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The observing strategy of the Geostationary Carbon Observatory (GeoCarb), which is a “step and stare” approach, can lead to distortions in the instrument spectral response function (ISRF) when there are gradients in brightness across instrument field of view. These distortions induce errors in the retrieved trace gases. In order to minimize these errors, the GeoCarb instrument design was modified to include a “slit homogenizer” whose purpose is to scramble the pattern of the incoming light and effectively remove the ISRF distortions caused by the variations in illumination across the slit. As a risk reduction, GeoCarb procured six different homogenizers and had them tested for performance in a benchtop optical system. The major finding is that the homogenizer performance depends strongly on the polarization of the incoming light, with the sensitivity growing as a function of wavelength. The width of the ISRF is substantially smaller when the light is vertically polarized (orthogonal to the slit length) compared to horizontally polarized (parallel to the slit length), and the throughput is accordingly reduced. These effects are due to the effects of the gold coating and high incidence angles present in the GeoCarb homogenizer design, which was verified using a polarization-dependent model generalized from previous homogenizer modeling work. The results strongly recommend controlling the polarization of the light entering a similar implementation using a polarizer, depolarizer, or polarization scrambler for other instruments attempting to mitigate scene illumination non-uniformity effects, as well as a robust characterization of the polarization sensitivity of all key subsystems.

Many forest cover maps have been generated by using optical and/or microwave images and various forest definitions, but these forest cover maps have large discrepancies. Both forest definition and validation data used for accuracy assessment of forest cover maps are often considered as two of the major factors for the discrepancy among these forest cover maps. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: (1) tree canopy height and (2) canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and Moderate Resolution Imaging Spectroradiometer, and the forest definition of the Food and Agriculture Organization (FAO) of the United Nations (>5-m tree height and >10 % canopy coverage). The canopy height and coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 were used to assess annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest and evergreen forest cover maps in the Brazilian Amazon.

Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often co-emitted with CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using Measurements of Pollution in The Troposphere (MOPITT) v8 data is performed to estimate fire emissions which are then converted into CO2 fire emissions (called FIREMo) through the use of the emission ratio. These optimized CO2 fire emissions are used to rebalance the CO2 net ecosystem exchange (NEEMo) and respiration (RhMo) with the global CO2 growth rate. Subsequently, in a second step, these rebalanced fluxes are used as priors for a CO2 inversion to derive the NEE and ocean fluxes constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ (IS) CO2 data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Emissions Database (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal, and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 (FIRE3) and GFED4.1s (FIRE4) fires, as well as with an inversion using both Carnegie–Ames–Stanford Approach (CASA)-GFED3 NEE and GFED3 fire priors (priorCMS). After comparison at the different scales, the inversions are evaluated against Total Carbon Column Observing Network (TCCON) data. Comparison of the flux estimates shows that at the global scale posterior net flux estimates are more robust than the different prior flux estimates. However, at the regional scale, we can observe differences in fire emissions among the priors, resulting in differences among the NEE prior emissions. The derived NEE prior emissions are rebalanced in concert with the fires. Consequently, the differences observed in the NEE posterior emissions are a result of the balancing with fires and the constraints provided by CO2 observations. Tropical net flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger net CO2 sources are derived with posterior fire emissions using either FIRE4 or FIREMo in the OCO-2 inversion, in particular for most tropical regions during the 2015 El Niño year. Similarly, larger net CO2 sources are also derived with posterior fire emissions in the in situ data inversion for Tropical Asia. Evaluation with CO2 TCCON data shows lower biases with the three rebalanced priors than with the prior using CASA-GFED3. However, posteriors have average bias and scatter very close each other, making it difficult to conclude which simulation performs better than the other. We observe that the assimilated CO2 data have a strong influence on the global net fluxes among the different inversions. Inversions using OCO-2 (or IS) data have similar emissions, mostly as a result of the observational constraints and to a lesser extent because of the fire prior used. But results in the tropical regions suggest net flux sensitivity to the fire prior for both the IS and OCO-2 inversions. Further work is needed to improve prior fluxes in tropical regions where fires are a significant component. Finally, even if the inversions using the FIREMo prior did enhance the biases over some TCCON sites, it is not the case for the majority of TCCON sites. This study consequently pushes forward the development of a CO–CO2 joint inversion with multi-observations for a possible stronger constraint on posterior CO2 fire and biospheric emissions.

Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often used as a tracer of CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using MOPITT v8 data is performed to estimate fire emissions which are then converted in CO2 fire emissions through the use of emission ratio. These CO2 fire emissions allow us, then, to estimate adjusted CO2 Net Ecosystem Exchange (NEE) and respiration which are then used as priors for CO2 inversions constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Database Emissions (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 and GFED4.1s, as well as with an inversion using CASA-GFED3 fire and NEE priors. After comparison at the different scales, the inversions are evaluated against TCCON data. Results show that variations in posterior flux are much smaller across different prior mean fluxes when compared with the data assimilated. However, at global scale and for most of the regions, while the net fluxes remain robust, we can observe differences in fire emissions among the priors, resulting in large adjustments in the Net Ecosystem Exchange (NEE) to match the fires and observations. Tropical flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger CO2 net sources are observed when using GFED4.1s and MOPITT CO prior in CO2 OCO-2 inversions than compared with the other priors, particularly during the 2015 El Niño event for most Tropical regions. Larger CO2 net sources with MOPITT CO and GFED4.1s priors are also observed in Tropical Asia in CO2 in situ inversions than compared with the other priors during the 2015–2016 El Niño period and shows large net emissions than compared to OCO-2 inversions. Evaluation with TCCON suggests that the re-balanced posterior simulated give biases and accuracy very close each other where biases have decreased and variability matches better the validation data than with the CASA-GFED3. Further work is needed to improve prior fluxes in Tropical regions where fires are a significant component.