­CO2 absorption increases withextension of the growing season, areas where ­CO2absorption decreases because of dryness during summer,
areas where ­CO2 and ­CH4 emissions increase because oflarge-scale forest fires, areas where albedo is expected tochange because of early snowmelt, areas where frozen
soil melts and the accompanying increase in C
­ O2 and­CH4 emissions from the soil occurs, and areas where ­CH4
hydrates exist.
5.3 Solar‑induced chlorophyll fluorescence (SIF)

Fig. 23 Example of ­XCO2 composite map observed by GOSAT-2
with its specific target observation conducted from 2019 to 2022.
GOSAT-2 level 2 (Ver.01.07) data observed in winter season(November–February) are used

Photosynthetic ­CO2 assimilation by plant vegetation,
known as gross primary production (GPP), is the largest
carbon flux on the Earth surface; it is also an importantdriver of global climate (Global Carbon Project 2017).
Its rate is extremely sensitive to environmental variation. For that reason, it varies spatiotemporally to a great
degree. To date, many efforts have been undertakenusing various techniques, including satellite remote sensing, to monitor the GPP of various forms of vegetation.
Earlier satellite remote sensing studies have used reflectance spectral indices (e.g., normalized difference vegetation index, NDVI) that estimate plant status related
to structural or pigment properties such as the leaf areaindex (White et al. 1997) and leaf chlorophyll content(Daughtry et al. 2000). Although these data are usefulImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

to predict the potential GPP (Running et al. 2004), they
are insufficient for assessment of physiological changesof the photosynthetic system that often occur withoutchanges in leaf area or chlorophyll content. Therefore,such vegetation indices are insufficient to assess spatiotemporal variations in GPP. In recent years, complementing such reflectance-based indices, solar-induced
chlorophyll fluorescence (SIF) has been used to estimate
GPP and plant stress (e.g., Parazoo et al. 2013, 2014;
Liu et al. 2017). Chlorophyll fluorescence is a radiationin the red and near-infrared regions with two peaks at
approximately 690 and 740 nm emitted by chlorophyll-amolecules. A large part of the light energy absorbed bychlorophyll is used for photochemistry. Therefore, carbon assimilation and photorespiration, to the extent thatit remains, are lost as heat and fluorescence (Baker 2008;
Porcar-Castell et al. 2014). Chlorophyll fluorescence yieldchanges depending on the status of photochemistry andheat dissipation. In fact, it provides various information
related to the photosynthetic machinery (Hikosaka andTsujimoto 2021). However, detecting SIF in the field has
been challenging (Meroni et al. 2009) because the magnitude of SIF is much lower than the reflected radiationfrom leaves. At a leaf scale, pulse amplitude-modulated(PAM) fluorometers have been able to detect chlorophyll
fluorescence. They have been used in many physiologicaland ecological studies. In remote sensing, including satellite observations, SIF can be retrieved using high-spectral
resolution measurements using the Fraunhofer depth
method (Meroni et al. 2009).

Page 27 of 35

The first SIF retrieval from satellite observations on a
global scale was conducted with TANSO-FTS onboard
GOSAT (Frankenberg et al. 2011c, 2012; Joiner et al.
2011; Guanter et al. 2012). Band 1 of TANSO-FTS covers the overlapping part of SIF and Fraunhofer lines in
the near-infrared region with high spectral resolution.
Subsequently, SIF has been retrieved from the data measured by satellites aiming at measuring GHGs concentration, such as SCIAMACHY onboard ENVISAT (Joiner
et al. 2012), GOME-2 of Op-A and B (Joiner et al. 2013),
OCO-2 (Sun et al. 2017), TROPOMI of Sentinel 5P(Guanter et al. 2015; Köehler et al. 2018), and TanSat (Duet al. 2018). These satellite SIF data have been used formutual validation (e.g., Oshio et al. 2019).
TANSO-FTS-2 onboard GOSAT-2 is also available toretrieve SIF. Those SIF data are provided as one standard product. GOSAT-2 is expected to provide SIF datathat have higher validity than those of GOSAT becauseof the intelligent pointing system. Generally speaking,satellites are unable to observe SIF accurately in cloudcovered areas. The intelligent pointing system is expected
to increase the number of clear-sky observations and toenhance our understanding of climate change effects onterrestrial ecosystems, especially in tropical rainforests,
which are often cloud-covered.
Figure 24 portrays a global map of GOSAT-2 SIF of
August 2019. Actually, SIF was greatest in areas withtropical vegetation such as the Amazon, Central Africa,and Southeast Asia, which are known to have the highest
GPP on Earth. By contrast, it was lowest in desert areas

Fig. 24 Global map of GOSAT-2 SIF (TANSO-FTS-2 SWPR L2 product, V) in August 2019. Monthly averages of SIF in 2 × 2 degrees grid are shownImasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

such as the Sahara and Arabia, and the Arctic and Antarctic regions, where vegetation is scarce. Results demonstrated that SIF is correlated linearly with data-driven
GPP estimates at global and/or annual scales (Frankenberg et al. 2011c; Guanter et al. 2012; Sun et al. 2017). The
correlations are rationalized to the light use efficiency(LUE) model for GPP by Monteith (1972). Actually, GPP
is regarded as the product of proportional to incoming
photosynthetically active radiation (PAR), the fraction
absorbed PAR (fAPAR) by vegetation, and the LUE for
GPP. Similarly, SIF is conceptualized as the product ofPAR, fAPAR, LUE for SIF. These equations can be combined as simple correlations between GPP and SIF (Leeet al. 2013; Guanter et al. 2014; Sun et al. 2017). Thisapproach has been used for various ecosystems such as
those of the Amazon rainforest (Parazoo et al. 2013) andthe boreal forests of Eurasia and North America (Walther
et al. 2016). However, the relation between GPP and SIF
differs depending on the season (Thum et al. 2017), vegetation type, and canopy structure (Guanter et al. 2014;
Migliavacca et al. 2017). Another approach is using theradiative transfer models, which simulate underlying
mechanisms such as the biophysiological processes andscattering and reabsorption effects on SIF signals withina canopy. The SCOPE model, developed by van der Tol
et al. (2009), is a 1-D radiative transfer, photosynthesis,
and energy balance model. SCOPE has contributed toassessment of GPP from SIF (Lee et al. 2013; Zhang et al.
2014; Thum et al. 2017).
Using the SCOPE-like model and carbon-cycle models, GOSAT-2’s SIF would enable us to improve the accuracy of the L4 product of GOSAT-2, ­CO2 flux. Accurate
estimation of GPP helps elucidate the consequences
between meteorological conditions and C­ O2 assimilation functions of vegetation, and to predict future trends.
Additionally, used in conjunction with X­ CO2 data, it
might contribute to estimation of anthropogenic ­CO2emissions.
5.4 Air pollution monitoring and related climate studies

One main objective of the GOSAT-2 satellite mission
is air pollution monitoring. As reported by the World
Health Organization (WHO 2016) and the Organization
for Economic Co-operation and Development (OECD2020), global health effects of air pollution are as greatas annual excess mortality of 3–4 million people. Furthermore, the IPCC Special Report on Global Warmingof 1.5° and the Sixth Assessment Report (AR6) assessment report have concluded that marked reduction of
the Short-Lived Climate Forcer (SLCF) along with ­CO2
reduction is necessary to attain the 1.5° reduction target

