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Special Issue "Remote Sensing of Greenhouse Gas Emissions"

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

Deadline for manuscript submissions: 1 May 2023 | Viewed by 4966

Special Issue Editors

Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Interests: carbon cycle; remote sensing of greenhouse gases; atmospheric greenhouse gas transport; atmospheric inversion; atmospheric methane; greenhouse gas fluxes
Dr. Tuula Aalto
E-Mail Website
Guest Editor
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Interests: atmospheric carbon dioxide and methane concentrations and ecosystem fluxes; greenhouse gas modeling
Department of Physical Geography and Ecosystem Science, Lunds Universitet, Box 188, 221 00 Lund, Sweden
Interests: global carbon cycle; land-climate interactions; terrestrial ecosystem modelling; atmospheric transport modelling; inverse modelling; model-data fusion

Special Issue Information

Dear Colleagues,

The remote sensing of atmospheric greenhouse gases (GHGs) and Earth’s surface provides possibilities for quantifying GHG fluxes, as well as their regional and global budgets, trends, spatial distributions, and seasonality. Observations of GHGs and other atmospheric tracers enable the quantification and evaluation of these compounds, originating from both anthropogenic and natural processes, and inform atmospheric chemistry. Remote sensing observations of vegetation activities and hydrological and cryospheric status on land, such as vegetation type, greenness, leaf area, precipitation, inundation, soil moisture, and snow and ice, provide valuable information about ecosystem states. Current developments also reveal emissions due to human activities at high resolutions, identifying point sources. The assimilation of Earth Observation (EO) data into models opens possibilities for novel modelling approaches and avenues for reducing uncertainties in GHG flux estimates.

This Special Issue invites contributions that present remote sensing applications providing means for GHG flux quantifications, including but not limited to GHG sources and sinks inferred from satellites’ GHG and EO data, utilization of those data in process-based land ecosystems modelling and atmospheric inverse modelling, variations in the atmospheric abundance of carbon gases, and the application of multiple tracers from satellite platforms.

Dr. Aki Tsuruta
Dr. Tuula Aalto
Dr. Marko Scholze
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Earth’s carbon cycle
  • Greenhouse gas flux quantification
  • Atmospheric greenhouse gases
  • Ecosystem fluxes
  • Land–climate interaction
  • Earth observations
  • Data assimilation
  • Atmospheric inversion
  • Terrestrial ecosystem modelling

Published Papers (5 papers)

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Research

Article
High Resolution Fourier Transform Spectrometer for Ground-Based Verification of Greenhouse Gases Satellites
Remote Sens. 2023, 15(6), 1671; https://doi.org/10.3390/rs15061671 - 20 Mar 2023
Viewed by 250
Abstract
Satellite remote sensing is currently the best monitoring means to obtain global carbon source and sink data. The United States, Japan, China and other countries are vigorously developing spaceborne detection technology. However, the important factors that restrict the application of greenhouse gas satellite [...] Read more.
Satellite remote sensing is currently the best monitoring means to obtain global carbon source and sink data. The United States, Japan, China and other countries are vigorously developing spaceborne detection technology. However, the important factors that restrict the application of greenhouse gas satellite remote sensing technology include the limited accuracy of data products. How to improve the retrieval level of greenhouse gas payloads is a problem that needs to be solved urgently. One effective way to improve data quality is to carry out satellite ground synchronous authenticity verification and system error correction. This paper mainly aims at the shortcomings of the existing TCCON and the portable verification equipment EM27/SUN, and develops a High-Resolution Fourier Transform Spectrometer (HRFTS) based on dynamic collimation technology. Through the gas absorption method and the band scanning method of the hyperspectral monochromatic light source, the instrument’s absorption spectrum measurement capability and the Instrument Line Shape (ILS) are demonstrated. The instrument’s spectral resolution is consistent with the on-orbit greenhouse gas satellite load, reaching 0.26 cm−1. For the interference data obtained by the spectrometer, spectral restoration processing, data quality control and inversion algorithm optimization were carried out to solve the problems of baseline correction, spectral fine registration, and environmental parameter profile reconstruction, and cross comparison experiments with EM27/SUN were carried out simultaneously. Finally, for the gases monitoring instrument (GMI) of the GF5-02 satellite launched on 7 September 2021, the first satellite ground synchronization verification experiment with high space-time matching was carried out. The results showed that the CO2 column concentration deviation of the satellite ground synchronization inversion was about 1.5 ppm, and the CH4 column concentration deviation was about 11.3 ppb, which verified the on-orbit detection accuracy of the GMI, and laid a foundation for the subsequent satellite inversion algorithm optimization and systematic error correction. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions)
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Article
CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes
Remote Sens. 2023, 15(6), 1620; https://doi.org/10.3390/rs15061620 - 16 Mar 2023
Viewed by 320
Abstract
Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air [...] Read more.
Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions)
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Article
Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2
Remote Sens. 2022, 14(19), 5024; https://doi.org/10.3390/rs14195024 - 09 Oct 2022
Viewed by 801
Abstract
Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In [...] Read more.
Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions)
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Article
Spatiotemporal Characteristics and Heterogeneity of Vegetation Phenology in the Yangtze River Delta
Remote Sens. 2022, 14(13), 2984; https://doi.org/10.3390/rs14132984 - 22 Jun 2022
Cited by 2 | Viewed by 894
Abstract
Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other [...] Read more.
Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions)
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Article
Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes
Remote Sens. 2021, 13(24), 5059; https://doi.org/10.3390/rs13245059 - 13 Dec 2021
Cited by 2 | Viewed by 1694
Abstract
The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the methane fluxes and emissions in northern latitudes but when atmospheric observations are sparse, the models are only as good as their a priori estimates. Thus, [...] Read more.
The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the methane fluxes and emissions in northern latitudes but when atmospheric observations are sparse, the models are only as good as their a priori estimates. Thus, improving a priori estimates is a competent way to reduce uncertainties and enhance emission estimates in the sparsely sampled regions. Here, we use a novel way to integrate remote sensing soil freeze/thaw (F/T) status from SMOS satellite to better capture the seasonality of methane emissions in the northern high latitude. The SMOS F/T data provide daily information of soil freezing state in the northern latitudes, and in this study, the data is used to define the cold season in the high latitudes and, thus, improve our knowledge of the seasonal cycle of biospheric methane fluxes. The SMOS F/T data is implemented to LPX-Bern DYPTOP model estimates and the modified fluxes are used as a biospheric a priori in the inversion model CarbonTracker Europe-CH4. The implementation of the SMOS F/T soil state is shown to be beneficial in improving the inversion model’s cold season biospheric flux estimates. Our results show that cold season biospheric CH4 emissions in northern high latitudes are approximately 0.60 Tg lower than previously estimated, which corresponds to 17% reduction in the cold season biospheric emissions. This reduction is partly compensated by increased anthropogenic emissions in the same area (0.23 Tg), and the results also indicates that the anthropogenic emissions could have even larger contribution in cold season than estimated here. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions)
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