Special Issue "Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Dr. Prabir K. Patra
E-Mail Website
Guest Editor
Japan Agency for Marine-Earth Science and Technology (JAMSTEC); 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Interests: greenhouse gases and air pollution research
Dr. David Crisp
E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, MS 233-200, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: Orbiting Carbon Observatory–2 (OCO-2) Science Team Leader
Dr. Thomas Lauvaux
E-Mail Website
Guest Editor
Department of Meteorology and Atmospheric Science, Pennsylvania State University, 415 Walker Building, University Park, PA 16802, USA
Interests: Carbon cycle science, Atmospheric inversion, Data assimilation

Special Issue Information

Dear Colleagues,

Carbon dioxide (CO2) and methane (CH4) are the two most important greenhouse gases that have led to a significant fraction of the increase in earth’s surface temperature in the past 100 years. Global average concentrations of CO2 and CH4 have increased from 296 ppm (parts per million) and 900 ppb (parts per billion) in 1900 to about 405 ppm and 1850 ppb in 2017, respectively. Studies have shown that these increases in concentration are due to the increase in anthropogenic activities on the earth’s surface, leading to higher emissions. However, there has not yet been a clear attribution of the processes involved in this increase of emissions and the role of other environmental factors, mainly due to the lack of observational data coverage. To alleviate the sparseness in observations, satellite remote sensing has become a major focus in the past couple of decades for monitoring greenhouse gases from space. The first dedicated mission for greenhouse gas monitoring was launched by JAXA in 2009, the Greenhouse Gases Observation Satellite (GOSAT). Large amount of observations have also been gathered by SCIAMACHY and AIRS, and a growing number of satellites are now in space in order to make more precise measurements, namely the OCO-2 since 2014, TROPOMI since 2017, TanSAT since 2016, etc. This Special Issue is dedicated to the past progress and new developments in satellite remote sensing of long-lived greenhouse gases, with a focus on CO and CH4.

Dr. Prabir K. Patra
Dr. David Crisp
Dr. Thomas Lauvaux
Guest Editors

Manuscript Submission Information

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Keywords

  • Carbon dioxide
  • Methane
  • Remote sensing
  • Greenhouse gases
  • Earth’s atmosphere

Published Papers (8 papers)

