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Remote Sensing of Greenhouse Gases and Air Pollution
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Remote Sensing of Greenhouse Gases and Air Pollution

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 37481

Special Issue Editors

Dr. Jane Liu

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Guest Editor
Department of Geography & Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
Interests: remote sensing of the atmosphere and land; atmospheric environment; atmosphere-biosphere interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Liangfu Chen

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Guest Editor
Chinese Academy of Sciences, China
Interests: remote sensing of atmospheric environment; satellite observation of aerosol, clouds and trace gases; remote sensing of global climate change
Prof. Dr. Weimin Ju

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Guest Editor
International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
Interests: remote sensing of ecological environment; data assimilation of remote sensing model; carbon - water coupling cycle simulation and climate change impact assessment for ecosystems
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaozhen Xiong

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Guest Editor
ESSIC, University of Maryland, USA
Interests: remote sensing of atmospheric compositions; validation of satellite products and their applications for air quality and climate change study
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fred Moshary

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Optical Remote Sensing Laboratory, Electrical Engineering, Grove School of Engineering, CUNY City College, New York, NY 10031, USA
Interests: remote sensing techniques; technologies and applications; optical sensors; sensor networks for urban and regional micro-meteorology/micro-cliamte research; atmospheric and ocean remote and insitu sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Continuous increases in human population and human activities have resulted in remarkable changes in the compositions of the atmosphere since the industrial revolution. Climate change and air pollution are two major consequences of such changes. The scientific understanding of these two issues requires a variety of observations of the atmosphere in different platforms. Among them, satellite remote sensing has added a new dimension to these observations because of its advantages in global coverage, frequent revisit time, and consistently improved quality in recent decades. Particularly, remote sensing of greenhouse gases has already illustrated promising applications related to climate change studies. Remote sensing data are being more and more widely used in the monitoring of air pollution, which helps to identify variations of air pollutants in space and time and untangle the underlying mechanisms responsible for these variations. This Special Issue “Remote Sensing of Greenhouse Gases and Air Pollution” invites contributions on recent advances in remote sensing of greenhouse gases (i.e., CO2, CH4, N2O, H2O, and tropospheric O3), polluted gases and particular matters (i.e., tropospheric O3, CO, SO2, NO2, and aerosols), as well as the applications of these remote sensing data for climate change and air pollution studies.

Dr. Jane Liu
Dr. Liangfu Chen
Dr. Weimin Ju
Dr. Xiaozhen Xiong
Prof. Fred Moshary
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 2700 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.

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Editorial

Jump to: Research

3 pages, 161 KiB  
Editorial
Special Issue “Remote Sensing of Greenhouse Gases and Air Pollution”
by Xiaozhen Xiong, Jane Liu, Liangfu Chen, Weimin Ju and Fred Moshary
Remote Sens. 2021, 13(11), 2057; https://doi.org/10.3390/rs13112057 - 23 May 2021
Cited by 3 | Viewed by 2297
Abstract
Continuous increases in the human population and human activities have resulted in remarkable changes in the composition of the atmosphere since the industrial revolution [...] Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)

