The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O₂) A-band at 0.765 microns and the carbon dioxide (CO₂) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO₂ dry-air mole fraction, XCO₂. Variations of XCO₂ in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO₂. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO₂ field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO₂ distribution could be misinterpreted as evidence for CO₂ fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s⁻¹ across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO₂. Each of these soundings has an unprecedented combination of spatial resolution (< 3 km²/sounding), spectral resolving power (λ∕Δλ > 17 000), dynamic range (∼ 10⁴), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

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 (CO₂) concentrations, but use different spectrometer technologies, observing geometries, and ground track repeat cycles. To demonstrate the effectiveness of satellite remote sensing for CO₂ 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 CO₂ dry air mole fraction (XCO₂) retrieved from GOSAT and OCO-2 from September 2014 to May 2016. GOSAT retrievals used Build 7.3 (V7.3) of the Atmospheric CO₂ 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 XCO₂ bias or standard deviation with time, demonstrating that both instruments are well calibrated.

In the fall of 2017, an airborne field campaign was conducted from the NASA Armstrong Flight Research Center in Palmdale, California, to advance the remote sensing of aerosols and clouds with multi-angle polarimeters (MAP) and lidars. The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign was jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON). Six instruments were deployed on the ER-2 high-altitude aircraft. Four were MAPs: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary EXploration (SPEX airborne), and the Research Scanning Polarimeter (RSP). The remainder were lidars, including the Cloud Physics Lidar (CPL) and the High Spectral Resolution Lidar 2 (HSRL-2). The southern California base of ACEPOL enabled observation of a wide variety of scene types, including urban, desert, forest, coastal ocean, and agricultural areas, with clear, cloudy, polluted, and pristine atmospheric conditions. Flights were performed in coordination with satellite overpasses and ground-based observations, including the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI), sun photometers, and a surface reflectance spectrometer. ACEPOL is a resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. Data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion. They are freely available to the public. The DOI for the primary database is https://doi.org/10.5067/SUBORBITAL/ACEPOL2017/DATA001 (ACEPOL Science Team, 2017), while for AirMSPI it is https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/ELLIPSOID_V006 and https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/TERRAIN_V006 (ACEPOL AirMSPI 75 Science Team, 2017a, b). GroundMSPI data are at https://doi.org/10.5067/GROUND/GROUNDMSPI/ACEPOL/RADIANCE_v009 (GroundMSPI Science Team, 2017). Table 3 lists further details of these archives. This paper describes ACEPOL for potential data users and also provides an outline of requirements for future field missions with similar objectives.

Remote sensing instruments, both aircraft and on-orbit platforms, undergo extensive laboratory calibrations to determine their geometric, spectral, and radiometric responses. Additional in-flight radiometric calibrations can be performed using well-characterized earth targets. The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign provided such an opportunity when the ER-2 aircraft overflew Railroad Valley on August 13 and 15, 2019. Surface reflectances were available from the August 4, 2019 field team and from the Radiometric Calibration Network (RadCalNet) portal, and spectral aerosol optical depths from an on-site AERosol RObotic NETwork (AERONET) sunphotometer. The Enhanced MODIS Airborne Simulator (eMAS), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), and the “Classic” Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-C) sensors individually performed a vicarious calibration using their respective methodologies and selection of input parameters. A comparison of the at-sensor radiances predicted from these independent analyses highlights some of the uncertainties in the inputs, including choice of solar irradiance model. Although good agreement, within 5%, is found at visible wavelengths, difference can be as large as 15% in the shortwave infrared (SWIR). This highlights the need for the remote sensing community to agree upon a standard solar model, to remove sensor-to-sensor biases derived from in-flight calibrations.

Vicarious calibration is the determination of an on-orbit sensor’s radiometric response using measurements over test sites such as Railroad Valley (RRV), Nevada. It has the highest accuracy when a remote sensor’s view angle is aligned with that of the surface measurements, namely at a nadir view. For view angles greater than 10°, the dominant error is the uncertainty in the off-nadir correction factor. The factor is largest in the back-scatter principal plane and can reach 20%. The Orbiting-Carbon Observatory has access to a number of datasets to determine this deviation. These include measurements from field instruments such as the Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), as well as satellite measurements from Multi-angle Imaging SpectroRadiometer (MISR) and MODerate resolution Imaging Spectroradiometer (MODIS). The correction factor derived from PARABOLA is consistent in time and space to within 2% for view angles as large as 30°. Field spectrometer data show that the correction term is spectrally invariant. For this reason, a time-invariant model of RRV surface reflectance, along with empirically derived coefficients, is sufficient to use in the calibration of off-nadir sensors, provided there has been no recent rainfall. With this off-nadir correction, calibrations can be expected to have uncertainties within 5%.