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

Kuze, Akihiko; Taylor, Thomas E.; Kataoka, Fumie; Bruegge, Carol J.; Crisp, David; Harada, Masatomo; Helmlinger, Mark; Inoue, Mayuri; Kawakami, S.; Kikuchi, Nobuhiro; Mitomi, Yasushi; Murooka, Jumpei; Naitoh, Masataka; O’Brien, D. M.; O’Dell, C.; Ohyama, Hirofumi; Pollock, Randy; Schwandner, F. M.; Shiomi, Kei; Shimada, Hiroshi; Takeda, Toru; Tanaka, Tomoaki; Urabe, Tomoyuki; Yokota, Tatsuya; Yoshida, Yukio

doi:10.1109/tgrs.2013.2278696

Cited by 48 publications

(57 citation statements)

References 17 publications

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“…The stability of these on-board calibration systems has been validated by annual vicarious calibration campaigns. As reported in Kuze et al [14], the vicarious calibration error is less than 7%. However, the dynamic range of the input light level using these solar diffuser and bright desert sources is limited.…”

Section: Objectives Of On-orbit Cross-comparisonssupporting

confidence: 58%

The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2

et al. 2017

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The Greenhouse gases Observing SATellite (GOSAT) launched in January 2009 has provided radiance spectra with a Fourier Transform Spectrometer for more than eight years. The Orbiting Carbon Observatory 2 (OCO-2) launched in July 2014, collects radiance spectra using an imaging grating spectrometer. Both sensors observe sunlight reflected from Earth's surface and retrieve atmospheric carbon dioxide (CO 2 ) concentrations, but use different spectrometer technologies, observing geometries, and ground track repeat cycles. To demonstrate the effectiveness of satellite remote sensing for CO 2 monitoring, the GOSAT and OCO-2 teams have worked together pre-and post-launch to cross-calibrate the instruments and cross-validate their retrieval algorithms and products. In this work, we first compare observed radiance spectra within three narrow bands centered at 0.76, 1.60 and 2.06 µm, at temporally coincident and spatially collocated points from September 2014 to March 2017. We reconciled the differences in observation footprints size, viewing geometry and associated differences in surface bidirectional reflectance distribution function (BRDF). We conclude that the spectral radiances measured by the two instruments agree within 5% for all bands. Second, we estimated mean bias and standard deviation of column-averaged CO 2 dry air mole fraction (XCO 2 ) retrieved from GOSAT and OCO-2 from September 2014 to May 2016. GOSAT retrievals used Build 7.3 (V7.3) of the Atmospheric CO 2 Observations from Space (ACOS) algorithm while OCO-2 retrievals used Version 7 of the OCO-2 retrieval algorithm. The mean biases and standard deviations are −0.57 ± 3.33 ppm over land with high gain, −0.17 ± 1.48 ppm over ocean with high gain and −0.19 ± 2.79 ppm over land with medium gain. Finally, our study is complemented with an analysis of error sources: retrieved surface pressure (P surf ), aerosol optical depth (AOD), BRDF and surface albedo inhomogeneity. We found no change in XCO 2 bias or standard deviation with time, demonstrating that both instruments are well calibrated.

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Section: Objectives Of On-orbit Cross-comparisonssupporting

confidence: 58%

The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2

et al. 2017

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“…The lunar radiometric calibration is consistent with the VCCs within their respective error budgets . Since the work published in Kuze et al (2014), annual VCCs were also performed in 2013, 2014, and 2015, and the data were processed using the same method. Figure 6a shows the TANSO-FTS RDF by comparing the measured radiance, using prelaunch radiometric conversion factor, and forward calculation using surface and radiosonde data together with the RDF model described in Kuze et al (2014).…”

Section: Radiometric Calibration: Response Degradationmentioning

confidence: 99%

“…Best-estimate radiance conversion for SWIR (bands 1, 2, and 3): the spectral radiance data, given in units of volt cm (V cm), have been converted to the calibrated spectral radiance given in units of W cm −1 str −1 using the prelaunch calibration data. The degradation after launch is also corrected using RDF provided in Kuze et al (2014).…”

Section: Calibration Informationmentioning

confidence: 99%

Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space

et al. 2016

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Abstract. A data set containing more than 6 years (February 2009 to present) of radiance spectra for carbon dioxide (CO 2 ) and methane (CH 4 ) observations has been acquired by the Greenhouse gases Observing SATellite (GOSAT, available at http://data.gosat.nies.go.jp/ GosatUserInterfaceGateway/guig/GuigPage/open.do), nicknamed "Ibuki", Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). This paper provides updates on the performance of the satellite and TANSO-FTS sensor and describes important changes to the data product, which has recently been made available to users. With these changes the typical accuracy of retrieved column-averaged dry air mole fractions of CO 2 and CH 4 (X CO 2 and X CH 4 , respectively) are 2 ppm or 0.5 % and 13 ppb or 0.7 %, respectively. Three major anomalies of the satellite system affecting TANSO-FTS are reported: a failure of one of the two solar paddles in May 2014, a switch to the secondary pointing system in January 2015, and most recently a cryocooler shutdown and restart in August 2015. The Level 1A (L1A) (raw interferogram) and the Level 1B (L1B) (radiance spectra) of version V201 described here have longterm uniform quality and provide consistent retrieval accuracy even after the satellite system anomalies. In addition, we discuss the unique observation abilities of GOSAT made possible by an agile pointing mechanism, which allows for optimization of global sampling patterns.

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“…The detectors for the TIR and SWIR regions are a photoconductive HgCdTe and Si/InGaAs, respectively. The TIR spectra are calibrated using onboard blackbody and deep space view data, whereas the SWIR spectra are corrected using vicarious calibration data [64] and on-orbit solar calibration data [65]. As mentioned in Sections 2.1.1 and 2.1.2, the Level 2 (retrieval) algorithm is also different for TIR and SWIR.…”

Section: Appendix Amentioning

confidence: 99%

Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

et al. 2017

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Spatial and temporal variability of atmospheric water vapor (H 2 O) is extremely high, and therefore it is difficult to accurately evaluate the measurement precision of H 2 O data by a simple comparison between the data derived from two different instruments. We determined the measurement precisions of column-averaged dry-air mole fractions of H 2 O (XH 2 O) retrieved independently from spectral radiances in the thermal infrared (TIR) and the short-wavelength infrared (SWIR) regions measured using a Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT), by an intercomparison between the two TANSO-FTS XH 2 O data products and the ground-based FTS XH 2 O data. Furthermore, the spatial variability of XH 2 O was also estimated in the intercomparison process. Mutually coincident XH 2 O data above land for the period ranging from April 2009 to May 2014 were intercompared with different spatial coincidence criteria. We found that the precisions of the TANSO-FTS TIR and TANSO-FTS SWIR XH 2 O were 7.3%-7.7% and 3.5%-4.5%, respectively, and that the spatial variability of XH 2 O was 6.7% within a radius of 50 km and 18.5% within a radius of 200 km. These results demonstrate that, in order to accurately evaluate the measurement precision of XH 2 O, it is necessary to set more rigorous spatial coincidence criteria or to take into account the spatial variability of XH 2 O as derived in the present study.

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

Cited by 48 publications

References 17 publications

The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2

The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2

Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space

Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

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