Research

Jump to: Editorial

20 pages, 6809 KiB  
Article
Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia
by Farhan Mustafa, Lingbing Bu, Qin Wang, Md. Arfan Ali, Muhammad Bilal, Muhammad Shahzaman and Zhongfeng Qiu
Remote Sens. 2020, 12(15), 2498; https://doi.org/10.3390/rs12152498 - 4 Aug 2020
Cited by 31 | Viewed by 7544
Abstract
Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, [...] Read more.
Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, the column-averaged CO2 dry air mole fraction (XCO2) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO2 from GOSAT is higher than the XCO2 simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO2 is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO2 can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Regional division of Asia.</p> Full article ">Figure 2
<p>(<b>a</b>) Distribution of 10 years mean XCO<sub>2</sub> over Asia for (<b>a</b>) CarbonTracker (CT) 2019; (<b>b</b>) Greenhouse Gases Observing Satellite (GOSAT); (<b>c</b>) their differences (CT-GOSAT); and (<b>d</b>) the total number of datasets from GOSAT in each grid.</p> Full article ">Figure 3
<p>Distribution of 5 years mean XCO<sub>2</sub> over Asia (<b>a</b>) from CT; (<b>b</b>) Orbiting Carbon Observatory 2 (OCO-2); (<b>c</b>) their differences (CT-OCO2); (<b>d</b>) and the total number of datasets from OCO-2 in each grid.</p> Full article ">Figure 4
<p>The scatter density plot between the XCO<sub>2</sub> derived from (<b>a</b>) the CT and GOSAT; (<b>b</b>) and the CT and OCO-2.</p> Full article ">Figure 5
<p>Spatial distributions of correlations between (<b>a</b>) the CarbonTracker and GOSAT; (<b>b</b>) the CarbonTracker and OCO-2; and mean posteriori estimate of XCO<sub>2</sub> uncertainty in (<b>c</b>) GOSAT; and (<b>d</b>) OCO-2.</p> Full article ">Figure 6
<p>The time-series variations of monthly averaged XCO<sub>2</sub> derived from CT and the two satellite datasets for a period of 5 years ranging from September 2014 to August 2019 over (<b>a</b>) Asia; (<b>b</b>) Central Asia; (<b>c</b>) East Asia; (<b>d</b>) South Asia; (<b>e</b>) Southeast Asia; (<b>f</b>) and Western Asia, The gaps in the graph show the missing data.</p> Full article ">Figure 7
<p>The annual growth rate of XCO<sub>2</sub> concentration for the CT and the satellite datasets over (<b>a</b>) Asia; (<b>b</b>) Central Asia; (<b>c</b>) East Asia; (<b>d</b>) South Asia; (<b>e</b>) Southeast Asia; and (<b>f</b>) Western Asia</p> Full article ">Figure 8
<p>Seasonal distribution of XCO<sub>2</sub> from CT (<b>a</b>), GOSAT (<b>b</b>), and their differences (CT-GOSAT) (<b>c</b>).</p> Full article ">Figure 9
<p>Seasonal distribution of XCO<sub>2</sub> from the CT (left panel), OCO-2 (middle panel), and their differences (CT-OCO2) (right panel).</p> Full article ">
22 pages, 2896 KiB  
Article
Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations
by Ralf Sussmann and Markus Rettinger
Remote Sens. 2020, 12(15), 2387; https://doi.org/10.3390/rs12152387 - 24 Jul 2020
Cited by 19 | Viewed by 9169
Abstract
The COVID-19 pandemic is causing projected annual CO2 emission reductions up to −8% for 2020. This approximately matches the reductions required year on year to fulfill the Paris agreement. We pursue the question whether related atmospheric concentration changes may be detected by [...] Read more.
The COVID-19 pandemic is causing projected annual CO2 emission reductions up to −8% for 2020. This approximately matches the reductions required year on year to fulfill the Paris agreement. We pursue the question whether related atmospheric concentration changes may be detected by the Total Carbon Column Observing Network (TCCON), and brought into agreement with bottom-up emission-reduction estimates. We present a mathematical framework to derive annual growth rates from observed column-averaged carbon dioxide (XCO2) including uncertainties. The min–max range of TCCON growth rates for 2012–2019 was [2.00, 3.27] ppm/yr with a largest one-year increase of 1.07 ppm/yr for 2015/16 caused by El Niño. Uncertainties are 0.38 [0.28, 0.44] ppm/yr limited by synoptic variability, including a 0.05 ppm/yr contribution from single-measurement precision. TCCON growth rates are linked to a UK Met Office forecast of a COVID-19-related reduction of −0.32 ppm yr−2 in 2020 for Mauna Loa. The separation of TCCON-measured growth rates vs. the reference forecast (without COVID-19) is discussed in terms of detection delay. A 0.6 [0.4, 0.7]-yr delay is caused by the impact of synoptic variability on XCO2, including a ≈1-month contribution from single-measurement precision. A hindrance for the detection of the COVID-19-related growth rate reduction in 2020 is the ±0.57 ppm/yr uncertainty for the forecasted reference case (without COVID-19). Only assuming the ongoing growth rate reductions increasing year-on-year by −0.32 ppm yr−2 would allow a discrimination of TCCON measurements vs. the unperturbed forecast and its uncertainty—with a 2.4 [2.2, 2.5]-yr delay. Using no forecast but the max–min range of the TCCON-observed growth rates for discrimination only leads to a factor ≈2 longer delay. Therefore, the forecast uncertainties for annual growth rates must be reduced. This requires improved terrestrial ecosystem models and ocean observations to better quantify the land and ocean sinks dominating interannual variability. