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We demonstrate the capability of the Sentinel-2 MultiSpectral Instrument (MSI) to detect and quantify anomalously large methane point sources with fine pixel resolution (20 m) and rapid revisit rates (2–5 d). We present three methane column retrieval methods that use shortwave infrared (SWIR) measurements from MSI spectral bands 11 (∼ 1560–1660 nm) and 12 (∼ 2090–2290 nm) to detect atmospheric methane plumes. The most successful is the multi-band–multi-pass (MBMP) method, which uses a combination of the two bands and a non-plume reference observation to retrieve methane columns. The MBMP method can quantify point sources down to about 3 t h−1 with a precision of ∼ 30 %–90 % (1σ) over favorable (quasi-homogeneous) surfaces. We applied our methods to perform high-frequency monitoring of strong methane point source plumes from a well-pad device in the Hassi Messaoud oil field of Algeria (October 2019 to August 2020, observed every 2.5 d) and from a compressor station in the Korpezhe oil and gas field of Turkmenistan (August 2015 to November 2020, observed every 5 d). The Algerian source was detected in 93 % of cloud-free scenes, with source rates ranging from 2.6 to 51.9 t h−1 (averaging 9.3 t h−1) until it was shut down by a flare lit in August 2020. The Turkmen source was detected in 40 % of cloud-free scenes, with variable intermittency and a 9-month shutdown period in March–December 2019 before it resumed; source rates ranged from 3.5 to 92.9 t h−1 (averaging 20.5 t h−1). Our source-rate retrievals for the Korpezhe point source are in close agreement with GHGSat-D satellite observations for February 2018 to January 2019, but provide much higher observation density. Our methods can be readily applied to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. High-frequency satellite-based detection of anomalous methane point sources as demonstrated here could enable prompt corrective action to help reduce global methane emissions.

Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosing an appropriate dataset for the intended application. In this study, 16 monthly AOD products obtained from different satellite sensors and with different algorithms were inter-compared and evaluated against Aerosol Robotic Network (AERONET) monthly AOD. Global and regional analyses indicate that products tend to agree qualitatively on the annual, seasonal and monthly timescales but may be offset in magnitude. Several approaches were then investigated to merge the AOD records from different satellites and create an optimised AOD dataset. With few exceptions, all merging approaches lead to similar results, indicating the robustness and stability of the merged AOD products. We introduce a gridded monthly AOD merged product for the period 1995–2017. We show that the quality of the merged product is as least as good as that of individual products. Optimal agreement of the AOD merged product with AERONET further demonstrates the advantage of merging multiple products. This merged dataset provides a long-term perspective on AOD changes over different regions of the world, and users are encouraged to use this dataset.

Air quality observations by satellite instruments are global and have a regular temporal resolution, which makes them very useful in studying long-term trends in atmospheric species. To monitor air quality trends in China for the period 2005-2015, we derive SO2 columns and NOx emissions on a provincial level with improved accuracy. To put these trends into perspective they are compared with public data on energy consumption and the environmental policies of China. We distinguish the effect of air quality regulations from economic growth by comparing them relatively to fossil fuel consumption. Pollutant levels, per unit of fossil fuel, are used to assess the effectiveness of air quality regulations. We note that the desulfurization regulations enforced in 2005-2006 only had a significant effect in the years 2008-2009, when a much stricter control of the actual use of the installations began. For national NOx emissions a distinct decreasing trend is only visible from 2012 onwards, but the emission peak year differs from province to province. Unlike SO2, emissions of NOx are highly related to traffic. Furthermore, regulations for NOx emissions are partly decided on a provincial level. The last 3 years show a reduction both in SO2 and NOx emissions per fossil fuel unit, since the authorities have implemented several new environmental regulations. Despite an increasing fossil fuel consumption and a growing transport sector, the effects of air quality policy in China are clearly visible. Without the air quality regulations the concentration of SO2 would be about 2.5 times higher and the NO2 concentrations would be at least 25% higher than they are today in China.