Page 28 of 35

for global warming mitigation (IPCC 2018, 2021). Recent
studies indicate that a SLCF reduction scenario must be
designed carefully by the full analysis of complex negative and positive feedback processes among the constituents (Stohl et al. 2015; Nakajima et al. 2020a, 2020b). A
small but significant surface temperature decrease by the
total BC reduction, for example, is explained by counterwarming effects of increases in the upper clouds becauseof atmospheric stability change (Takemura and Suzuki2019).
Earth observation by the GOSAT-2 satellite contributes to air pollution monitoring and to collecting useful knowledge for the SLCF climate impacts with theirunique simultaneous measurements of ­CH4, CO, andaerosols with the TANSO-FTS2 sensor with a 9.7 km
FOV and the TANSO-CAI-2 imager with two UV bandsof 340 nm and 380 nm and with a 500 m FOV. The fineFOV of TANSO-CAI-2 is especially useful for pollution
monitoring over heterogeneous land surfaces such as city
areas and coastal zones when data are analyzed using the
MWMP algorithm (Hashimoto and Nakajima 2017; Shi
and Nakajima 2018; Shi et al. 2019). Figure 25 shows thatthe two UV bands can detect smoke aerosols clearly inthe case of an Australian forest fire (JAXA 2020; Nakajima et al. 2021). Estimation of the BC fraction is promising, as reported by Shi et al. (2020) for the Australianforest fire case, and for the Indian case (Gogoi et al. 2021,2022).
Recent reports of close correlation of ­CO2 concentration with short-lived atmospheric materials, i.e., ­NO2and aerosols, in model simulations and airborne and satellite observations around large-scale emission sources(e.g., Goldberg et al. 2019; Fujinawa et al. 2021) indicatethat the combination of GOSAT-series satellites withother satellites for short-lived material observation isa promising method for providing wide-area information that is useful for emission inventory construction
and the United Nations Framework Convention on Climate Change (UNFCCC) global stock taking routine.
Itahashi et al. (2019) demonstrated an update of REAS
­NOx inventory through assimilation of OMI-observed­NO2 data with short turn-around time of less than several years. This benefit is expected to be increased in thenext-phase global monitoring system by Geostationary
Environment Monitoring Spectrometer (GEMS), Tropospheric Emissions: Monitoring Pollution (TEMPO),
and Sentinel-4 geostationary satellites (Kim et al. 2020).
Therefore, designing a satellite that is able to monitor
NOx and GHGs simultaneously is expected to be important for future work.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Page 29 of 35

Fig. 25 Aerosol observation of the Australian forest fire smoke of November 12–13, 2019, by Himawari-8, GCOM-C/SGLI, and GOSAT-2/CAI-2.
Results show that the introduction of two ultraviolet bands is useful to distinguish smoke from clouds. Cited from JAXA (2020) and Nakajima (2021)

6 Conclusions
Compared to the first GOSAT sensors, the GOSAT-2
equipped sensors have demonstrated improved performance in most respects. The quality and quantity ofobservation data are expected to be improved accordingly. The signal-to-noise ratio (SNR) is improved inboth the SWIR and TIR bands of TANSO-FTS-2, whichis the main sensor of GOSAT-2. This improvement will
ultimately enhance the accuracy of greenhouse gas concentration analysis. In addition, because of the improved
SNR in the SWIR band, the northern limit at which data
are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data were
heretofore unavailable because of weak signal strength,
has moved to higher latitudes. As more and better data
are obtained, progress in carbon cycle research for highlatitude regions is anticipated. Moreover, it is estimatedthat the improvement of the SNR in the TIR band canbe considerable. Particularly, the resolution of the vertical concentration distribution of C
­ O2 and ­CH4 has been
improved drastically. Using the C­ O2 column concentration obtained from the SWIR band and the upper air
concentration of ­CO2 obtained from the TIR band incombination, the retrieved ­CO2 concentrations near the
ground surface, which are important for estimating theamount of ­CO2 emission and uptake, are obtainable. This
benefit of GOSAT/GOSAT-2 is not provided by satellites
equipped with other similar sensors.
The first function introduced into GOSAT-2 which has
not been implemented in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using
the in-field camera of TANSO-FTS-2. This function canbe expected to increase the amounts of observation data
globally and to improve the estimation accuracy of ­CO2
emissions and uptake intensity. The effect is expected
to be strong, especially in the tropics, because the mostcommon cloud type is cumulus there. The intelligent
pointing system can avoid clouds effectively.
Another major change of TANSO-FTS-2 is that the
wavelength range of Band 3 of SWIR has been expanded
for measuring carbon monoxide (CO). Because CO is a
substance originating from combustion, it is used to evaluate the effects of human activities in urban areas andbiomass burning in the field. Particularly, black carbontype aerosols can be measured by TANSO-CAI-2 for biomass burning as well as C
­ O2 and CO by TANSO-FTS-2.

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

The partitioning ratio of these three substances mightprovide information that characterizes the fire, such as
combustion completeness. In fact, GOSAT-2 is the only
satellite that can simultaneously measure Short-Lived
Climate Forces (SLCF) such as C
­ H4, BC, and groundlevel ozone together with ­CO2.
The ultimate goal of most greenhouse gas observation
satellite projects, including GOSAT and GOSAT-2, is elucidation of anthropogenic greenhouse gas emissions andtheir effects on the carbon cycle and climate. However, toextract only the effects of anthropogenic origin from theinformation obtained using these satellite-based sensors,
it is necessary to evaluate and eliminate the effects of natural origin which are included therein. Eliminating these
influences is mainly a question of how to achieve accurate
evaluation of ­CO2 emissions (respiration) and absorption(photosynthesis) by terrestrial ecosystems. One method
is cooperation with satellites launched to evaluate thedynamics of terrestrial ecosystems. By combining related
to various activities of terrestrial ecosystems such as thevegetation index and heat dissipation (photochemicalreflection index) obtained from these satellites with SIF
information obtained from GOSAT-2, the physiologicaland ecological activity of vegetation can be better evaluated. In this way, the combined use of various satellite
data makes it possible to acquire more advanced information related to the carbon cycle.
Future research undertaken to elucidate the global carbon cycle including greenhouse gases and research toevaluate the global environment are expected to become
more sophisticated and increasingly complicated. Therefore, achieving sufficient scientific results with onlya single satellite project is difficult. The only solution
is addressing these issues through collaboration withnumerous satellite projects and extensive international
cooperation.
Abbreviations
4D-Var		4-Dimensional variational method
ACTM		Atmospheric chemistry transport modelAE		Ångström exponent
AERONET		AErosol RObotic NETwork
AIRS		Atmospheric InfraRed Sounder
AOT		Aerosol optical thickness
AR6		6Th Assessment Report
BCF		Black carbon volume fraction
BFGS		Broyden–Fletcher–Goldfarb–Shanno
CCL		Clear-sky confidence levels
CEOS		Committee on Earth Observation Satellites
CLAUDIA		CLoud and Aerosol Unbiased Decision Intellectual
Algorithm
COCCON		COllaborative Carbon Column Observing Network
CONTRAIL		Comprehensive Observation Network for TRace gases
by AIrLiner project
CrIS		Cross-track infrared sounder
DFS		Degrees of freedom for signals
DIAL		DIfferential absorption lidar

Page 30 of 35

DOE		Department of energy
EarthCARE		Earth clouds, aerosols and radiation explorer
ENVISAT		ENVIronmental SATellite
ESRL/GMD		Earth System Research Laboratory/Global Monitoring
Division
FLEX		FLuorescence explorer
fAPAR		Fraction of absorbed PAR
GEMS		Geostationary Environment Monitoring Spectrometer
GHG		Greenhouse gas
GOME		Global Ozone Monitoring Experiment
GOSAT		Greenhouse gases observing SATellite
GPP		Gross primary production
HITRAN		HIgh-resolution TRANsmission molecular absorption
IAGOS		In-service Aircraft for a Global Observing System
IASI		Infrared Atmospheric Sounding Interferometer
IFOV		Instantaneous field of view
ILS		Instrument line shape
IPCC		Intergovernmental Panel on Climate Change
JMA		Japan Meteorological Agency
LBLRTM		Line-by-line radiative transfer model
LOS		Line of sight
LUE		Light use efficiency
MAP		Maximum a posteriori
MLI		Multilayer insulation
MOBY		Marine optical BuoY
MODIS		MODerate resolution Imaging Spectroradiometer
MOPITT		Measurements of Pollution In The Troposphere
MWPM		Multi-wavelength and multi-pixel method
NASA		National Aeronautics and Space Administration
NDACC​		Network for the Detection of Atmospheric Composition Change
NDVI		Normalized difference vegetation index
NICAM		Nonhydrostatic ICosahedral Atmospheric Model
NICAM-TM		NICAM-based Transport Model
NISMON		NICAM-based Inverse Simulation for Monitoring
NOAA		National Oceanic and Atmospheric Administration
OCO		Orbiting Carbon Observatory
OECD		Organization for Economic Co-operation andDevelopment
OMI		Ozone monitoring instrument
OMPS		Ozone Mapping and Profiler Suite
OSA		Optical spectrum analyzer
PAR		Photosynthetically active radiation
PFT		Pre-flight test
RGB		Red green and blue
RRV		RailRoad valley
RTEs		Radiative transfer equations
SCIAMACHY	SCanning Imaging Absorption spectroMeter for
Atmospheric CHartographY
SCOPE		Sustained and COordinated Processing of Environmental satellite data
SIF		Solar-induced fluorescence
SLCF		Short-lived climate forcer
SNR		Signal-to-noise ratio
SSA		Single scattering albedo
SVM		Support vector machine
SWIR		Short wavelength InfraRed
TanSat		Chinese Carbon Dioxide Observation SATellite Mission
TANSO-CAI		Thermal and Near Infrared Sensor for carbon
Observation-Cloud and Aerosol Imager
TANSO-FTS		Thermal and Near Infrared Sensor for carbon
Observation-Fourier Transform Spectrometer
TCCON		The Total Carbon Column Observing Network
TEMPO		Tropospheric Emissions: Monitoring Pollution
TIR		Thermal InfraRed
TOA		Top of the atmosphere
TROPOMI		TROPOspheric Monitoring Instrument
UNFCCC​		United Nations Framework Convention on Climate
ChangeUV		UltraViolet

