Hiroshi Suto

Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring

The Greenhouse Gases Observing Satellite (GOSAT) monitors carbon dioxide (CO(2)) and methane (CH(4)) globally from space using two instruments. The Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) detects gas absorption spectra of the solar short wave infrared (SWIR) reflected on the Earths surface as well as of the thermal infrared radiated from the ground and the atmosphere. TANSO-FTS is capable of detecting three narrow bands (0.76, 1.6, and 2.0 microm) and a wide band (5.5-14.3 microm) with 0.2 cm(-1) spectral resolution (interval). The TANSO Cloud and Aerosol Imager (TANSO-CAI) is an ultraviolet (UV), visible, near infrared, and SWIR radiometer designed to detect cloud and aerosol interference and to provide the data for their correction. GOSAT is placed in a sun-synchronous orbit 666 km at 13:00 local time, with an inclination angle of 98 degrees . A brief overview of the GOSAT project, scientific requirements, instrument designs, hardware performance, on-orbit operation, and data processing is provided.

Continuous measurements of methane from a tower network over Siberia

We have been conducting continuous measurements of Methane (sCH4) concentration from an expanding network of towers (JR-STATION: Japan–Russia Siberian Tall Tower Inland Observation Network) located in taiga, steppe and wetland biomes of Siberia since 2004. High daytime means (>2000 ppb) observed simultaneously at several towers during winter, together with in situ weather data and NCEP/NCAR reanalysis data, indicate that high pressure systems caused CH4 accumulation at subcontinental scale due to the widespread formation of an inversion layer. Daytime means sometimes exceeded 2000 ppb, particularly in the summer of 2007 when temperature and precipitation rates were anomalously high over West Siberia, which implies that CH4 emission from wetlands were exceptionally high in 2007. Many hot spots detected by MODIS in the summer of 2007 illustrate that the contribution of biomass burning also cannot be neglected. Daytime mean CH4 concentrations from the Siberian tower sites were generally higher than CH4 values reported at NOAA coastal sites in the same latitudinal zone, and the difference in concentrations between two sets of sites was reproduced with a coupled Eulerian–Lagrangian transport model. Simulations of emissions from different CH4 sources suggested that the major contributor to variation switched from wetlands during summer to fossil fuel during winter.

Vicarious Calibration of the GOSAT Sensors Using the Railroad Valley Desert Playa

Japans Greenhouse Gases Observing Satellite (GOSAT) was successfully launched into a sun-synchronous orbit on January 23, 2009 to monitor global distributions of carbon dioxide ( CO2) and methane (CH4). GOSAT carries two instruments. The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) measures reflected radiances in the 0.76 μm oxygen band and in the weak and strong CO2 bands at 1.6 and 2.0 μm. The TANSO Cloud and Aerosol Imager (TANSO-CAI) uses four spectral bands at 0.380, 0.674, 0.870, and 1.60 μm to identify clear soundings and to provide cloud and aerosol optical properties. Vicarious calibration was performed at Railroad Valley, Nevada, in the summer of 2009. The site was chosen for its flat surface and high spectral reflectance. In situ measurements of geophysical parameters, such as surface reflectance, aerosol optical thickness, and profiles of temperature, pressure, and humidity, were acquired at the overpass times. Because the instantaneous field of view of TANSO-FTS is large (10.5 km at nadir), the spatially limited reflectance measurements at the field sites were extrapolated to the entire footprint using independent satellite data. During the campaign, six days of measurements were acquired from two different orbit paths. Spectral radiances at the top of the atmosphere were calculated using vector radiative transfer models coupled with ground in situ data. The agreement of the modeled radiance spectra with those measured by the TANSO-FTS is within 7%. Significant degradations in responsivity since launch have been detected in the short-wavelength bands of both TANSO-FTS and TANSO-CAI.