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Daily-mean XCO<sub>2</sub> time series for the TCCON site Garmisch. The model fit (red) comprises a linear trend along with a fourth order Fourier series. The insert shows in addition in orange the forecasted trend reduction in 2020 due to COVID-19 [<a href="#B22-remotesensing-12-02387" class="html-bibr">22</a>] along with the tilted seasonal curve.</p> Full article ">Figure 2
<p>Annual XCO<sub>2</sub> growth rates for the TCCON sites Garmisch, Zugspitze, Park Falls, and Karlsruhe. The colored lines indicate the 95% confidence bands retrieved from the bootstrap resampling of the individual sites, and black is the multi-site combination: (<b>a</b>) the analysis based on the single-spectra TCCON data; (<b>b</b>) the analysis based on the daily-mean TCCON data; (<b>c</b>) the analysis based on the weekly-mean TCCON data; (<b>d</b>) the analysis based on the 2-weekly-mean TCCON data; and (<b>e</b>) the analysis based on the monthly-mean TCCON data.</p> Full article ">Figure 3
<p>Width of the 95% confidence bands for the annual growth rates retrieved from the TCCON multi-site combination (gm, zs, pa, ka) as a function of the sampling resolution of the time series: single-spectra (≈1 min), daily, weekly, 2-weekly, and monthly resolution. Underlying data are the last row of <a href="#remotesensing-12-02387-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 4
<p>Hemispherically representative annual growth rates derived from a combination of the TCCON sites Garmisch, Zugspitze, Karlsruhe, and Park Falls. The thick black lines give the 95% confidence band derived from the measured time series aggregated into weekly means, the thin black is from the 2-weekly sampling resolution, the dashed black is from daily sampling, the while grey is from using ≈1-min single-spectra sampling. The red and blue stars are the forecasts for Mauna Loa, Hawaii (MLO), with and without the COVID-19 impact [<a href="#B22-remotesensing-12-02387" class="html-bibr">22</a>]. The blue vertical error bars represent 2-sigma forecast uncertainty (±0.57 ppm/yr), and the red lines are the TCCON confidence bands.</p> Full article ">Figure A1
<p>Park Falls time series aggregated into five differing temporal resolutions, i.e., single-spectra (≈1 min), daily, weekly, 2-weekly, and monthly resolution.</p> Full article ">
18 pages, 3560 KiB  
Article
Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China
by Yang Wang, Liangfu Chen, Jinyuan Xin and Xinhui Wang
Remote Sens. 2020, 12(6), 991; https://doi.org/10.3390/rs12060991 - 19 Mar 2020
Cited by 10 | Viewed by 3718
Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network [...] Read more.
The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network (AERONET) station in China, the independent data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) was used to evaluate the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD products in six typical sites and analyze the influence of the aerosol model selection process in five subregions, particularly for dust. Compared with ground-based observations, the performance of all retrievals (except the Shapotou (SPT) site) is similar to other previous studies on a global scale. However, the results illustrate that the AOD retrievals with the dust model showed poor consistency with a regression equation as y = 0.312x + 0.086, while the retrievals obtained from the other models perform much better with a regression equation as y = 0.783x + 0.119. The poor AOD retrieval with the dust model was also verified by a comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product. The results show they have a lower correlation coefficient (R) and a higher mean relative error (MRE) when the aerosol model used in the retrieval is identified as dust. According to the Ultraviolet Aerosol Index (UVAI), the frequency of dust type over southern China is inconsistent with the actual atmospheric condition. In addition, a comparison of ground-based Ångström exponent (α) values yields an unexpected result that the dust model percentage exceed 40% when α < 1.0, and the mean α shows a high value of ~0.75. Meanwhile, the α peak value (~1.1) of the “dust” model determined by a satellite retravel algorithm indicate there is some problem in the dust model selection process. This mismatching of the aerosol model may partly explain the low accuracy at the SPT and the systemic biases in regional and global validations. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
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Figure 1