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

VIIRS		Visible Infrared Imaging Radiometer Suite
VNIR		Visible and near InfraRed
WDCGG​		World Data Center for Greenhouse Gases
WMO		World Health Organization
Acknowledgements
This research has been supported by the Ministry of Environment, Japan.
Author contributions
RI, TM, and MN proposed the topic and conceived the study. YY developed retrieval systems. KS carried out calibration of the sensors. IM and UO
conducted validation study. NS and YS developed retrieval algorithms for TIR
data. YN, SM, and HT developed the inverse analysis system. YO, HI, and TYN
developed the cloud screening system. MH, TN, and CS developed the aerosol
retrieval system and analyzed aerosol data. HN and KH analyzed SIF data. All
authors read and approved the final manuscript.
Availability of data and materials
All standard products of GOSAT and GOSAT-2 project can be downloaded
from the GOSAT Data Archive Service (GDAS) [https://​data2.​gosat.​nies.​go.​jp/]and the GOSAT-2 Product Archive [https://​prdct.​gosat-2.​nies.​go.​jp/​index.​html.​
ja].

Declarations
Competing interests
The authors declare that they have no competing interest.
Author details
1
Atmosphere and Ocean Research Institute, The University of Tokyo, 5‑1‑5Kashiwanoha, Kashiwa, Chiba 277‑8568, Japan. 2 National Institute for Environmental Studies, 16‑2 Onogawa, Tsukuba, Ibaraki 305‑8506, Japan. 3 Japan Aerospace Exploration Agency, 2‑1‑1 Sengen, Tsukuba, Ibaraki 305‑8505, Japan.
4Center for Environmental Remote Sensing (CEReS), Chiba University, 1‑33
Yayoi‑Cho, Inage‑Ku, Chiba‑Shi, Chiba 263‑8522, Japan. 5 Tohoku University,
Aoba, Sendai, Miyagi 980‑8578, Japan. 6 Research Center for Agricultural Information Technology, The National Agriculture and Food Research Organization,
1‑31‑1 Kannondai, Tsukuba, Ibaraki 305‑0856, Japan. 7 Meteorological ResearchInstitute, Japan Meteorological Agency, 1‑1 Nagamine, Tsukuba, Ibaraki
305‑0052, Japan. 8 Tokai University, 2‑28‑4 Tomigaya, Shibuya, Tokyo 151‑8677,
Japan. 9 Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China.
Received: 11 November 2022 Accepted: 6 June 2023
Published: 3 July 2023

References
Baker NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo.
Annual Rev Plant Biol 59:89–113
Beer R, Shephard MW, Kulawik SS, Clough SA, Eldering A, Bowman KW,
Sander SP, Fisher BM, Payne VH, Luo M (2008) First satellite observations of lower tropospheric ammonia and methanol. Geophys Res Lett
35:L09801. https://​doi.​org/​10.​1029/​2008G​L0336​42
Biraud SC, Torn MS, Smith JR, Sweeney C, Riley WJ, Tans PP (2013) A multi-yearrecord of airborne ­CO2 observations in the US Southern Great Plains.
Atmos Meas Tech 6:751–763. https://​doi.​org/​10.​5194/​amt-6-​751-​2013
Boesch H, Deutscher NM, Warneke T, Byckling K, Cogan AJ, Griffith DWT,
Notholt J, Parker RJ, Wang Z (2013) HDO/H2O ratio retrievals fromGOSAT. Atmos Meas Tech 6:599–612. https://​doi.​org/​10.​5194/​
amt-6-​599-​2013
Clerbaux C, Boynard A, Clarisse L, George M, Hadji-Lazaro J, Herbin H, Hurtmans D, Pommier M, Razavi A, Turquety S (2009) Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder.
Atmos Chem Phys 9:6041–6054
Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K,
Boukabara S, Brown PD (2005) Atmospheric radiative transfer modeling:a summary of the AER codes. J Quant Spectr Rad Trans 91:233–244

Page 31 of 35

Coheur PF, Clarisse L, Turquety S, Hurtmans D, Clerbaux C (2009) IASI measurements of reactive trace species in biomass burning plumes. Atmos
Chem Phys 9:5655–5667
Crisp D, Pollock HR, Rosenberg R, Chapsky L, Lee RAM, Oyafuso FA, Frankenberg C, O’Dell CW, Bruegge CJ, Doran GB, Eldering A, Fisher BM, Fu D,
Gunson MR, Mandrake L, Osterman GB, Schwandner FM, Sun K, Taylor
TE, Wennberg PO, Wunch D (2017) The on-orbit performance of the
Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products. Atmos Meas Tech 10:59–81. https://​doi.​org/​
10.​5194/​amt-​10-​59-​2017
Daughtry CST, Walthall CL, Kim MS, Brown de Colstoun E, McMurtrey JE (2000)
Estimating corn leaf chlorophyll concentration from leaf and canopy
reflectance. Remote Sens Env 74:229–239
De Mazière M, Thompson AM, Kurylo MJ, Wild JD, Bernhard G, Blumenstock
T, Braathen GO, Hannigan JW, Lambert J-C, Leblanc T, McGee TJ,
Nedoluha G, Petropavlovskikh I, Seckmeyer G, Simon PC, Steinbrecht
W, Strahan SE (2018) The Network for the Detection of AtmosphericComposition Change (NDACC): history, status and perspectives. Atmos
Chem Phys 18:4935–4964. https://​doi.​org/​10.​5194/​acp-​18-​4935-​2018
Du S, Liu L, Liu X, Zhang X, Zhang X, Bi Y, Zhang, (2018) Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Sci
Bul 63:1502–1512. https://​doi.​org/​10.​1016/j.​scib.​2018.​10.​003
Eldering A, Taylor TE, O’Dell CW, Pavlick P (2019) The OCO-3 mission: measurement objectives and expected performance based on 1 year ofsimulated data. Atmos Meas Tech 12:2341–2370. https://​doi.​org/​10.​
5194/​amt-​12-​2341-​2019
Filges A, Gerbig C, Chen H, Franke H, Klaus C, Jordan A (2015) The IAGOS-coregreenhouse gas package: a measurement system for continuous airborne observations of C
­ O2, ­CH4, ­H2O and CO. Tellus B 67:27989. https://​doi.​org/​10.​3402/​tellu​sb.​v67.​27989
Frankenberg C, Aben I, Bergamaschi P, Dlugokencky EJ, van Hees R, Houweling S, van der Meer P, Snel R, Tol P (2011a) Global column-averaged
methane mixing ratios from 2003 to 2009 as derived from SCIAMACHY:
Trends and variability. J Geophys Res 116:D04302. https://​doi.​org/​10.​1029/​2010J​D0148​49
Frankenberg C, Butz A, Toon GC (2011b) Disentangling chlorophyll fluorescence from atmospheric scattering effects in ­O2 A-band spectra ofreflected sun-light. Geophys Res Let 38:1–5
Frankenberg C, Fisher JB, Worden J, Badgley G, Saatchi SS, Lee JE, Toon GC,
Butz A, Jung M, Kuze A, Yokota T (2011c) New global observations of
the terrestrial carbon cycle from GOSAT: patterns of plant fluorescencewith gross primary productivity. Geophys Res Lett 38:L17706. https://​doi.​org/​10.​1029/​2011G​L0487​38Frankenberg C, O’Dell C, Guanter L, McDuffie J (2012) Remote sensing of nearinfrared chlorophyll fluorescence from space in scattering atmospheres:
implications for its retrieval and interferences with atmospheric ­CO2
retrievals. Atmos Meas Tech 5:2081–2094
Frey M, Sha MK, Hase F, Kiel M, Blumenstock T, Harig R, Surawicz G, Deutscher
NM, Shiomi K, Franklin JE, Bösch H, Chen J, Grutter M, Ohyama H, Sun
Y, Butz A, Mengistu Tsidu G, Ene D, Wunch D, Cao Z, Garcia O, Ramonet
M, Vogel F, Orphal J (2019) Building the COllaborative Carbon Column
Observing Network (COCCON): long-term stability and ensemble
performance of the EM27/SUN Fourier transform spectrometer. Atmos
Meas Tech 12:1513–1530. https://​doi.​org/​10.​5194/​amt-​12-​1513-​2019
Fujii Y (2005) Preconditioned optimizing utility for large- dimensional
analyses (POpULar). J Oceano 61:167–181. https://​doi.​org/​10.​1007/​
s10872-​005-​0029-z
Fujinawa T, Kuze A, Suto H, Shiomi K, Kanaya Y, Kawashima T, Kataoka F, MoriS, Eskes H, Tanimoto H (2021) First concurrent observations of ­NO2 and­CO2 from power plant plumes by airborne remote sensing. Geophys
Res Lett. https://​doi.​org/​10.​1029/​2021G​L0926​85
Fukuda S, Nakajima T, Takenaka H, Higurashi A, Kikuchi N, Nakajima TY, Ishida
H (2013) New approaches to removing cloud shadows and evaluating
the 380 nm surface reflectance for improved aerosol optical thickness
retrievals from the GOSAT/TANSO-Cloud and Aerosol Imager. J Geophys
Res 118:13–520. https://​doi.​org/​10.​1002/​2013J​D0200​90
Garzonio R, Di Mauro B, Colombo R, Cogliati S (2017) Surface reflectance andsun-induced fluorescence spectroscopy measurements using a small
hyperspectral UAS. Remote Sens 9:472. https://​doi.​org/​10.​3390/​rs905​0472

