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In recent years, solar-induced chlorophyll fluorescence (SIF) retrieved from space borne spectrometers has been extensively used as a proxy for terrestrial photosynthesis at relatively sparse temporal and spatial scales. The near-infrared band of the recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF in a spectral range devoid of atmospheric absorption features. We find that initial TROPOMI spectra meet high expectations for a substantially improved spatio-temporal resolution (up to 7 km × 3.5 km pixels with daily revisit), representing a step change in SIF remote sensing capabilities. However, interpretation requires caution, as the broad range of viewing-illumination geometries covered by TROPOMI's 2600 km wide swath needs to be taken into account. A first inter-sensor comparison with OCO-2 (Orbiting Carbon Observatory-2) SIF shows excellent agreement, underscoring the high quality of TROPOMI's SIF retrievals and the notable radiometric performance of the instrument.Plain language summaryPhotosynthesis is the most essential process for life on Earth, but gradually changing environmental conditions such as increasing concentrations of atmospheric trace gases, rising temperatures or reduced water availability could adversely affect the photosynthetic productivity. The recently launched TROPOspheric Monitoring Instrument (TROPOMI) is designed to monitor atmospheric trace gases and air pollutants with an unprecedented resolution in space and time, while its radiometric performance also permits us to see a weak electromagnetic signal emitted by photosynthetically active vegetation - solar induced chlorophyll fluorescence (SIF). Mounting evidence suggests that SIF observations from satellite instruments augment our abilities to track the photosynthetic performance and carbon uptake of terrestrial vegetation. In this study, we present the first TROPOMI SIF retrievals, largely outperforming previous and existing capabilities for a spatial continuous monitoring of SIF from space.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019-2020 summer season.sup.1,2. The fires released substantial amounts of carbon dioxide into the atmosphere.sup.3. However, existing emission estimates based on fire inventories are uncertain.sup.4, and vary by up to a factor of four for this event. Here we constrain emission estimates with the help of satellite observations of carbon monoxide.sup.5, an analytical Bayesian inversion.sup.6 and observed ratios between emitted carbon dioxide and carbon monoxide.sup.7. We estimate emissions of carbon dioxide to be 715 teragrams (range 517-867) from November 2019 to January 2020. This is more than twice the estimate derived by five different fire inventories.sup.8-12, and broadly consistent with estimates based on a bottom-up bootstrap analysis of this fire episode.sup.13. Although fires occur regularly in the savannas in northern Australia, the recent episodes were extremely large in scale and intensity, burning unusually large areas of eucalyptus forest in the southeast.sup.13. The fires were driven partly by climate change.sup.14,15, making better-constrained emission estimates particularly important. This is because the build-up of atmospheric carbon dioxide may become increasingly dependent on fire-driven climate-carbon feedbacks, as highlighted by this event.sup.16. The amount of carbon dioxide released by the Australian wildfires of 2019-2020 is uncertain, but is estimated here using satellite observations of carbon monoxide to be more than twice the amount suggested by fire inventories., Author(s): Ivar R. van der Velde [sup.1] [sup.2] , Guido R. van der Werf [sup.2] , Sander Houweling [sup.1] [sup.2] , Joannes D. Maasakkers [sup.1] , Tobias Borsdorff [sup.1] , [...]

The Greenhouse Gas Monitoring Instrument (GMI) onboard the Chinese hyperspectral satellite GF5–02 can provide abundant observations of global atmospheric CO2, which plays an important role in climate research. CO2 retrieval precision is the key to determining the application value of the GMI. To reduce the influence of atmospheric scattering on retrieval, we combined the Directional Polarimetric Camera (DPC) data on the same satellite to improve the anti-interference ability of GMI CO2 retrieval and ensure its retrieval precision. To realize the reliability and feasibility of the collaborative use of the GMI and DPC, this paper designs the pointing registration method of the GMI based on coastline observations, the spatial resolution matching method and the collaborative cloud screening method of the GMI and DPC observations. Combined with the DPC, which supplied the spectral data and aerosol product, the retrieval ability of the coupled bidirectional reflectance distribution function CO2 retrieval (CBCR) method developed for GMI CO2 retrieval was improved, with the retrieval efficiency of CO2 products increasing by 27%, and the CO2 retrieval precision increasing from 3.3 ppm to 2.7 ppm. Moreover, collaborative use not only guaranteed the GMI's ability to detect global and area CO2 concentration distribution characteristics, such as significant concentration differences between the Northern and Southern Hemispheres in winter and high CO2 concentrations in urban agglomeration areas caused by human activities, but also extended the GMI's potential for monitoring anomalous events, such as the Tonga volcanic eruption. [ABSTRACT FROM AUTHOR]

