Hiroshi Suto’s research while affiliated with Japan Aerospace Exploration Agency and other places

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Publications (121)


The GOBLEU monitoring instrument suite. The instrument suite consists of two modules: GHG observation (left) and NO2/SIF observation (right). These modules are connected by optical fiber from the window seat. The instrumental parameters, including the observation setting, differ between left-side and right-side seats. The instruments are labeled as “-L” or “-R” for their proper seat to avoid missetting
The collected spectra by FNO2 (top), FSIF (middle), and FGHG (bottom) during a ground function test period
The GOBLEU instrument suite onboard the ANA aircraft. The instruments are seated on cabin seats (top left panel), targeting the input optics to slant-view thorough the cabin window (top right panel)
The GOBLEU observation configuration. “New passenger” (Monitoring instruments) onboard ANA passenger flights and slant-viewing to detect changes in NO2, SIF, and CO2 levels over the surface using cabin seats. A typical sampling distance in along-track is 100 m with 50 km across-track coverage. In the double-side case, the coverage is up to 100 km. The sampling size for across-track direction depends on the viewing angle from the aircraft, and it varies from sub-km to 2 km
The observation flight route with an EDGAR CO2 emission map (left) and a TROPOMI SIF intensity map (right). Flight routes cover Tokyo, Osaka, Nagoya, Fukuoka, Sendai, and Sapporo in Japan and industrial area between Tokyo to Fukuoka

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The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU): a mission overview, instrument description, and results from the first flight
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  • Full-text available

August 2024

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46 Reads

Carbon Balance and Management

Hiroshi Suto

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Akihiko Kuze

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Ayako Matsumoto

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[...]

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Yasuhiro Tsubakihara

Background The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU) is a new joint project by Japan Aerospace Exploration Agency (JAXA) and ANA HOLDING INC. (ANAHD), which operates ANA flights. GOBLEU aims to visualizes our climate mitigation effort progress in support of subnational climate mitigation by collecting greenhouse gas (GHG) data as well as relevant data for emissions (nitrous dioxide, NO2) and removals (Solar-Induced Fluorescence, SIF) from regular passenger flights. We developed a luggage-sized instrument based on the space remote-sensing techniques that JAXA has developed for Japan’s Greenhouse gas Observing SATellite (GOSAT). The instrument can be conveniently installed on a coach-class passenger seat without modifying the seat or the aircraft. Results The first GOBLEU observation was made on the flight from the Tokyo Haneda Airport to the Fukuoka Airport, with only the NO2 module activated. The collected high-spatial-resolution NO2 data were compared to that from the TROPOspheric Monitoring Instrument (TROPOMI) satellite and surface NO2 data from ground-based air quality monitoring stations. While GOBLEU and TROPOMI data shared the major concentration patterns largely driven by cities and large point sources, regardless of different observation times, we found fine-scale concentration pattern differences, which might be an indication of potential room for GOBLEU to bring in new emission information and thus is worth further examination. We also characterized the levels of NO2 spatial correlation that change over time. The quickly degrading correlation level of GOBLEU and TROPOMI suggests a potentially significant impact of the time difference between CO2 and NO2 as an emission marker and, thus, the significance of co-located observations planned by future space missions. Conclusions GOBLEU proposes aircraft-based, cost-effective, frequent monitoring of greenhouse emissions by GOBLEU instruments carried on regular passenger aircraft. Theoretically, the GOBLEU instrument can be installed and operated in most commercially used passenger aircraft without modifications. JAXA and ANAHD wish to promote the observation technique by expanding the observation coverage and partnership to other countries by enhancing international cooperation under the Paris Agreement.

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Updated spectral radiance calibration on TIR bands for TANSO-FTS-2 onboard GOSAT-2

September 2022

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104 Reads

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2 Citations

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse Gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to the longwave Thermal InfraRed radiation (TIR) with 0.2 cm-1 spectral sampling, and the corresponding spectral resolution (full width at half maximum, FWHM) of TIR region is less than 0.27 cm-1. TANSO-FTS-2 has operated nominally since February 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version v210210 TIR products of TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the pointing mirror thermal emission. These correction parameters are characterized by an optimization that depends on the difference between the spectral radiance of TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for the simultaneous nadir overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1σ) from 180 to 330 K for the 680 cm-1 CO2 spectral range. The brightness temperatures between TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO2 spectral ranges at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-orthogonal simultaneous off-nadir overpass (2O-SONO) is now done for TANSO-FTS-2 and IASI, TANSO-FTS-2 and AIRS, and TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO2 spectral range for scenes with forward and backward viewing angles greater than 20∘. These multi-satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products.