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Global Carbon Project (2017) Supplemental data of Global Carbon Budget
2017 (Ver 1.0). Global Carbon Project. https://​doi.​org/​10.​18160/​
gcp-​2017
Gogoi MM, Babu SS, Imasu R, Hashimoto M (2022) Satellite (GOSAT-2 CAI-2)
retrieval and surface (ARFINET) observations of Aerosol Black Carbon
over India. Atmos Chem Phys Discuss. https://​doi.​org/​10.​5194/​
acp-​2022-​555
Gogoi MM., Babu SS, Imasu R, Hashimoto H (2021) Aerosol black carbon overIndia from satellite (GOSAT-2 CAI-2) and ground-based (ARFINET)
measurements. In: Abstract of the International SKYNET Workshop,
Chiba, 9–12 Nov 2021
Goldberg DL, Lu Z, Oda T, Lamsal LN, Liu F, Griffin D, McLinden CA, Krotkov NA,
Duncan BN, Streets DG (2019) Exploiting OMI N­ O2 satellite observations
to infer fossil-fuel ­CO2 emissions from US megacities. Sci Total Environ.
https://​doi.​org/​10.​1016/j.​scito​tenv.​2019.​133805
Guanter L, Frankenberg C, Dudhia A, Lewis PE, Gomez-Dans J, Kuze A, Suto
H, Grainger RG (2012) Retrieval and global assessment of terrestrial
chlorophyll fluorescence from GOSAT space measurements. Remote
Sens Environ 121:236–251
Guanter L, Zhang Y, Jung M, Joiner J, Voigt M, Berry JA, Frankenberg C, Huete
AR, Zarco-Tejada P, Lee JE, Moran MS, Ponce-Campos G, Beer C, CampsValls G, Buchmann N, Gianelle D, Klumpp K, Cescatti A, Baker JM, Griffis
TJ (2014) Global and time-resolved monitoring of crop photosynthesis
with chlorophyll fluorescence. Nati Acad Sci USA 111:E1327-1333.
https://​doi.​org/​10.​1073/​pnas.​13200​08111
Guanter L, Aben I, Tol P, Krijger JM, Hollstein A, Köhler P, Damm A, Joiner
J, Frankenberg C, Landgraf J (2015) Potential of the TROPOspheric
Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor
for the monitoring of terrestrial chlorophyll fluorescence. Atmos Meas
Tech 8:1337–1352
Guerlet S, Butz A, Schepers D, Basu S, Hasekamp OP, Kuze A, Yokota T, Blavier
J-F, Deutscher NM, Griffith DW, Hase F, Kyro E, Morino I, Sherlock V,
Sussmann R, Galli A, Aben I (2013) Impact of aerosol and thin cirrus on
retrieving and validating X­ CO2 from GOSAT shortwave infrared measurements. J Geophys Res 118:4887–4905
Hamazaki T, Kaneko Y, Kuze A, Kondo K (2005) Fourier transform spectrometer
for Greenhouse Gases Observing Satellite (GOSAT). Proc SPIE 5659:73
Hashimoto M, Nakajima T (2017) Development of a remote sensing algorithm
to retrieve atmospheric aerosol properties using multi-wavelength andmulti-pixel information. J Geophys Res. https://​doi.​org/​10.​1002/​2016J​
D0256​98
Hikosaka K, Tsujimoto K (2021) Linking remote sensing parameters to C
­ O2
assimilation rates at a leaf scale. J Plant Res 134:695–711. https://​doi.​
org/​10.​1007/​s10265-​021-​01313-4
Imasu R (2019) Expected scientific achievement by GOSAT-2 Science plan. J
Remo Sens Soc Jpn 39:9–13. https://​doi.​org/​10.​11440/​rssj.​39.9
Inoue T (1985) On the temperature and effective emissivity determination ofsemi-transparent cirrus clouds by bi-spectral measurements in the 10
mm window region. J Meteor Soc Jpn 63:88–99
Inoue M, Morino I, Uchino O, Miyamoto Y, Yoshida Y, Yokota T, Machida T, Sawa
Y, Matsueda H, Sweeney C, Tans PP, Andrews AE, Biraud SC, Tanaka T,
Kawakami S, Patra PK (2013) Validation of ­XCO2 derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data. Atmos Chem
Phys 13:9771–9788. https://​doi.​org/​10.​5194/​acp-​13-​9771-​2013
Inoue M, Morino I, Uchino O, Nakatsuru T, Yoshida Y, Yokota T, Wunch D, Wennberg PO, Roehl CM, Griffith DWT, Velazco VA, Deutscher NM, Warneke
T, Notholt J, Robinson J, Sherlock V, Hase F, Blumenstock T, Rettinger M,
Sussmann R, Kyrö E, Kivi R, Shiomi K, Kawakami S, De Mazière M, Arnold
SG, Feist DG, Barrow EA, Barney J, Dubey M, Schneider M, Iraci LT, Podolske JR, Hillyard PW, Machida T, Sawa Y, Tsuboi K, Matsueda H, Sweeney C,
Tans PP, Andrews AE, Biraud SC, Fukuyama Y, Pittman JV, Kort EA, Tanaka
T (2016) Bias corrections of GOSAT SWIR ­XCO2 and ­XCH4 with TCCON
data and their evaluation using aircraft measurement data. Atmos Meas
Tech 9:3491–3512. https://​doi.​org/​10.​5194/​amt-9-​3491-​2016
Intergovernmental Panel on Climate Change (2018) Global Warming of 1.5°C.
https://​www.​ipcc.​ch/​sr15/. Accessed 25 Sept 2022
Intergovernmental Panel on Climate Change (2021) Climate Change 2021: The
Physical Science Basis. Cambridge University Press, UK
Ishida H, Nakajima TY (2009) Development of an unbiased cloud detection algorithm for a spaceborne multispectral imager. J Geophys Res
114:D07206