Long-Term Vicarious Calibration of GOSAT Short-Wave Sensors: Techniques for Error Reduction and New Estimates of Radiometric Degradation Factors

This work describes the radiometric calibration of the short-wave infrared (SWIR) bands of two instruments aboard the Greenhouse gases Observing SATellite (GOSAT), the Thermal And Near infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). Four vicarious calibration campaigns (VCCs) have been performed annually since June 2009 at Railroad Valley, NV, USA, to estimate changes in the radiometric response of both sensors. While the 2009 campaign ( VCC2009) indicated significant initial degradation in the sensors compared to the prelaunch values, the results presented here show that the stability of the sensors has improved with time. The largest changes were seen in the 0.76 μm oxygen A-band for TANSO-FTS and in the 0.380 and 0.674 μm bands for TANSO-CAI. This paper describes techniques used to optimize the vicarious calibration of the GOSAT SWIR sensors. We discuss error reductions, relative to previous work, achieved by using higher quality and more comprehensive in situ measurements and proper selection of reference remote sensing products from the Moderate Resolution Imaging Spectroradiometer used in radiative transfer calculations to model top-of-the-atmosphere radiances. In addition, we present new estimates of TANSO-FTS radiometric degradation factors derived by combining the new vicarious calibration results with the time-dependent model provided by Yoshida (2012), which is based on analysis of on-board solar diffuser data. We conclude that this combined model provides a robust correction for TANSO-FTS Level 1B spectra. A detailed error budget for TANSO-FTS vicarious calibration is also provided.

The instrumentation and the BBM test results of Thermal And Near infrared Sensor for carbon Observation (TANSO) on GOSAT

The Greenhouse gases Observing SATellite (GOSAT) is a satellite to monitor the carbon dioxide (CO2) and the methane (CH4) globally from orbit. GOSAT will be placed in a 666 km sun-synchronous orbit of 13:00 local time, with an inclination angle of 98 deg. Two instruments are accommodated on GOSAT. Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) detects the Short wave infrared (SWIR) reflected on the earths surface as well as the thermal infrared (TIR) radiated from the ground and the atmosphere. TANSO-FTS is capable of detecting wide spectral coverage, specifically, three narrow bands (0.76, 1.6, and 2 micron) and a wide band (5.5-14.3 micron) with 0.2 cm-1 spectral resolution. TANSO Cloud and Aerosol Imager (TANSO-CAI) is a radiometer of ultraviolet (UV), visible, and SWIR to correct cloud and aerosol interference. The paper presents the instrument design of TANSO-FTS/CAI, and test results using Bread Board Model (BBM) are presented.

TIR Spectral Radiance Calibration of the GOSAT Satellite Borne TANSO-FTS With the Aircraft-Based S-HIS and the Ground-Based S-AERI at the Railroad Valley Desert Playa

The thermal infrared (TIR) band of Thermal and Near-Infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse gases Observing SATellite (GOSAT) measures a wide range of scene temperatures using a single detector band with broad spectral coverage. This work describes the vicarious radiometric calibration over a large footprint (10.5 km) and high temperature surface using well-calibrated ground-based and airborne FTS sensors. The vicarious calibration campaign of GOSAT was conducted at Railroad Valley, NV in June 2011. During the campaign, the Scanning High-resolution Interferometer Sounder (S-HIS) mounted on the high-altitude NASA ER-2 aircraft observed upwelling radiation and the ground-based Surface-Atmospheric Emitted Radiance Interferometer (S-AERI) observed infrared thermal emission from the atmosphere and the surface at the same location and time as the GOSAT TANSO-FTS. We validated TANSO-FTS TIR radiance with S-HIS radiance using double difference method, which reduces the effect of differences in the observation geometry. In this paper, we estimated the TANSO-FTS Instantaneous Field of View average temperature and emissivity by the coincident S-AERI and S-HIS observed radiance. The double difference between TANSO-FTS and S-HIS result in a difference of 0.5 K at atmospheric window channels (800 ~ 900 cm-1) and CO2 warm brightness temperature channels (700 ~ 750 cm-1), 0.1 K at ozone channels (980 ~ 1080 cm-1), and more than 2 K at CO2 cool brightness temperature channels (650 ~ 700 cm-1). The main reason of remaining errors is attributed to a calibration error in the TANSO-FTS Level 1B product version under evaluation.