Retrieval of greenhouse gases from GOSAT and GOSAT-2 using the FOCAL algorithm

June 2022

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174 Reads

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24 Citations

We show new results from an updated version of the Fast atmOspheric traCe gAs retrievaL (FOCAL) retrieval method applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL was originally developed for estimating the total column carbon dioxide mixing ratio (XCO2) from spectral measurements made by the Orbiting Carbon Observatory-2 (OCO-2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species and their uncertainties within one single retrieval. The main focus of the current paper is on methane (XCH4; full-physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2, it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. For XCO2, the new FOCAL retrieval (v3.0) significantly increases the number of valid data compared with the previous FOCAL retrieval version (v1) by 50 % for GOSAT and about a factor of 2 for GOSAT-2 due to relaxed pre-screening and improved post-processing. All v3.0 FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with the Total Carbon Column Observing Network (TCCON) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are on the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data. Noting that only few XN2O measurements from satellites exist so far, the GOSAT-2 FOCAL product will be a valuable contribution in this context.


Figure 2: Polarization sensitivity model for bands 4 and 5. The blue line shows the polarization sensitivity as the transmittance ratio between p-and s-polarization against wavenumber. The gray line shows the observed spectral radiance in the TIR band for the TANSO-FTS-2.
The averaged difference (Ave.) and deviation (SD.) of brightness temperatures between the TANSO-FTS-2 and multi-satellite sensors with SNO
Updated spectral radiance calibration on TIR bands for the TANSO-FTS-2 onboard GOSAT-2

May 2022

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27 Reads

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to longwave Thermal InfraRed radiation (TIR) with 0.2 cm-1 spectral intervals. The TANSO-FTS-2 has operated nominally since Feb 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version 210210 TIR products of the TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the scanner mirror thermal emission. These correction parameters are characterized by an optimization which depends on the difference between the spectral radiance of the TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for Simultaneous Nadir Overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between the TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1σ) from 180 K to 330 K for the 680 cm-1 CO2 channel. The brightness temperatures between the TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 K to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO2 channels at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-Orthogonal Simultaneous Off-Nadir Overpass (2O-SONO) is now done for the TANSO-FTS-2 and IASI, the TANSO-FTS-2 and AIRS, and the TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for the TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO2 channel for scenes with forward and backward viewing angles greater than 20°. These multi-satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for the TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products.


Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities

May 2022

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65 Reads

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15 Citations

Remote Sensing of Environment

We retrieved and examined the partial-column densities of carbon dioxide (CO2) in the lower (LT, typically 0–4 km) and upper (UT, typically 4–12 km) troposphere (XCO2LT and XCO2UT) collected over six global megacities: Beijing, New Delhi, New York City, Riyadh, Shanghai, and Tokyo. The radiance spectra were collected using the Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT). Our retrieval method uniquely utilizes reflected sunlight with two orthogonal components of polarization and thermal emissions. We defined megacity concentration enhancement due to surface CO2 emissions as XCO2LT minus XCO2UT, allowing us to overcome some of the challenges in the enhancement analysis using existing column density data. We examined the relationship between the XCO2LT enhancements from the time series of intensive target observations over megacities and the inverse of simulated wind speed, which could be potentially used to estimate surface emissions. Next, we attempted to estimate the average emission intensity for each city from the linear regression slope. We also compared our obtained emission estimates with the Open-Data Inventory for Anthropogenic CO2 (ODIAC) inventory for evaluation. Our results demonstrate the potential utility of the new partial-column density retrievals for estimating megacity CO2 emissions. More frequent and comprehensive coverage characterizing the spatial distribution of emissions is necessary to reduce random error and bias associated with the obtained estimate.