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S-5P) satellite, launched in 2017, measures the total column concentration of the trace gas Carbon Monoxide (CO) daily on a global scale and at a high spatial resolution of 7×7 km², improved to 5.5×7 km² in August 2019. The TROPOMI observations show plumes of CO due to localized CO emissions from industrial sources and biomass burning. In this paper, we quantify these CO emissions for biomass burning by an automated algorithm, APE, to detect plumes and quantify the CO emission rate using cross-sectional flux method. Furthermore, the influence of a constant and a varying plume height in downwind direction on emissions is investigated and algorithm uncertainties are quantified. The VIIRS active fire data in conjunction with the TROPOMI CO datasets is used to identify fires and the fire locations. Then, an automated plume detection algorithm using traditional image processing algorithms is developed and utilized to identify plumes. For these plumes, the emission rate is estimated by the cross10 section flux method at three different plume heights. The first two are constant plume heights at a 100 m and an IS4FIRES injection height from Global Fire Assimilation System. And the last one is a varying plume height in downwind direction. A 3D Lagrangian model is used to simulate tracer particles where the source locations for the simulation are based on the VIIRS fire counts and IS4FIRES injection height. 3D velocities at 137 model levels (ERA5) are utilized to simulate tracer particles. We demonstrate the quality and validity of our automated approach by investigating biomass burning events and their emissions for Australia on Oct 2019 and the US on Sept 2020. A total of 110 and 31 individual fire plumes in Australia and the US, respectively were detected and their emissions estimated. The emissions were severely under-predicted and negative for 11 cases when based on constant plume height of 100 m compared to emissions based on varying plume height. Furthermore, the effect of the changing plume height in downwind direction on the emission estimate compared to emissions from constant IS4FIRES plume height was minor as 124 cases are found to have emission variation less than 10%. However, we were able to identify several cases where the flux estimates become more reliable with varying plume height. Thus, the varying plume height in downwind direction is considered for the automated algorithm. The cross-section flux method is found to have an uncertainty of 38% in one of the idealized cases. However, overall uncertainty of the algorithm is difficult to quantify as conditions for each fire are unique. [ABSTRACT FROM AUTHOR]

Since 2017 the Tropospheric Monitoring Instrument (TROPOMI) onboard ESA's Copernicus Sentinel-5 satellite S5-P) has provided the operational Carbon Monoxide (CO) data product with daily global coverage on a spatial resolution of 5.5 × 7 km² (7× 7 km² before August 2019). The European Centre for Medium-Range Weather Forecasts (ECMWF) plans assimilate the retrieved total columns and the corresponding vertical sensitivities in the Copernicus Atmosphere Monitoring Service Integrated Forecasting System (CAMS-IFS) to improve forecasts of the atmospheric chemical composition. The TROPOMI data will primarily constrain the vertical integrated CO field (VCD) of CAMS-IFS but to a lesser extent also its vertical CO distribution as well. For clear-sky conditions, the vertical sensitivity of the TROPOMI CO data product is useful throughout the atmosphere but for cloudy scenes it varies due to cloud shielding and light scattering. To assess the profile information, we deploy a posteriori profile retrieval that combines individual TROPOMI CO column retrievals with different vertical sensitivities to obtain a vertical CO profile that is then a representative average for the chosen spatial and temporal domain. We demonstrate the approach on three CO pollution cases. For the so called "Rabbit Foot Fire" in Idaho on the 12 August 2018, we estimate a CO profile showing the pollution at an altitude of about 5 km in good agreement with airborne in-situ measurements of the Biomass Burning fluxes of trace gases and aerosols (BB-FLUX) field campaign. The distinct CO enhancement in a plume aloft, decoupled from the ground, is sensed by TROPOMI but is not present in the CAMS-IFS model. For a large-scale event, we analyzed the CO pollution from Siberian wildfires that took place from 14 to 18 August 2018. The TROPOMI data is estimating the height of the pollution plume over Canada at 7 km in agreement with CAMS-IFS. However, CAMS-IFS underestimates the enhanced CO vertical column densities sensed by TROPOMI within the plume by more than 100 ppb. Finally, we study the seasonal biomass burning in the Amazon. During the burning season (1 - 15 August 2019) the CO profile retrieved from the TROPOMI measurements agrees well with the one of CAMS-IFS with a similar vertical shape between ground and 14 km altitude. Hence, our results indicate that assimilating TROPOMI CO retrieval with different vertical sensitivities e.g., under clear-sky and cloudy conditions provide information about the vertical distribution of CO. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of methane (CH4) using the TROPOMI instrument is key to monitor and quantify emissions globally. In the past years, analysis of TROPOMI methane data has pointed to few false methane anomalies that can potentially be misinterpreted as enhancements due to strong emission sources. These artefacts are caused by spectral features of the underlying surfaces, which are not well represented in the forward model. Surface reflectance spectral dependence in the fullphysics RemoTeC retrieval algorithm is modelled using a second order polynomial in wavelength. We show in this study that a third order polynomial better represents the surface reflectance dependency with wavelength of specific surface materials (e.g., rock), resulting in an improved characterization of the spectral features that caused the artificial localized XCH4 enhancements found in several locations like e.g., Siberia, Australia, and Algeria. The use of a third order polynomial removed these artificial XCH4 enhancements and significantly improved the fit over these specific features, while outside of these areas globally the fit did not improve in most cases. This reflects that a second order polynomial is optimal to capture the spectral dependencies of most surfaces given the characteristic of the TROPOMI instrument, but a third order polynomial is needed for the specific spectral characteristics of several surfaces. Furthermore, increasing the order of the polynomial to higher degrees did not further improve the retrieval. We also found that the known bias in retrieved methane for low albedo measurements slightly improves, but still a posterior correction needs to be applied, leaving open the question about the root cause of the albedo bias. After applying the third order polynomial globally, we perform the routine validation with TCCON and GOSAT. GOSAT comparison does not significantly improve, while TCCON validation results show an overall improvement of 2-4 ppb, reflecting that TCCON stations are not close to any of the corrected artefacts and are typically located around spectrally smooth surfaces. [ABSTRACT FROM AUTHOR]