Page 32 of 35

Ishidaa H, Oishi Y, Morita K, Moriwaki K, Nakajima TY (2018) Development of
a support vector machine based cloud detection method for MODIS
with the adjustability to various conditions. Remote Sens Environ
205:390–407. https://​doi.​org/​10.​1016/j.​rse.​2017.​11.​003
Ishijima K, Patra PK, Takigawa M, Machida T, Matsueda H, Sawa Y, Steele LP,
Krummel PB, Langenfelds RL, Aoki S, Nakazawa T (2010) Stratosphericinfluence on the seasonal cycle of nitrous oxide in the troposphere as
deduced from aircraft observations and model simulations. J Geophys
Res 115:D20308. https://​doi.​org/​10.​1029/​2009J​D0133​22
Itahashi S, Yumimoto K, Kurokawa J, Morino Y, Nagashima T, Miyazaki K, Maki
T, Ohara T (2019) Inverse estimation of NOx emissions over China andIndia 2005–2016: contrasting recent trends and future perspectives.
Environ Res Lett 14:124020. https://​doi.​org/​10.​1088/​1748-​9326/​ab4d7f
Japan Aerospace Exploration Agency (2020) Large scale Australian forest fire
observed from space, Earthview. https://​www.​eorc.​jaxa.​jp/​earth​view/​
2020/​tp200​131.​html. Accessed 25 Sept 2022
Jethva H, Torres O (2019) A comparative evaluation of Aura-OMI and SKYNET
near-UV single-scattering albedo products. Atmos Meas Tech 12:6489–
6503. https://​doi.​org/​10.​5194/​amt-​12-​6489-​2019
Joiner J, Yoshida Y, Vasilkov AP, Yoshida Y, Corp LA, Middleton EM (2011) First
observations of global and seasonal terrestrial chlorophyll fluorescencefrom space. Biogeoscience 8:637–651
Joiner J, Yoshida Y, Vasilkov AP, Middleton EM, Campbell PKE, Yoshida Y, Kuze
A, Corp LA (2012) Filling-in of near-infrared solar lines by terrestrial
fluorescence and other geophysical effects: simulations and spacebased observations from SCIAMACHY and GOSAT. Atmos Meas Tech
5:809–829
Joiner J, Guanter L, Lindstrot R, Voigt M, Vasilkov AP, Middleton EM, Huemmrich KF, Yoshida Y, Frankenberg C (2013) Global monitoring ofterrestrial chlorophyll fluorescence from moderate-spectral-resolution
near-infrared satellite measurements: methodology, simulations, andapplication to GOME-2. Atmos Meas Tech 6:2803–2823. https://​doi.​org/​
10.​5194/​amt-6-​2803-​2013
Julitta T, Corp LA, Rossini M, Burkart A, Cogliati S, Davies N, Hom M, Mac Arthur
A, Middleton EM, Rascher U, Schickling A, Colombo R (2016) Comparison of Sun-induced chlorophyll fluorescence estimates obtained fromfour portable field spectroradiometers. Remote Sens 8:122. https://​doi.​
org/​10.​3390/​rs802​0122
Karion A, Sweeney C, Tans P, Newberger T (2010) AirCore: an innovative atmospheric sampling system. J Atmos Ocean Tech 27:1839–1853. https://​doi.​org/​10.​1175/​2010J​TECHA​1448.1
Kaufman YJ (1987) Satellite sensing of aerosol absorption. J Geophys Res
92:4307–4317. https://​doi.​org/​10.​1029/​JD092​iD04p​04307
Kawasaki M, Yoshioka H, Jones NB, Macatangay R, Griffith DWT, Kawakami S,
Ohyama H, Tanaka T, Morino I, Uchino O, Ibuki T (2012) Usability of optical spectrum analyzer in measuring atmospheric ­CO2 and ­CH4 column
densities: inspection with FTS and aircraft profiles in situ. Atmos Meas
Tech 5:2593–2600. https://​doi.​org/​10.​5194/​amt-5-​2593-​2012
Kim J, Jeong U, Ahn MH, Kim JH, Park RJ, Lee H, Song CH, Choi YS, Lee KH, Yoo
JM, Jeong MJ, Park SK, Lee KM, Song CK, Kim SW, Kim YJ, Kim SW, Kim
M, Go S, Liu X, Chance K, Miller CC, Al-Saadi J, Veihelmann B, Bhartia PK,
Torres O, Abad GG, Haffner DP, Ko DH, Lee SH et al (2020) New era of air
quality monitoring from space: geostationary environment monitoring
spectrometer (GEMS). Bull Amer Meteo Soc 101:E1–E22. https://​doi.​org/​
10.​1175/​BAMS-D-​18-​0013.1
Kobayashi N, Inoue G, Kawasaki M, Yoshioka H, Minomura M, Murata I, Nagahama T, Matsumi Y, Tanaka T, Morino I, Ibuki T (2010) Remotely operable
compact instruments for measuring atmospheric ­CO2 and ­CH4 column
densities at surface monitoring sites. Atmos Meas Tech 3:1103–1112.
https://​doi.​org/​10.​5194/​amt-3-​1103-​2010
Köehler P, Frankenberg C, Magney TS, Guanter L, Joiner J, Landgraf J (2018)
Global retrievals of solar induced chlorophyll fluorescence with TROPOMI: first results and inter-sensor comparison to OCO-2. Geophys Res
Let 45:10456–10463. https://​doi.​org/​10.​1029/​2018G​L0790​31
Kuze A, Suto H, Nakajima M, Hamazaki T (2009) Thermal and near infrared
sensor for carbon observation Fourier-transform spectrometer on theGreenhouse Gases Observing Satellite for greenhouse gases monitoring. Appl Opt 48:6716–6733. https://​doi.​org/​10.​1364/​AO.​48.​006716
Lee JE, Frankenberg C, van der Tol C, Berry JA, Guanter L, Boyce CK, Fisher
JB, Morrow E, Worden JR, Asefi S, Badgley G, Saatchi S (2013) Forest
productivity and water stress in Amazonia: observations from GOSAT