Figure 11. Example time series of TCCON and GOSAT FOCAL data at Lamont (station code oc). (a) XCO2. (b) XCH4 full physics product. (c) XCH4 proxy product. (d) XH2O. (e) δD.
Figure A3. Scatter plot of the data shown in Fig. A2. (a) SLIMCO2 data vs. CT2019B. (b) SLIMCH4 vs. TM5. σ corresponds to the standard deviation of the difference δ corresponds to the average bias, and ρ is the Pearson correlation coefficient.
Coefficients of linear uncertainty correction.
Results from TCCON comparisons. Nstations denotes the number of TCCON stations involved in the comparison, N data is the
Retrieval of greenhouse gases from GOSAT and greenhouse gases and carbon monoxide from GOSAT-2 using the FOCAL algorithm

March 2022

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151 Reads

Recently, the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm has been applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL has been originally developed for Orbiting Carbon Observatory-2 (OCO-2) retrievals with the focus on the derivation of carbon dioxide (XCO2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species. Here, we show new results from updated GOSAT and GOSAT-2 FOCAL retrievals. The main focus is placed on methane (XCH4; full physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2 it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. Compared to the previous product version (v1), the number of valid XCO2 data could be significantly increased in the updated version (v3.0) by 50 % for GOSAT and about a factor of two for GOSAT-2. All FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with TCCON (Total Carbon Column Observing Network) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes in the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are in the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data.


Mesh plots of the VCDNO2 (left panel) and XCO2 (right panel) retrieved during 11:55:00‐12:14:00 (JST). White diamonds represent locations of the stacks. White and black arrows represent the wind direction (not the wind speed) derived from the numerical model and the manual adjustment. White dotted rectangles represent the cross‐sectional area (800 × 2,000 m) perpendicular to the adjusted wind direction, which was centered at a point 400 m away from the middle point between the two stacks.
Cross‐sectional plume of emitted XCO2 (red) and VCDNO2 (blue) perpendicular to the wind direction 400 m away from the middle point between the two stacks. A horizontal axis represents the distance from a center line (m). The legend shows the inversed and pre‐calculated emission rates of CO2 and NOx in units of kg s⁻¹ in red and blue text, respectively.
First Concurrent Observations of NO 2 and CO 2 From Power Plant Plumes by Airborne Remote Sensing

July 2021

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87 Reads

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19 Citations

Plain Language Summary Burning of fossil fuels at high temperatures constitutes a major anthropogenic source of nitrogen oxides (NOx) and carbon dioxide (CO2). While CO2 stays in the atmosphere for hundreds of years, thereby being a well‐mixed gas, NO2 has a much shorter lifetime of only a few hours. This substantial difference in lifetime between NO2 and CO2 means that concurrent NO2 and CO2 observations obtained by the same platform can be used to identify the locations and strength of CO2 emissions from point sources such as power plants. In February 2018, for the first time, we obtained concurrent airborne spectroscopic NO2 and CO2 observations over an urban area, to demonstrate the traceability of NO2 to CO2. The plumes of co‐emitted NO2 and CO2 were derived from measured spectra. The plumes of NO2 and CO2 co‐emitted from the stacks of power plants were well identified owing to constraint by NO2. Uncertainties of inversed emission rates were statistically derived. For CO2, the results were within 40% in agreement with a bottom‐up emission inventory known as REAS v3.1. For NOx, however, a disagreement of 80% was identified, likely due to the uncertainties of the inventory data or in the NOx partitioning in fresh plumes.


XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm

May 2021

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171 Reads

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27 Citations

Since 2009, the Greenhouse gases Observing SATellite (GOSAT) has performed radiance measurements in the near-infrared (NIR) and shortwave infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 have also been available. We present the first results from the application of the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL was initially developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast, it is currently being considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This particularly includes modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT and GOSAT-2 is the independent use of both S- and P-polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT's wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O), and solar-induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semi-heavy water vapour (HDO). In the case of GOSAT-2, the retrieval of nitrous oxide (XN2O) and carbon monoxide (CO) may also be possible. Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal agreement of long-term temporal variations within about 1 ppm over 1 decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a station-to-station bias of the new GOSAT-FOCAL product to the ground-based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until the end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results and also TCCON.