Figure 1
<p>One example of aerosol model mismatching in Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) algorithm. (<b>a</b>) VIIRS true color image, (<b>b</b>) Ozone Mapping and Profiler Suite Ultraviolet Aerosol Index (OMPS UVAI), (<b>c</b>) VIIRS aerosol model selection, (<b>d</b>) MODIS/Aqua C6 AOD, and (<b>e</b>) VIIRS environment data record aerosol optical depth (EDR AOD) on 1 Mar. 2016.</p> Full article ">Figure 2
<p>(<b>a</b>) CARE-China site locations and five research subregions. VIIRS AOD validation for (<b>b</b>) all match-ups and (<b>c</b>, <b>d</b>, <b>e</b>, <b>f</b>, <b>g</b>, and <b>h</b>) at different CHINA-CARE sites. The red solid, black solid, and dashed lines are the linear regression of the scatter dots, 1:1 line, and the expected error (EE) envelope of ±(0.05 + 0.15AOD), respectively.</p> Full article ">Figure 3
<p>Comparisons of the AOD<sub>VIIRS</sub> and AOD<sub>CARE</sub> for (<b>a</b>) all aerosol models, (<b>b</b>) non-dust aerosol models, and <b>c</b>, <b>d</b>, <b>e</b>, <b>f,</b> and <b>g</b>, sorted by other four type models (Smoke Low Absorption, Smoke Low Absorption, Urban Clean, and Urban Polluted).</p> Full article ">Figure 4
<p>Daily variations of five aerosol model proportions in 2013 at the five subregions: (<b>a</b>) CWC, (<b>b</b>) NCP, (<b>c</b>) CC, (<b>d</b>) SC, (<b>e</b>) SECC; and (<b>f</b>) all subregions. The black solid lines represent the mean proportion of the dust model.</p> Full article ">Figure 5
<p>Annual frequency (2013) of OMPS UVAI greater than 1 in China. The red rectangle frames line out the areas of Subregion 1 (CWC) and Subregion 2 (NCP).</p> Full article ">Figure 6
<p>AOD comparison between the VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data for the dust (black lines) and non-dust (red lines) aerosol models in two regions of interest: R1 (solid line; Subregion 1, Subregion 2, and Subregion 3) and R<sup>2</sup> (dashed line; Subregion 4 and Subregion 5). (<b>a</b>) Correlation coefficient (R) and mean relative error (MRE) (<b>b</b>) of the AOD<sub>VIIRS</sub> and AOD<sub>MODIS</sub>. The blue solid line represents the approximative trendline of R for the dust model.</p> Full article ">Figure 7
<p>(<b>a</b>) Frequency and (<b>b</b>) proportion from the five aerosol models for each Ångström exponent bin.</p> Full article ">Figure 8
<p>Box plot of α from the 5 types of aerosol models. The solid lines in each box indicate the 25th and 75th percentiles, the whiskers represent 2 standard deviation intervals, the middle line is the median value, and the middle point is the mean value of the α.</p> Full article ">Figure 9
<p>Simulation of top of the atmosphere (TOA) reflectance at the VIIRS M3 band according to aerosol models with different dust proportions using the 6SV radiative transfer model. Model-1, 2, 3, and 4 contain 30%, 70%, 80%, and 90% dust, respectively. The surface reflectance is assumed to be 0.07, the satellite zenith angle, solar zenith angle, and relative azimuth are assumed to be 60°, 60°, and 120°, respectively, and the target altitude is 0.</p> Full article ">
24 pages, 12339 KiB  
Article
Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method
by Zhonghua He, Liping Lei, Yuhui Zhang, Mengya Sheng, Changjiang Wu, Liang Li, Zhao-Cheng Zeng and Lisa R. Welp
Remote Sens. 2020, 12(3), 576; https://doi.org/10.3390/rs12030576 - 9 Feb 2020
Cited by 46 | Viewed by 5808
Abstract
Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon [...] Read more.
Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Example of XCO<sub>2</sub> from SCIAMACHY, GOSAT, and OCO-2. Green and blue points represent SCI-XCO<sub>2</sub> and GOS-XCO<sub>2</sub> from 1–8 June 2009. Black and red points are GOS-XCO<sub>2</sub> and OCO-XCO<sub>2</sub> from 1–8 September 2014. Total carbon column observing network (TCCON) sites used for validation are shown with a pink star.</p> Full article ">Figure 2