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

chlorophyll fluorescence. Royal Soci b: Bio Sci 280:20130171–20130171.
https://​doi.​org/​10.​1098/​rspb.​2013.​0171
Liu J, Bowman KW, Schimel DS, Parazoo NC, Jiang Z, Lee M, Bloom AA, Wunch
D, Frankenberg C, Sun Y, O’Dell CW, Gurney KR, Menemenlis D, Gierach
M, Crisp D, Eldering A (2017) Contrasting carbon cycle responses of thetropical continents to the 2015–2016 El Niño. Science 358:aam5690
Machida T, Matsueda H, Sawa Y, Nakagawa Y, Hirotani K, Kondo N, Goto K,
Nakazawa T, Ishikawa K, Ogawa T (2008) Worldwide measurements ofatmospheric ­CO2 and other trace gas species using commercial airlines.
J Atmos Ocean Tech 25:1744–1754. https://​doi.​org/​10.​1175/​2008J​TECHA​1082.1
Meroni M, Rossinia M, Guanter L, Alonsoc L, Rascher U, Colombo R, Moreno
J (2009) Remote sensing of solar-induced chlorophyll fluorescence:
review of methods and applications. Remo Sens Env 113:2037–2051.
https://​doi.​org/​10.​1016/j.​rse.​2009.​05.​003
Migliavacca M, Perez-Priego O, Rossini M, El-Madany TS, Moreno G, van der Tol
C, Rascher U, Berninger A, Bessenbacher V, Burkart A, Carrara A, Fava
F, Guan JH, Hammer TW, Henkel K, Juarez-Alcalde E, Julitta T, Kolle O,
Martin MP, Musavi T, Pacheco-Labrador J, Perez-Burgueno A, Wutzler T,
Zaehle S, Reichstein M (2017) Plant functional traits and canopy structure control the relationship between photosynthetic C­ O2 uptake andfar-red sun-induced fluorescence in a Mediterranean grassland under
different nutrient availability. New Phytol 214:1078–1091. https://​doi.​
org/​10.​1111/​nph.​14437
Monteith JL (1972) Solar radiation and productivity in tropical ecosystems. J
App Ecology 9:747–766
Nakajima T (2021) Development of earth observation satellites for the atmospheric sciences. J Remote Sens Soc Jpn 41:109–118
Nakajima T, Tanaka M (1986) Matrix formulation for the transfer of solar radiation in a plane-parallel scattering atmosphere. J Quant Spect Rad Trans35:13–21
Nakajima T, Tanaka M (1988) Algorithms for radiative intensity calculations
in moderately thick atmospheres using a truncation approximation. J
Quant Spect Rad Trans 40:51–69
Nakajima T, Ohara T, Masui T, Takemura T, Yoshimura K, Goto D, Hanaoka T,
Itahashi S, Kurata G, Kurokawa J, Maki T, Masutomi Y, Nakata M, Nitta T,
Seposo X, Sudo K, Suzuki C, Suzuki K, Tsuruta H, Ueda K, Watanabe S, Yu
Y, Yumimoto K, Zhao S (2020a) A development of reduction scenariosof the short-lived climate pollutants (SLCPs) for mitigating global warming and environmental problems. Prog Earth Planet Sci 7:33. https://​doi.​
org/​10.​1186/​s40645-​020-​00351-1
Nakajima T, Campanelli M, Che H, Estellés V, Irie H, Kim S-W, Kim J, Liu D,
Nishizawa T, Pandithurai G, Soni VK, Thana B, Tugjsurn N-U, Aoki K, Go
S, Hashimoto M, Higurashi A, Kazadzis S, Khatri P, Kouremeti N, Kudo R,
Marenco F, Momoi M, Ningombam SS, Ryder CL, Uchiyama A, Yamazaki
A (2020b) An overview of and issues with sky radiometer technology
and SKYNET. Atmos Meas Tech 13:4195–4218. https://​doi.​org/​10.​5194/​
amt-​13-​4195-​2020
Nguyen H, Osterman G, Wunch D, O’Dell C, Mandrake L, Wennberg P,
Fisher B, Castano R (2014) A method for collocating satellite ­XCO2
data to ground-based data and its application to ACOS-GOSAT and
TCCON. Atmos Meas Tech 7:2631–2644. https://​doi.​org/​10.​5194/​
amt-7-​2631-​2014
Niwa Y, Fujii Y (2020) A conjugate BFGS method for accurate estimation of aposterior error covariance matrix in a linear inverse problem. Quart J
Royal Meteorol Soc 146:3118–3143. https://​doi.​org/​10.​1002/​qj.​3838
Niwa Y, Tomita H, Satoh M, Imasu R (2011) A three-dimensional icosahedral
grid advection scheme preserving monotonicity and consistency
with continuity for atmospheric tracer transport. J Meteor Soc Jpn
89:255–268
Niwa Y, Tomita H, Satoh M, Imasu R, Sawa Y, Tsuboi K, Matsueda H, Machida
T, Sasakawa M, Belan B, Saigusa N (2017a) A 4D-Var inversion system
based on the icosahedral grid model (NICAM-TM 4D-Var v1.0)—part
1: offline forward and adjoint transport models. Geosci Model Dev
10:1157–1174. https://​doi.​org/​10.​5194/​gmd-​10-​1157-​2017
Niwa Y, Fujii Y, Sawa Y, Iida Y, Ito A, Satoh M, Imasu R, Tsuboi K, Matsueda H,
Saigusa N (2017b) A 4D-Var inversion system based on the icosahedral
grid model (NICAM-TM 4D-Var v1.0)—part 2: optimization scheme andidentical twin experiment of atmospheric ­CO2 inversion. Geosci Model
Dev 10:2201–2219. https://​doi.​org/​10.​5194/​gmd-​10-​2201-​2017

Page 33 of 35

Ohyama H, Kawakami S, Shiomi K, Morino I, Uchino O (2013) Atmospherictemperature and water vapor retrievals from GOSAT thermal infrared
spectra and initial validation with coincident radiosonde measurements. SOLA 9:143–147
Oishi Y, Nagao T, Ishida H, Nakajima TY, Matsunaga T (2015) Preliminary
study of a method using the GOSAT-2 CAI-2 cloud discrimination forscreening of cloud-contaminated FTS-2 data. J Remote Sens Soc Jpn
35:299–306
Oishi Y, Nakajima TY, Matsunaga T (2016) Difference between forward- andbackward-looking bands of GOSAT-2 CAI-2 cloud discrimination usedwith Terra MISR data. Int J Remote Sens 37:1115–1126
Oishi Y, Ishida H, Nakamura R, Matsunaga T (2017) The impact of different support vectors on GOSAT-2 CAI-2 L2 Cloud discrimination. Remote Sens
9:1236. https://​doi.​org/​10.​3390/​rs912​1236
Oishi Y, Ishida H, Nakajima TY, Nakamura R, Matsunaga T (2018) Preliminary
verification for application of a support vector machine-based clouddetection method to GOSAT-2 CAI-2. Atmos Meas Tech 11:2863–2878.
https://​doi.​org/​10.​5194/​amt-​11-​2863-​2018
Organisation for Economic Co-operation and Development (2020) Environment at a glance 2020. OECD Publishing, Paris. https://​doi.​org/​10.​1787/​
4ea7d​35f-​en
Oshio H, Yoshida Y, Matsunaga T (2019) On the zero-level offset in the GOSATTANSO-FTS O2 A band and the quality of solar-induced chlorophyll fluorescence (SIF): comparison of SIF between GOSAT and OCO-2. Atmos
Meas Tech 12:6721–6735. https://​doi.​org/​10.​5194/​amt-​12-​6721-​2019
Ouchi M, Matsumi Y, Nakayama T, Shimizu K, Sawada T, Machida T, Matsueda
H, Sawa Y, Morino I, Uchino O, Tanaka T, Imasu R (2019) Development of
a balloon-borne instrument for ­CO2 vertical profile observations in the
troposphere. Atmos Meas Tech 12:5639–5653. https://​doi.​org/​10.​5194/​
amt-​12-​5639-​2019
Parazoo NC, Bowman K, Frankenberg C, Lee JE, Fisher JB, Worden J, Jones DBA,
Berry J, Collatz GJ, Baker IT, Jung M, Liu J, Osterman G, O’Dell C, Sparks
A, Butz A, Guerlet S, Yoshida Y, Chen H, Gerbig C (2013) Interpreting
seasonal changes in the carbon balance of southern Amazonia using
measurements of ­XCO2 and chlorophyll fluorescence from GOSAT.
Geophys Res Let 40:2829–2833
Parazoo NC, Bowman K, Fisher JB, Frankenberg C, Jones DBA, Cescatti A, PérezPriego O, Wohlfahrt G, Montagnani L (2014) Terrestrial gross primary
production inferred from satellite fluorescence and vegetation models.
Global Change Biol 20:3103–3121
Parker R, Boesch H, Cogan A, Fraser A, Feng L, Palmer PI, Messerschmidt J,
Deutscher N, Griffith DWT, Notholt J, Wennberg PO, Wunch D (2011)
Methane observations from the Greenhouse Gases Observing SATellite:
Comparison to ground-based TCCON data and model calculations.
Geophys Res Lett 38:L15807. https://​doi.​org/​10.​1029/​2011G​L0478​71
Porcar-Castell A, Tyystjärvi E, Atherton J, van der Tol C, Flexas J, Pfündel EE,
Moreno J, Frankenberg C, Berry JA (2014) Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms
and challenges. J Exp Bot 65:4065–4095
Rodgers CD (2000) Inverse methods for atmospheric sounding: theory andpractice. World Science, Singapore
Rothman LS, Jacquemart D, Barbe A, Benner DC, Birk M, Brown L, Carleer M,
Chackerian C, Chance KLH, Dana V, Devi VM, Flaud JM, Gamache RR,
Goldman A, Hartmann JM, Jucks KW, Maki AG, Mandin JY, Massie ST,
Orphal J, Perrin A, Rins-land CP, Smith MAH, Tennyson J, Tolchenov RN,
Toth RA, Vander Auwera J, Varanasi P, Wagner G (2005) The HITRAN 2004
molecular spectroscopic database. J Quant Spec Rad Trans 96:139–204
Rothman LS, Gordon IE, Barbe A, Benner DC, Bernath PF, Birk M, Boudon V,
Brown LR, Campargue A, Champion JP, Chance K, Coudert LH, Dana
V, Devi VM, Fally S, Flaud J-M, Gamache RR, Goldman A, Jacquemart
D, Kleiner I, Lacome N, Lafferty WJ, Mandin J-Y, Massie ST, Mikhailenko
SN, Miller CE, Moazzen-Ahmadi N, Naumenko OV, Nikitin AV, Orphal J,
Perevalov VI, Perrin A, Predoi-Cross A, Rinsland CP, Rotger M, Simeckova
M, Smith MAH, Sung K, Tashkun SA, Tennyson J, Toth RA, Vandaele AC,
Vander Auwera J (2009) The HITRAN 2008 molecular spectroscopic
database. J Quant Spect Rad Trans 110:533–572
Rothman L, Gordon I, Babikov Y, Barbe A, Benner DC, Bernath P, Birk M, Bizzocchi L, Boudon V, Brown LR, Campargue A, Chance K, Coudert LH, Devi
VM, Drouin BJ, Fayt A, Flaud J-M, Gamache RR, Harrison JJ, Hartmann
J-M, Hill C, Hodges JT, Jacquemart D, Jolly A, Lamouroux J, Le Roy RJ,