Updated level-1 processing after two-years operation of TANSO-FTS

To monitor the global column concentration of carbon dioxide (CO2) and methane (CH4) from space, the Greenhouse gases Observing SATellite (GOSAT) was launched on January 23, 2009, and has started the operational observation. Thermal and Near Infrared Sensor for Carbon Observation- Fourier Transform Spectrometer (TANSO-FTS) has been continuously measuring CO2 and CH4 distributions globally every three days, and data distribution to the public started from Feb. 16, 2010. During two years operational periods, the radiometric, geometric and spectroscopic characterizations of TANSO have been continuously conducted with updating the Level-1 processing algorithm. To make a precise spectroscopic observation, correction algorithms were newly developed, demonstrated and installed on operational processing. Two major corrections are discussed. One is correction of the scan-speed instability caused by microvibration from satellite. Through the on-orbit data analysis, degrading spectroscopic accuracy caused by periodically micro-vibrations was found, and these distortion effects were compensated with applying the re-sampling technique for interferogram. The other is non-linearity correction in the electronics. In this presentation, the detail of on-orbit characteristics and the current status of Level-1procesing for TANSO will be presented.

On-orbit performance and level 1 data processing of TANSO-FTS and CAI on GOSAT

The Greenhouse gases Observing SATellite (GOSAT) monitors carbon dioxide (CO2) and methane (CH4) globally from space. It is a joint project of Japan Aerospace Exploration Agency (JAXA), Ministry of the Environment (MOE) and National Institute for Environmental Studies (NIES). GOSAT is placed in a sun-synchronous orbit of 666km and 12:48 local time, with an inclination angle of 98 deg. It was launched on January 23, 2009 from Tanegashima Space Center. There are two instruments on GOSAT. The Thermal And Near infrared Sensor for carbon Observation Fourier- Transform Spectrometer (TANSO-FTS) detects the Short wave infrared (SWIR) reflected on the earths surface as well as the thermal infrared (TIR) radiated from the ground and the atmosphere. TANSO-FTS is capable of detecting wide spectral coverage; three narrow bands (0.76, 1.6, and 2 μm) and a wide band (5.5-14.3 μm) with 0.27 cm-1 spectral resolution. The TANSO Cloud and Aerosol Imager (TANSO-CAI) is a radiometer of ultraviolet (UV), visible, and SWIR to correct cloud and aerosol interference. For three months after the launch, the on-orbit function and performance have been checked out. Now level 1A (raw interferogram) and level 2B (spectra) are now being processed and provided regularly with calibration data.

The pre-launch performance test and calibration results of Thermal And Near-infrared Sensor for carbon Observation (TANSO) on GOSAT

In order to characterize the pre-launch performance of Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) and Cloud and Aerosol Imager (TANSO-CAI) on the Green house gases Observing SATellite (GOSAT) under the environmental condition on orbit as well as in the laboratory, the Proto Flight Model (PFM) for TANSO-FTS and CAI have been developed. TANSO-FTS has three narrow bands of 0.76, 1.6 and 2.0 micron (Band 1, 2 and 3) with +/-2.5cm maximum optical path difference, and a wide band of 5.5 - 14.3 micron (band 4) in thermal near infrared region. TANSO-CAI is a radiometer for detection and correction of clouds and aerosol effects which might degrade the column concentration retrieval of CO2 and CH4. It has four spectral band regions; ultraviolet (UV), visible, near IR and SWIR. The basic character of TANSO-FTS and CAI, such as the Signal to Noise Ratio (SNR), the polarization sensitivity (PS), Instantaneous Field Of View (IFOV), spectral response, and also Instrumental Line Shape Function (ILSF) have been characterized by introducing the light emitted from the black body, halogen lamp and the tunable diode laser. In addition to these characterizations, micro vibration effect on orbit has been investigated on TANSO-FTS. There prelaunch test results demonstrated that TANSO will provide data for high accuracy CO2 and CH4 retrieval on orbit.

Characterization and correction of spectral distortions induced by microvibrations onboard the GOSAT Fourier transform spectrometer.

Microvibrations onboard greenhouse gases observing satellite (GOSAT) cause scan speed variations in the TANSO Fourier transform spectrometer. The associated periodic sampling errors generate ghost features in O2 A-band spectra, where surface pressure and aerosol properties are retrieved to determine the optical path through the atmosphere. A correction algorithm has been developed to re-compute the interferograms at equally spaced sampling intervals. The key is to determine iteratively the amplitude and phase of sinusoidal perturbations with predetermined frequencies to minimize the magnitude of the out-of-band ghosts artifacts after correction of the sampling grid. This correction algorithm drastically reduces errors in retrieved surface pressure and improves agreement with ground-based observations.