We report on updated trends using different merged datasets from satellite and ground-based observations for the period from 1979 to 2016. Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. Merged datasets used here include NASA MOD v8.6 and National Oceanic and Atmospheric Administration (NOAA) merge v8.6, both based on data from the series of Solar Backscatter UltraViolet (SBUV) and SBUV-2 satellite instruments (1978–present) as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone (GTO) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (1995-present), mainly comprising satellite data from GOME, the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and GOME-2A. The fifth dataset consists of the monthly mean zonal mean data from ground-based measurements collected at World Ozone and UV Data Center (WOUDC). The addition of four more years of data since the last World Meteorological Organization (WMO) ozone assessment (2013-2016) shows that for most datasets and regions the trends since the stratospheric halogen reached its maximum (approximately 1996 globally and approximately 2000 in polar regions) are mostly not significantly different from zero. However, for some latitudes, in particular the Southern Hemisphere extratropics and Northern Hemisphere subtropics, several datasets show small positive trends of slightly below +1 percent decade(exp. -1) that are barely statistically significant at the 2 Sigma uncertainty level. In the tropics, only two datasets show significant trends of +0.5 to +0.8 percent(exp.-1), while the others show near-zero trends. Positive trends since 2000 have been observed over Antarctica in September, but near-zero trends are found in October as well as in March over the Arctic. Uncertainties due to possible drifts between the datasets, from the merging procedure used to combine satellite datasets and related to the low sampling of ground-based data, are not accounted for in the trend analysis. Consequently, the retrieved trends can be only considered to be at the brink of becoming significant, but there are indications that we are about to emerge into the expected recovery phase. However, the recent trends are still considerably masked by the observed large year-to-year dynamical variability in total ozone.

Global satellite measurements of solar-induced fluorescence (SIF) from chlorophyll over land and ocean have proven useful for a number of different applications related to physiology, phenology, and productivity of plants and phytoplankton. Terrestrial chlorophyll fluorescence is emitted throughout the red and far-red spectrum, producing two broad peaks near 683 and 736nm. From ocean surfaces, phytoplankton fluorescence emissions are entirely from the red region (683nm peak). Studies using satellite-derived SIF over land have focused almost exclusively on measurements in the far red (wavelengths greater than 712nm), since those are the most easily obtained with existing instrumentation. Here, we examine new ways to use existing hyperspectral satellite data sets to retrieve red SIF (wavelengths less than 712nm) over both land and ocean. Red SIF is thought to provide complementary information to that from the far red for terrestrial vegetation. The satellite instruments that we use were designed to make atmospheric trace-gas measurements and are therefore not optimal for observing SIF; they have coarse spatial resolution and only moderate spectral resolution (0.5nm). Nevertheless, these instruments, the Global Ozone Monitoring Instrument 2 (GOME-2) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), offer a unique opportunity to compare red and far-red terrestrial SIF at regional spatial scales. Terrestrial SIF has been estimated with ground-, aircraft-, or satellite-based instruments by measuring the filling-in of atmospheric andor solar absorption spectral features by SIF. Our approach makes use of the oxygen (O2) gamma band that is not affected by SIF. The SIF-free O2 gamma band helps to estimate absorption within the spectrally variable O2 B band, which is filled in by red SIF. SIF also fills in the spectrally stable solar Fraunhofer lines (SFLs) at wavelengths both inside and just outside the O2 B band, which further helps to estimate red SIF emission. Our approach is then an extension of previous approaches applied to satellite data that utilized only the filling-in of SFLs by red SIF. We conducted retrievals of red SIF using an extensive database of simulated radiances covering a wide range of conditions. Our new algorithm produces good agreement between the simulated truth and retrievals and shows the potential of the O2 bands for noise reduction in red SIF retrievals as compared with approaches that rely solely on SFL filling. Biases seen with existing satellite data, most likely due to instrumental artifacts that vary in time, space, and with instrument, must be addressed in order to obtain reasonable results. Our 8-year record of red SIF observations over land with the GOME-2 allows for the first time reliable global mapping of monthly anomalies. These anomalies are shown to have similar spatiotemporal structure as those in the far red, particularly for drought-prone regions. There is a somewhat larger percentage response in the red as compared with the far red for these areas that are drought sensitive. We also demonstrate that good-quality ocean fluorescence line height retrievals can be achieved with GOME-2, SCIAMACHY, and similar instruments by utilizing the full complement of radiance measurements that span the red SIF emission feature.