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

Li G, Long DA, Lyulin ST, Mackie OM, Massie ST, Mikhailenko S, Muller
HSP, Naumenko OV, Nikitin AV, Orphal J, Perevalov V, Perrin A, Polotseva
ER, Richard C, Smith MAH, Starikova E, Sung K, Tashkun SA, Tennyson J,
Toon GC, VlG T, Wagner G (2013) The HITRAN2012 molecular spectroscopic database. J Quant Spect Rad Trans 130:4–50
Running SW, Nemani R, Heinsch FA, ZhaoM RM, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. Biosci 54:547–560
Saito M, Niwa Y, Saeki T, Cong R, Miyauchi T (2019) Overview of model systems
for global carbon dioxide and methane flux estimates using GOSAT and
GOSAT-2 observations. J Remo Sens Soc Japan 39:50–56
Saitoh N, Imasu R, Ota Y, Niwa Y (2009) ­CO2 retrieval algorithm for the thermal
infrared spectra of the greenhouse gases observing satellite potentialof retrieving ­CO2 vertical profile from high-resolution FTS sensor. J
Geophys Res. https://​doi.​org/​10.​1029/​2007J​D0115​00
Saitoh N, Kimoto S, Sugimura R, Imasu R, Kawakami S, Shiomi K, Kuze K,
Machia T, Sawa Y, Matsueda H (2016) Algorithm update of the GOSAT/TANSO−FTS thermal infrared C­ O2 product (version 1) and validation of
the UTLS ­CO2 data using CONTRAIL measurements. Atmos Meas Tech
9:2119–2134
Sakaizawa D, Nagasawa C, Nagai T, Abo M, Shibata Y, Nakazato M, Sakai T(2009) Development of a 1.6 μm differential absorption lidar with aquasi-phase matching OPO and photon-counting detector for thevertical ­CO2 profile. Appl Opt 48:748–757. https://​doi.​org/​10.​1364/​AO.​
48.​000748
Satoh M, Tomita H, Yashiro H, Miura H, Kodama C, Seiki T, Noda AT, Yamada Y,
Goto D, Sawada M, Miyoshi T, Niwa Y, Hara M, Ohno T, Iga S, Arakawa
T, Inoue T, Kubokawa H (2014) The non-hydrostatic icosahedral atmospheric model: description and development. Prog Earth Planet Sci 1:18.
https://​doi.​org/​10.​1186/​s40645-​014-​0018-1
Schmid B, Tomlinson JM, Hubbe JM, Comstock JM, Mei F, Chand D, Pekour MS,
Kluzek CD, Andrews E, Biraud SC, McFarquhar GM (2014) The DOE ARM
aerial facility. Bull Am Meteor Soc 95:723–742. https://​doi.​org/​10.​1175/​
BAMS-D-​13-​00040.1
Sekiguchi M, Nakajima T (2008) A k-distribution-based radiation code and its
computational optimization for an atmospheric general circulation
model. J Quant Spect Rad Trans 109:2779–2793. https://​doi.​org/​10.​1016/j.​jqsrt.​2008.​07.​013
Shi C, Nakajima T (2018) Simultaneous determination of aerosol optical thickness and water-leaving radiance from multispectral measurements incoastal waters. Atmos Chem Phys 18:3865–3994
Shi C, Hashimoto M, Nakajima T (2019) Remote sensing of aerosol properties
from multi-wavelength and multi-pixel information over the ocean.
Atmos Chem Phys 19:2461–2475
Shi C, Hashimoto M, Shiomi K, Nakajima T (2020) Development of an algorithm
to retrieve aerosol optical properties over water using an artificial
neural network radiative transfer scheme: first result from GOSAT-2/
CAI-2. IEEE Trans Geosci Remote Sens. https://​doi.​org/​10.​1109/​TGRS.​
2020.​30388​92
Someya Y, Imasu R, Saitoh N, Ota Y, Shiomi K (2016) A development ofcloud top height retrieval using thermal infrared spectra observedwith GOSAT and comparison with CALIPSO data. Atmos Meas Tech
9:1981–1992. https://​doi.​org/​10.​5194/​amt-9-​1981-​2016
Someya Y, Imasu R, Shiomi K (2019) Dust aerosol detection by the modified­CO2 slicing method. Sensors 19:1615. https://​doi.​org/​10.​3390/​s1907​
1615
Someya Y, Imasu R, Shiomi K, Saitoh N (2020) Atmospheric ammonia retrieval
from the TANSO-FTS/GOSAT thermal infrared sounder. Atmos Meas
Tech 13:309–321. https://​doi.​org/​10.​5194/​amt-​13-​309-​2020
Stohl A, Aamaas B, Amann M, Baker LH, Bellouin N, Berntsen TK, Boucher O,
Cherian R, Collins W, Daskalakis N, Dusinska M, Eckhardt S, Fuglestvedt
JS, Harju M, Heyes C, Hodnebrog Ø, Hao J, Im U, Kanakidou M, Klimont
Z, Kupiainen K, Law KS, Lund MT, Maas R, MacIntosh CR, Myhre G,
Myriokefalitakis S, Olivié D, Quaas J, Quennehen B, Raut JC, Rumbold ST,
Samset BH, Schulz M, Seland Ø, Shine KP, Skeie RB, Wang S, Yttri KE, Zhu
T (2015) Evaluating the climate and air quality impacts of short-lived
pollutants. Atmos Chem Phys 15:10529–10566
Sun Y, Frankenberg C, Wood JD, Schimel DS, Jung M, Guanter L, Drewry DT,
Verma M, Porcar-Castell A, Griffis TJ, Gu L, Magney TS, Köhler P, Evans B,
Yuen K (2017) OCO-2 advances photosynthesis observation from space
via solar-induced chlorophyll fluorescence. Science 358:eaam5747