<p>Workflow chart of spatio-temporal integration of multi-satellite observed XCO<sub>2</sub> using a precision-weighted kriging method.</p> Full article ">Figure 3
<p>One example of the optimized spatio-temporal semi-variogram surface (Zone 1: Latitude center: 55°N). Grey, black, and red points represent spatio-temporal semi-variogram that was calculated from experimental data, fitted models of the conventional and optimized correlation structure.</p> Full article ">Figure 4
<p>Latitudinal-temporal change of integrated XCO<sub>2</sub> (<b>a</b>) and XCO<sub>2</sub> adjustments made during the integration processing, integrated XCO<sub>2</sub> (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mrow> <mi>int</mi> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math>) minus original XCO<sub>2</sub> (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mrow> <mi>ret</mi> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math>) (<b>b</b>) from SCIAMACHY, GOSAT, and OCO-2.</p> Full article ">Figure 5
<p>Latitudinal and temporal variability of global mapped XCO<sub>2</sub> (GM-XCO<sub>2</sub>, top panel), the uncertainty of the prediction (standard deviation, middle panel), and precision (bottom panel).</p> Full article ">Figure 6
<p>Latitudinal and temporal difference between results from precision-weighted and conventional spatio-temporal kriging methods for global mapped XCO<sub>2</sub> (GM-XCO<sub>2</sub>, top panel), the difference in the uncertainty of the prediction (standard deviation, middle panel) and the difference in GM-XCO<sub>2</sub> precision (bottom panel). Positive values indicate precision-weighted results are higher and vice versa.</p> Full article ">Figure 7
<p>Spatial-temporal distribution of mean seasonal globally-mapped XCO<sub>2</sub> (GM-CO<sub>2</sub>) during spring (March, April, May), summer (June, July, August), autumn (September, October, November) and winter (December, January, Febryary) of 2003 (top-left), 2008 (top-right), 2013 (bottom-left), and 2015 (bottom-right). Color bars for different years assume an annual increase of 2 ppm.</p> Full article ">Figure 8
<p>Spatial-temporal distribution of mean GM-XCO<sub>2</sub> in 2016.</p> Full article ">Figure 9
<p>Results of cross-validation using the precision-weighted spatio-temporal kriging method. The relationship between predicted XCO<sub>2</sub> (GM-XCO<sub>2</sub>) and reserved integrated XCO<sub>2</sub> (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mi>int</mi> </msub> </mrow> </msub> </mrow> </semantics></math>) is shown in the left panel. The distribution of predicted bias (absolute difference between GM-XCO<sub>2</sub> and reserved <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mi>int</mi> </msub> </mrow> </msub> </mrow> </semantics></math> ) and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mi>int</mi> </msub> </mrow> </msub> </mrow> </semantics></math> precision is shown in the right panel. The black and red lines in the right panel represent the slope of 1 and 2.</p> Full article ">Figure 10
<p>Temporal variation comparison of GM-XCO<sub>2</sub> at 12 TCCON sites. Grey, red, and blue points represent <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mi>int</mi> </msub> </mrow> </msub> </mrow> </semantics></math>, GM-XCO<sub>2,</sub> and XCO<sub>2</sub> from TCCON measurements, respectively. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>XCO</mi> </mrow> <mrow> <msub> <mn>2</mn> <mi>int</mi> </msub> </mrow> </msub> </mrow> </semantics></math> was retrieved within 500 km of TCCON sites. TCCON measurements from 11:00 to 15:00 local time were selected for comparison.</p> Full article ">Figure 11