Thermal and near-infrared sensor for carbon observation Fourier transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse gases Observing SATellite-2 (GOSAT-2) during its first year in orbit

March 2021

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395 Reads

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88 Citations

The Japanese Greenhouse gases Observing SATellite-2 (GOSAT-2), in orbit since 29 October 2018, follows up the GOSAT mission, itself in orbit since 23 January 2009. GOSAT-2 monitors carbon dioxide and methane in order to increase our understanding of the global carbon cycle. It simultaneously measures carbon monoxide emitted from fossil fuel combustion and biomass burning and permits identification of the amount of combustion-related carbon. To do this, the satellite utilizes the Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2). This spectrometer detects gas absorption spectra of solar radiation reflected from the Earth's surface in the shortwave-infrared (SWIR) region as well as the emitted thermal infrared radiation (TIR) from the ground and the atmosphere. TANSO-FTS-2 can measure the oxygen A band (0.76 µm), weak and strong CO2 bands (1.6 and 2.0 µm), weak and strong CH4 bands (1.6 and 2.3 µm), a weak CO band (2.3 µm), a mid-wave TIR band (5.5–8.4 µm), and a long-wave TIR band (8.4–14.3 µm) with 0.2 cm-1 spectral sampling intervals. TANSO-FTS-2 is equipped with a solar diffuser target, a monochromatic light source, and a blackbody for spectral radiance calibration. These calibration sources permit characterization of time-dependent instrument changes in orbit. The onboard-recalibrated instrumental parameters are considered in operational level-1 processing and released as TANSO-FTS-2 level-1 version 102102 products, which were officially released on 25 May 2020. This paper provides an overview of the TANSO-FTS-2 instrument, the level-1 processing, and the first-year in-orbit performance. To validate the spectral radiance calibration during the first year of operation, the spectral radiance of the version 102102 product is compared at temporally coincident and spatially collocated points from February 2019 to March 2020 with TANSO-FTS on GOSAT for SWIR and with AIRS on Aqua and IASI on METOP-B for TIR. The spectral radiances measured by TANSO-FTS and TANSO-FTS-2 agree within 2 % of the averaged bias and 0.5 % standard deviation for SWIR bands. The agreement of brightness temperature between TANSO-FTS-2 and AIRS–IASI is better than 1 K in the range from 220 to 320 K. GOSAT-2 not only provides seamless global CO2 and CH4 observation but also observes local emissions and uptake with an additional CO channel, fully customized sampling patterns, higher signal-to-noise ratios, and wider pointing angles than GOSAT.


Citations (64)


... GOSAT is the world's first carbon satellite (Kadygrov et al., 2009;Kuze et al., 2009;Shiomi et al., 2022), launched by Japan on 23 January 2009. This satellite is equipped with TANSO-FTS, which can detect gas absorption spectra of reflected light in the short-wave infrared (SWIR) region (0.76, 1.6, and 2.0 μm) and thermal infrared (TIR) band (from 5.5 to 14.3 μm) from the Frontiers in Environmental Science frontiersin.org ...

Reference:

Spatiotemporal analysis of global atmospheric XCO2 concentrations before and after COVID-19 using HASM data fusion method
Gosat Partial Column Observation for Better Quantifying Urban CO 2 Flux
  • Citing Conference Paper
  • July 2022

... The research community has explored the ways to utilize greenhouse gas (GHG) data collected from various observation platforms (e.g., ground, aircraft, and satellites) in order to support the evaluation at GST [2]. Over the past decade, GHG remote sensing has significantly advanced, matured, and started playing a key role in collecting GHG data for science [e.g., [3][4][5][6][7][8][9][10][11][12], and for climate mitigation monitoring applications [e.g., [13][14][15]. ...

Updated spectral radiance calibration on TIR bands for TANSO-FTS-2 onboard GOSAT-2

... Several low-Earth orbit (LEO) nadir-viewing satellite instruments launched over the past 2 decades provide near-global measurements of CO vertical profile or total column based on its absorption of thermal or near-infrared (TIR or NIR) radiation [ 8 , 9 ]. These instruments and associated CO retrieval efforts include, but are not limited to, Terra/MOPITT (Measurement Of Pollution In The Troposphere) [ 10 ], Aqua/AIRS (Atmospheric Infra-Red Sounder) [ 11 ], Aura/TES (Tropospheric Emission Spectrometer) [ 12 ], MetOp/IASI (Infrared Atmospheric Sounding Interferometer) [ 13 , 14 ], Suomi-NPP/CrIS (Cross-track Infrared Sounder) [ 15 , 16 ], Envisat/SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHarto-graphY) [ 17 , 18 ], Sentinel-5P/TROPOMI (Tropospheric Monitoring Instrument) [ 19 , 20 ], and GOSAT-2/TANSO-FTS (Thermal And Near infrared Sensor for carbon Observations-Fourier Transform Spectrometer) [ 21 ]. MOPITT uses TIR or NIR radiation or both to retrieve the CO profile. ...