Page 34 of 35

Suto H, Kataoka F, Kikuchi N, Knuteson RO, Butz A, Haun M, Buijs H, Shiomi
K, Imai H, Kuze A (2021) Thermal and near-infrared sensor for carbon
observation Fourier transform spectrometer-2 (TANSO-FTS-2) on theGreenhouse gases Observing SATellite-2 (GOSAT-2) during its first year
in orbit. Atmos Meas Tech 14:2013–2039. https://​doi.​org/​10.​5194/​
amt-​14-​2013-​2021
Suto H, Kataoka F, Knuteson OR, Shiomi K, Kikuchi N, Kuze A (2022) Updated
spectral radiance calibration on TIR bands for TANSO-FTS-2 onboard
GOSAT-2. Atmos Meas Tech 15:5399–5413. https://​doi.​org/​10.​5194/​
amt-​15-​5399-​2022
Sweeney C, Karion A, Wolter S, Newberger T, Guenther D, Higgs JA, Andrews
AE, Lang PM, Neff D, Dlugokencky E, Miller JB, Montzka SA, Miller BR,
Masarie KA, Biraud SC, Novelli PC, Crotwell M, Crotwell AM, Thoning
K, Tans PP (2015) Seasonal climatology of ­CO2 across North America
from aircraft measurements in the NOAA/ESRL global greenhouse gas
reference network. J Geophys Res 120:5155–5190. https://​doi.​org/​10.​
1002/​2014J​D0225​91
Takagi H, Saeki T, Oda T, Saito M, Calsala V, Delikov D, Saito R, Yoshida Y, Morino
I, Uchino O, Andres RJ, Yokota T, Maksyutov S (2011) On the benefit of
GOSAT observations to the estimation of regional ­CO2 fluxes. SOLA
7:161–164. https://​doi.​org/​10.​2151/​sola.​2011-​041
Takekawa N, Kondo Y, Koike M, Chen G, Machida T, Watai T, Blake DR, Streets
DG, Woo JH, Carmichael GR, Kita K, Miyazaki Y, Shirai T, Liley JB, Ogawa
T (2004) Removal of NOx and NOy in Asian outflow plume: Aircraft
measurements over the western Pacific in January 2002. J Geophys Res
109:D23S04. https://​doi.​org/​10.​1029/​2004J​D0048​66
Takemura T, Suzuki K (2019) Weak global warming mitigation by reducing
black carbon emissions. Sci Rep 9:4419. https://​doi.​org/​10.​1038/​
s41598-​019-​41181-6
Takenaka H, Nakajima TY, Higurashi A, Higuchi A, Takamura T, Pinker RT,
Nakajima T (2011) Estimation of solar radiation using a neural network
based on radiative transfer. J Geophys Res 116:D08215. https://​doi.​org/​
10.​1029/​2009J​D0133​37
Thum T, Zaehle S, Köhler P, Aalto T, Aurela M, Guanter L, Kolari P, Laurila T, Lohila
A, Magnani F, Van der Tol C, Markkanen T (2017) Modelling sun-induced
fluorescence and photosynthesis with a land surface model at local
and regional scales in northern Europe. Biogeosci 14:1969–1984
Torres O, Jethva H, Ahn C, Jaross G, Loyola DG (2020) TROPOMI aerosol
products: evaluation and observations of synoptic-scale carbonaceous
aerosol plumes during 2018–2020. Atmos Meas Tech 13:6789–6806.
https://​doi.​org/​10.​5194/​amt-​13-​6789-​2020
Uchino O, Sakai T, Izumi T, Nagai T, Morino I, Yamazaki A, Deushi M, Yumimoto
K, Maki T, Tanaka TY, Akaho T, Okumura H, Arai K, Nakatsuru T, Matsunaga T, Yokota T (2017) Lidar detection of high concentrations of ozone
aerosol transported from northeastern Asia over Saga, Japan. Atmos
Chem Phys 17:1865–1879. https://​doi.​org/​10.​5194/​acp-​17-​1865-​2017
Van der Tol C, Verhoef W, Timmermans J, Verhoef A, Su Z (2009) An integrated
model of soil-canopy spectral radiances, photosynthesis, fluorescence,
temperature energy balance. Biogeosci 6:3109–3129
Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait
method. Auto Remo Cont 24:774–780
Walther S, Voigt M, Thum T, Gonsamo A, Zhang Y, Köhler JM, Varlagin A,
Guanter L (2016) Satellite chlorophyll fluorescence measurements
reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Glob Change Bio 22:2979–2996
White JD, Running SW, Nemani R, Keane RE, Ryan KC (1997) Measurement andremote sensing of LAI in Rocky Mountain montane ecosystems. Can J
For Res 27:1714–1727
Wilson EL, McLinden ML, Miller JH, Allen GR, Ott LE, Melroy HR, Clarke GB(2014) Miniaturized laser heterodyne radiometer for measurements of­CO2 in the atmospheric column. Appl Phys B Lasers Opt 114:385–393.
https://​doi.​org/​10.​1007/​s00340-​013-​5531-1
World Data Center for Greenhouse Gases (2022) https://​gaw.​kishou.​go.​jp/.
Accessed 29 Sept 2022
World Health Organization (2016) Ambient air pollution-A global assessmentof exposure and burden of disease. World Health Organization, Geneva
Wunch D, Toon GC, Wennberg PO, Wofsy SC, Stephens BB, Fischer ML, UchinoO, Abshire JB, Bernath P, Biraud SC, Blavier J-FL, Boone C, Bowman
KP, Browell EV, Campos T, Connor BJ, Daube BC, Deutscher NM, Diao
M, Elkins JW, Gerbig C, Gottlieb E, Griffith DWT, Hurst DF, Jiménez R,
Keppel-Aleks G, Kort EA, Macatangay R, Machida T, Matsueda H, Moore

Imasu et al. Progress in Earth and Planetary Science 2023, 10(1):33

F, Morino I, Park S, Robinson J, Roehl CM, Sawa Y, Sherlock V, Sweeney
C, Tanaka T, Zondlo MA (2010) Calibration of the total carbon column
observing network using aircraft profile data. Atmos Meas Tech
3:1351–1362. https://​doi.​org/​10.​5194/​amt-3-​1351-​2010
Wunch D, Toon GC, Blavier J-FL, Washenfelder RA, Notholt J, Connor BJ, Griffith DWT, Sherlock V, Wennberg PO (2011a) The total carbon column
observing network. Philos Trans R Soc A 369:2087–2112. https://​doi.​
org/​10.​1098/​rsta.​2010.​0240
Wunch D, Wennberg PO, Toon GC, Connor BJ, Fisher B, Osterman GB, Frankenberg C, Mandrake L, O’Dell C, Ahonen P, Biraud SC, Castano R, Cressie
N, Crisp D, Deutscher NM, Eldering A, Fisher ML, Griffith DWT, Gunson
M, Heikkinen P, Keppel-Aleks G, Kyrö E, Lindenmaier R, Macatangay R,
Mendonca J, Messerschmidt J, Miller CE, Morino I, Notholt J, Oyafuso
FA, Rettinger M, Robinson J, Roehl CM, Salawitch RJ, Sherlock V, Strong
K, Sussmann R, Tanaka T, Thompson DR, Uchino O, Warneke T, Wofsy SC(2011) A method for evaluating bias in global measurements of C
­ O2
total columns from space. Atmos Chem Phys 11:12317–12337. https://​doi.​org/​10.​5194/​acp-​11-​12317-​2011b
Xiong X, Barnet C, Maddy E, Sweeney C, Liu X, Zhou L, Goldberg M (2008)
Characterization and validation of methane products from the Atmospheric Infrared Sounder (AIRS). J Geophys Res 113:G00A01. https://​doi.​
org/​10.​1029/​2007J​G0005​00
Yoshida Y, Ota Y, Eguchi N, Kikuchi N, Nobuta K, Tran H, Morino I, Yokota T(2011) Retrieval algorithm for ­CO2 and C­ H4 column abundances fromshort-wavelength infrared spectral observations by the Greenhousegases observing satellite. Atmos Meas Tech 4:717–734. https://​doi.​org/​
10.​5194/​amt-4-​717-​2011
Yoshida Y, Kikuchi N, Morino I, Uchino O, Oshchepkov S, Bril A, Saeki T, Schutgens N, Toon GC, Wunch W, Roehl CM, Wennberg PO, Griffith DWT,
Deutscher NM, Warneke T, Notholt J, Robionson J, Sherlock V, Connor B,
Rettinger M, Sussmann R, Ahonen P, Heikkinen P, Kyro E, Mendonca J,
Strong K, Hase F, Dohe S, Yokota T (2013) Improvement of the retrieval
algorithm for GOSAT SWIR X­ CO2 and X­ CH4 and their validation using
TCCON data. Atmos Meas Tech 6:1533–1547. https://​doi.​org/​10.​5194/​
amt-6-​1533-​2013
Zhang Y, Guanter L, Berry JA, Joiner J, van der Tol C, Huete A, Gitelson A, Voigt
M, Köhler P (2014) Estimation of vegetation photosynthetic capacityfrom space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biol 20:3727–3742

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

Ryoichi Imasu is the chief scientist of the GOSAT-2 Science Team.
TM is the project leader of GOSAT-2 in the National Institute for Environmental Studies (NIES). MN is the project manager of GOSAT-2 mission in Japan Aerospace Exploration Agency (JAXA).

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