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

Kuze, Akihiko; O’Brien, D. M.; Taylor, Thomas E.; Day, J O; O’Dell, Chris; Kataoka, Fumie; Yoshida, Mayumi; Mitomi, Yasushi; Bruegge, Carol J.; Pollock, Randy; Basilio, Ralph R.; Helmlinger, Mark; Matsunaga, Tsuneo; Kawakami, S.; Shiomi, Kei; Urabe, Tomoyuki; Shimada, Hiroshi

doi:10.1109/tgrs.2010.2089527

Cited by 53 publications

(52 citation statements)

References 26 publications

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“…3, orange stars), and, although data from those targets will not be analyzed in this paper, the data will help assess the radiometric calibration of the instrument, its ability to measure large urban sources of CO 2 , validate its solar-induced fluorescence observations , and assess its ability to measure vertically resolved information about CO 2 . Railroad Valley is a heavily instrumented radiometric calibration site (Kuze et al, 2011), and Libya has surface properties that are valuable for radiometric calibration. Shanghai, São Paulo, and Mexico City are geographically well-constrained urban regions with significant CO 2 emissions.…”

Section: Target Locations and Selectionmentioning

confidence: 99%

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON

et al. 2017

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Abstract. NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dryair mole fraction, X CO 2 , in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X CO 2 from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TC-CON). The OCO-2 X CO 2 retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X CO 2 data quality throughout its mission.

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Section: Target Locations and Selectionmentioning

confidence: 99%

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON

et al. 2017

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“…The RDFs are estimated from annul vicarious calibration campaigns in Railroad Valley (RRV), Nevada, USA by the joint GOSAT and OCO-2 teams, and monthly on-orbit solar diffuser calibrations. GOSAT sensitivity experienced rapid degradation in the first two years on-orbit, especially at shorter wavelengths [18]. The degradation has slowed in subsequent years.…”

Section: Spectral Radiance Data and Retrieved Xco 2 For The Inter-commentioning

confidence: 99%

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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“…The Greenhouse Gases Observing Satellite, developed by the Japan Aerospace Exploration Agency (JAXA), the Ministry of the Environment of Japan (MOE) and the National Institute for Environmental Studies (NIES) of Japan, was successfully launched on 23 January 2009 from Tanegashima Island, Japan [30,42]. GOSAT flies in a sun-synchronous orbit with a ground speed of 6.8 km/s at an altitude of 666 km and maintains an inclination angle of 98 • .…”

Section: Gosat Tanso-caimentioning

confidence: 99%

“…GOSAT flies in a sun-synchronous orbit with a ground speed of 6.8 km/s at an altitude of 666 km and maintains an inclination angle of 98°. Its period of revolution is approximately 1 hour and 40 minutes, the local nadir overpass time is approximately 12:47 p.m., and it revisits the same point in space every three days with a total of 44 paths [42]. GOSAT carries two primary instruments, TANSO-FTS and the TANSO-CAI, to monitor the global distributions of carbon dioxide and methane and to detect clouds and aerosols [43].…”

Section: Gosat Tanso-caimentioning

confidence: 99%

A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

et al. 2016

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Abstract:In this paper, we introduced a new algorithm for retrieving aerosol optical depth (AOD) over land, from the Cloud and Aerosol Imager (CAI), which is one of the instruments on the Greenhouse Gases Observing Satellite (GOSAT) for detecting and correcting cloud and aerosol interference. We used the GOSAT and AErosol RObotic NETwork (AERONET) collocated data from different regions over the globe to analyze the relationship between the top-of-atmosphere (TOA) reflectance in the shortwave infrared (1.6 µm) band and the surface reflectance in the red (0.67 µm) band. Our results confirmed that the relationships between the surface reflectance at 0.67 µm and TOA reflectance at 1.6 µm are not constant for different surface conditions. Under low AOD conditions (AOD at 0.55 µm < 0.1), a Normalized Difference Vegetation Index (NDVI) based regression function for estimating the surface reflectance of 0.67 µm band from the 1.6 µm band was summarized, and it achieved good performance, proving that the reflectance relations of the 0.67 µm and 1.6 µm bands are typically vegetation dependent. Since the NDVI itself is easily affected by aerosols, we combined the advantages of the Aerosol Free Vegetation Index (AFRI), which is aerosol resistant and highly correlated with regular NDVI, with our regression function, which can preserve the various correlations of 0.67 µm and 1.6 µm bands for different surface types, and developed a new surface reflectance and aerosol-free NDVI estimation algorithm, which we named the Modified AFRI 1.6 algorithm. This algorithm was applied to AOD retrieval, and the validation results for our algorithm show that the retrieved AOD has a consistent relationship with AERONET measurements, with a correlation coefficient of 0.912, and approximately 67.7% of the AOD retrieved data were within the expected error range (± 0.1 ± 0.15AOD (AERONET) ).

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

Cited by 53 publications

References 26 publications

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) <i>X</i><sub>CO<sub>2</sub></sub> measurements with TCCON

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) <i>X</i><sub>CO<sub>2</sub></sub> measurements with TCCON

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

A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

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

Cited by 53 publications

References 26 publications

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) &lt;i&gt;X&lt;/i&gt;&lt;sub&gt;CO&lt;sub&gt;2&lt;/sub&gt;&lt;/sub&gt; measurements with TCCON

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) &lt;i&gt;X&lt;/i&gt;&lt;sub&gt;CO&lt;sub&gt;2&lt;/sub&gt;&lt;/sub&gt; measurements with TCCON

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

A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

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Product

Resources

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Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) <i>X</i><sub>CO<sub>2</sub></sub> measurements with TCCON

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) <i>X</i><sub>CO<sub>2</sub></sub> measurements with TCCON