Retrieval of greenhouse gases from GOSAT and GOSAT-2 using the FOCAL algorithm

... Zhang et al., 2011), and differences of seasonality compared to surface observations (Zhou et al., 2023). The combination of SWIR and TIR observations has been used to develop lower troposphere methane products including with GOSAT+AIRS (Worden et al., 2015), GOSAT+IASI (Schneider et al., 2022), and GOSAT-2 (Kuze et al., 2022;Suto, 2022). 95 ...

Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities
  • Citing Article
  • May 2022

Remote Sensing of Environment

... GOBLEU should also provide direct technical and scientific implications to the synergic use of remotely sensed GHG and AQ data that is planned by future space GHG observing missions, such as Japan's Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW; planned launch 2024) [20] and Europe's Copernicus Carbon Dioxide Monitoring mission (CO2M; planned launch 2026) [21]. As recent studies [22][23][24][25][26] demonstrated, simultaneously collecting CO 2 and NO 2 data should enhance our ability to quantify anthropogenic GHG emissions. However, it is important to note that the previous studies have been based on data from different satellite platforms based on certain spatial and temporal colocation criteria or a campaign flight. ...

First Concurrent Observations of NO 2 and CO 2 From Power Plant Plumes by Airborne Remote Sensing

... This type of methodology cannot be used in the infrared spectral region because the molar absorptivity is pressure-temperature-dependent. To overcome this problem, several alternative methods and algorithms have been implemented; they are all based on retrieval methods; these consist of the development of a wavelength-dependent radiation model, the concentration of the gaseous species in question, and environmental conditions such as temperature and pressure [16][17][18][19]. ...

XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm

... GOSAT-2 is in a sun-synchronous orbit at an altitude of 613 km with a six-day revisit cycle. It carries the Thermal And Near infrared Sensor for carbon Observations-Fourier Transform Spectrometer-2 (TANSO-FTS-2) and the Cloud and Aerosol Imager-2 (TANSO-CAI-2) (Suto et al 2021). The IFOV of the TANSO-FTS-2 is 15.8 mrad, which corresponds to a footprint with a diameter of about 9.7 km at nadir. ...

Thermal and near-infrared sensor for carbon observation Fourier transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse gases Observing SATellite-2 (GOSAT-2) during its first year in orbit

... The FOCAL retrieval uses as main input calibrated GOSAT L1B V220.220 spectra from the three SWIR bands (around 0.76, 1.6 and 2.0 µm) of TANSO FTS. GOSAT-2 (Nakajima et al., 2017;Suto et al., 2020) was launched in October 2018 and comprises a similar instrumentation as GOSAT. The GOSAT-2 FTS has the same spectral resolution but an extended spectral range for SIF and CO retrievals. ...

Thermal and near-infrared sensor for carbon observation Fourier-transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse Gases Observing Satellite-2 (GOSAT-2) during its first year on orbit

... We use the latest release of the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) product (v1.8) (Schneising et al., 2023), which includes processing improvements such as an increased polynomial degree (cubic instead of quadratic) and an updated digital elevation model to account for various localized topography-related biases (Hachmeister et al., 2022). Furthermore, the machine-learning-based quality filter in the post-processing is improved to further reduce scenes with residual clouds. ...

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications

... Considering the NASA TEMPO (Tropospheric Emissions Monitoring of Pollution) (Zoogman et al., 2017) geostationary satellite mission that will operate in the visible and ultraviolet spectral regions, Geostationary Trace gas and Aerosol Sensor Optimization (Geo-TASO) (Leitch et al., 2014;Nowlan et al., 2016), Airborne Compact Atmospheric Mapper (ACAM) (Kowalewski and Janz, 2009;Lamsal et al., 2017;Nowlan et al., 2018) and GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) are examples of airborne simulators that 60 have provided the resources to design and enhance the retrieval algorithms. In the SWIR, AVIRIS (Thompson et al., 2016), MAMAP (Gerilowski et al., 2011), GHOST (Humpage et al., 2018) and JAXA's airborne instrument (Kuze et al., 2020), have been utilized to retrieve methane emissions. The airborne simulator for MethaneSAT is appropriately named MethaneAIR and it includes two spectrometers scanning almost the same SWIR regions as MethaneSAT. ...

City-level CO2, CH4, and NO2 observations from Space: Airborne model demonstration over Nagoya
  • Citing Preprint
  • January 2020