We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of methane sources for 2019 at 2∘ × 2.5∘ horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals and to a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT and regional biases of 6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding 20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses an analytical solution to the Bayesian inference of methane sources, thus providing an explicit characterization of error statistics and information content together with the solution. TROPOMI has ∼ 100 times more observations than GOSAT, but error correlation on the 2∘ × 2.5∘ scale of the inversion and large spatial inhomogeneity in the number of observations make it less useful than GOSAT for quantifying emissions at that scale. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI and GOSAT inversions show consistent downward adjustments of global oil–gas emissions relative to a prior estimate based on national inventory reports to the United Nations Framework Convention on Climate Change but consistent increases in the south-central US and in Venezuela. Global emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We find large artifacts in the TROPOMI inversion over southeast China, where seasonal rice emissions are particularly high but in phase with extensive cloudiness and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the global scale.

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 µm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5.1 ppb (5.8 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.3 ppb (0.2 %). The natural XCO and XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R=0.97 for XCO and R=0.91 for XCH4 based on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector, which potentially allows for the advance of emission monitoring and air quality assessments to an entirely new level.

The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743–758 nm window. A secondary SIF dataset derived from an extended fitting window (735–758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3 nm windows devoid of atmospheric absorption within the 665–785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier (DOI): https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104 (Guanter et al., 2021).

We present an updated version of the Global Fuel Exploitation Inventory (GFEI) for methane emissions and evaluate it with results from global inversions of atmospheric methane observations from satellite (GOSAT) and in situ platforms (GLOBALVIEWplus). GFEI allocates methane emissions from oil, gas, and coal sectors and subsectors to a 0.1∘ × 0.1∘ grid by using the national emissions reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) and mapping them to infrastructure locations. Our updated GFEI v2 gives annual emissions for 2010–2019 that incorporate the most recent UNFCCC national reports, new oil–gas well locations, and improved spatial distribution of emissions for Canada, Mexico, and China. Russia's oil–gas emissions in its latest UNFCCC report (4.1 Tg a−1 for 2019) decrease by 83 % compared to its previous report while Nigeria's latest reported oil–gas emissions (3.1 Tg a−1 for 2016) increase 7-fold compared to its previous report, reflecting changes in assumed emission factors. Global gas emissions in GFEI v2 show little net change from 2010 to 2019 while oil emissions decrease and coal emissions slightly increase. Global emissions from the oil, gas, and coal sectors in GFEI v2 (26, 22, and 33 Tg a−1, respectively in 2019) are lower than the EDGAR v6 inventory (32, 44, and 37 Tg a−1 in 2018) and lower than the IEA inventory for oil and gas (38 and 43 Tg a−1 in 2019), though there is considerable variability between inventories for individual countries. GFEI v2 estimates higher emissions by country than the Climate TRACE inventory, with notable exceptions in Russia, the US, and the Middle East where TRACE is up to an order of magnitude higher than GFEI v2. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but find that Nigeria's reported UNFCCC emissions are too high. Oil–gas emissions are generally underestimated by the national inventories for the highest emitting countries including the US, Venezuela, Uzbekistan, Canada, and Turkmenistan. Offshore emissions tend to be overestimated. Our updated GFEI v2 provides a platform for future evaluation of national emission inventories reported to the UNFCCC using the newer generation of satellite instruments such as TROPOMI with improved coverage and spatial resolution. This increased observational data density will be especially beneficial in regions where current inversion systems have limited sensitivity including Russia. Our work responds to recent aspirations of the Intergovernmental Panel on Climate Change (IPCC) to integrate top-down and bottom-up information into the construction of national emission inventories.

The Global Space-based Inter-Calibration System (GSICS) routinely monitors the calibration of various channels of Earth-observing satellite instruments and generates GSICS Corrections, which are functions that can be applied to tie them to reference instruments. For the infrared channels of geostationary imagers GSICS algorithms are based on comparisons of collocated observations with hyperspectral reference instruments; whereas Pseudo Invariant Calibration Targets are currently used to compare the counterpart channels in the reflected solar band to multispectral reference sensors. This paper discusses how GSICS products derived from both approaches can be tied to an absolute scale using specialized satellite reference instruments with SI-traceable calibration on orbit. This would provide resilience against gaps between reference instruments and drifts in their calibration outside their overlap period and allow construction of robust and harmonized data records from multiple satellite sources to build Fundamental Climate Data Records, as well as more uniform environmental retrievals in both space and time, thus improving inter-operability.