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Research

Open AccessArticle
Sensitivity of Optimal Estimation Satellite Retrievals to Misspecification of the Prior Mean and Covariance, with Application to OCO-2 Retrievals
Remote Sens. 2019, 11(23), 2770; https://doi.org/10.3390/rs11232770 - 25 Nov 2019
Abstract
Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the [...] Read more.
Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the state vector. In many remote sensing applications, the true priors are multivariate and hard to characterize properly. Instead, priors are often constructed based on subject-matter expertise, existing empirical knowledge, and a need for computational expediency, resulting in a “working prior.” This paper explores the retrieval bias and the inaccuracy in retrieval uncertainty caused by explicitly separating the true prior (the probability distribution of the underlying state) from the working prior (the probability distribution used within the OE algorithm), with an application to Orbiting Carbon Observatory-2 (OCO-2) retrievals. We find that, in general, misspecifying the mean in the working prior will lead to biased retrievals, and misspecifying the covariance in the working prior will lead to inaccurate estimates of the retrieval uncertainty, though their effects vary depending on the state-space signal-to-noise ratio of the observing instrument. Our results point towards some attractive properties of a class of uninformative priors that is implicit for least-squares retrievals. Furthermore, our derivations provide a theoretical basis, and an understanding of the trade-offs involved, for the practice of inflating a working-prior covariance in order to reduce the prior’s impact on a retrieval (e.g., for OCO-2 retrievals). Finally, our results also lead to practical recommendations for specifying the prior mean and the prior covariance in OE. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
Remote Sens. 2019, 11(21), 2489; https://doi.org/10.3390/rs11212489 - 24 Oct 2019
Abstract
We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric [...] Read more.
We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH4 yr−1 for Russia, 12.8 to 14.3 Tg CH4 yr−1 for temperate South America, and 43.2 to 44.9 Tg CH4 yr−1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH4 yr−1 for China, 26.2 to 25.5 Tg CH4 yr−1 for Europe, and by 12.4 Tg CH4 yr−1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2
Remote Sens. 2019, 11(17), 2061; https://doi.org/10.3390/rs11172061 - 02 Sep 2019
Abstract
Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues [...] Read more.
Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO2 Emissions from Space
Remote Sens. 2019, 11(13), 1608; https://doi.org/10.3390/rs11131608 - 06 Jul 2019
Cited by 1
Abstract
The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based [...] Read more.
The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO2 emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO 2 monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO 2 emission monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
A Theoretical Analysis for Improving Aerosol-Induced CO2 Retrieval Uncertainties Over Land Based on TanSat Nadir Observations Under Clear Sky Conditions
Remote Sens. 2019, 11(9), 1061; https://doi.org/10.3390/rs11091061 - 05 May 2019
Abstract
Aerosols significantly affect carbon dioxide (CO2) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring [...] Read more.
Aerosols significantly affect carbon dioxide (CO2) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite, TanSat) contain aerosol information for correction of scattering effects in the retrieval. Herein, a new approach is proposed for optimizing the aerosol model used in the TanSat CO2 retrieval algorithm to reduce CO2 uncertainties associated with aerosols. The weighting functions of hyperspectral observations with respect to elements in the state vector are simulated by a forward radiative transfer model. Using the optimal estimation method (OEM), the information content and each component of the CO2 column-averaged dry-air mole fraction (XCO2) retrieval errors from the TanSat simulations are calculated for typical aerosols which are described by Aerosol Robotic Network (AERONET) inversion products at selected sites based on the a priori and measurement assumptions. The results indicate that the size distribution parameters (reff, veff), real refractive index coefficient of fine mode (arf) and fine mode fraction (fmf) dominate the interference errors, with each causing 0.2–0.8 ppm of XCO2 errors. Given that only 4–7 degrees of freedom for signal (DFS) of aerosols can be obtained simultaneously and CO2 information decreases as more aerosol parameters are retrieved, four to seven aerosol parameters are suggested as the most appropriate for inclusion in CO2 retrieval. Focusing on only aerosol-induced XCO2 errors, forward model parameter errors, rather than interference errors, are dominant. A comparison of these errors across different aerosol parameter combination groups reveals that fewer aerosol-induced XCO2 errors are found when retrieving seven aerosol parameters. Therefore, the model selected as the optimal aerosol model includes aerosol optical depth (AOD), peak height of aerosol profile (Hp), width of aerosol profile (Hw), effective variance of fine mode aerosol (vefff), effective radius of coarse mode aerosol (reffc), coefficient a of the real part of the refractive index for the fine mode and coarse mode (arf and arc), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, Hp, Hw, reffc and arc) are recommended for the low aerosol information. This optimal aerosol model therefore offers a theoretical foundation for improving CO2 retrieval precision from real TanSat observations in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
Increase of Atmospheric Methane Observed from Space-Borne and Ground-Based Measurements
Remote Sens. 2019, 11(8), 964; https://doi.org/10.3390/rs11080964 - 23 Apr 2019
Cited by 1
Abstract
It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. [...] Read more.
It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH4 trends from three different measurements: the mid–upper tropospheric CH4 (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH4 in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH4 in the atmosphere (XCH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH4 trends in the mid–upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH4 increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH4 in the MUT, as observed by AIRS, are overall smaller than CH4 in MBL and the column-average CH4. During 2009–2011, there was a dip in the increase rate for CH4 in MBL, and the MUT-CH4 increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH4 also reached the minimum during 2009–2011 accordingly, suggesting that the trends of CH4 are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH4 increase rates in the mid–high northern latitude regions is larger than those in the tropics and southern hemisphere. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals
Remote Sens. 2019, 11(7), 882; https://doi.org/10.3390/rs11070882 - 11 Apr 2019
Abstract
Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (XCH 4). To understand [...] Read more.
Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (XCH 4). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4. These differences are linked to the regional CH 4 sources and sinks, and call for further research. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Open AccessArticle
Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2
Remote Sens. 2019, 11(7), 850; https://doi.org/10.3390/rs11070850 - 09 Apr 2019
Abstract
NASA’s carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015–2018) of global (60°S–60°N) XCO2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO [...] Read more.
NASA’s carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015–2018) of global (60°S–60°N) XCO2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO2 dry air mole fraction, XCO2, are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO2 enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO2 enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO2 anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO2 anomalies collocated with the recent TROPOspheric Monitoring Instrument NO2 observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO2 signatures on global scale. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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