<p>Latitudinal and temporal variability of the difference between GM-XCO<sub>2</sub> and CT-XCO<sub>2</sub> (<b>a</b>): GM-XCO<sub>2</sub> minus CT-XCO<sub>2</sub>) and a histogram of the differences (<b>b</b>).</p> Full article ">Figure 12
<p>Temporal variation of XCO<sub>2</sub> from integrated and global mapped results (grey and red points) and CarbonTracker (blue points) over latitude in the range of 30 to 45°N and longitude of 60 to 125°W and 60 to 125°E.</p> Full article ">Figure A1
<p>Latitudinal-temporal change of mean XCO<sub>2</sub> averaging kernel from SCIAMACHY (January–March 2003), GOSAT (June 2009–May 2014), and OCO-2 (September 2014.09–December 2016).</p> Full article ">Figure A2
<p>Latitudinal-temporal change of CT-XCO<sub>2</sub> from 2003 to 2016</p> Full article ">
16 pages, 3781 KiB  
Article
Comparison of Continuous In-Situ CO2 Measurements with Co-Located Column-Averaged XCO2 TCCON/Satellite Observations and CarbonTracker Model Over the Zugspitze Region
by Ye Yuan, Ralf Sussmann, Markus Rettinger, Ludwig Ries, Hannes Petermeier and Annette Menzel
Remote Sens. 2019, 11(24), 2981; https://doi.org/10.3390/rs11242981 - 12 Dec 2019
Cited by 11 | Viewed by 3882
Abstract
Atmospheric CO2 measurements are important in understanding the global carbon cycle and in studying local sources and sinks. Ground and satellite-based measurements provide information on different temporal and spatial scales. However, the compatibility of such measurements at single sites is still underexplored, [...] Read more.
Atmospheric CO2 measurements are important in understanding the global carbon cycle and in studying local sources and sinks. Ground and satellite-based measurements provide information on different temporal and spatial scales. However, the compatibility of such measurements at single sites is still underexplored, and the applicability of consistent data processing routines remains a challenge. In this study, we present an inter-comparison among representative surface and column-averaged CO2 records derived from continuous in-situ measurements, ground-based Fourier transform infrared measurements, satellite measurements, and modeled results over the Mount Zugspitze region of Germany. The mean annual growth rates agree well with around 2.2 ppm yr−1 over a 17-year period (2002–2018), while the mean seasonal amplitudes show distinct differences (surface: 11.7 ppm/column-averaged: 6.6 ppm) due to differing air masses. We were able to demonstrate that, by using consistent data processing routines with proper data retrieval and gap interpolation algorithms, the trend and seasonality can be well extracted from all measurement data sets. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Locations of GAW Global station ZSF and TCCON sites Zugspitze and Garmisch with rectangles representing the spatial coverage of XCO<sub>2</sub> levels extracted in this study for satellite measurements (red) and CarbonTracker-modeled results (blue).</p> Full article ">Figure 2
<p>Monthly CO<sub>2</sub> series of all measurements/data products shown as colored points with fitted curves (colored lines) consisting of STL-decomposed trend and seasonal components, divided into CO<sub>2</sub> (upper panel) and XCO<sub>2</sub> (lower panel).</p> Full article ">Figure 3
<p>Offsets of six different CO<sub>2</sub> and XCO<sub>2</sub> data sets of this study (monthly fitted curves, STL-decomposed trend plus seasonal components) relative to CO<sub>2_INSITU_ZSF</sub>.</p> Full article ">Figure 4
<p>Annual mean growth rates derived from all seven CO<sub>2</sub> and XCO<sub>2</sub> series in this study (STL-decomposed trends, black points, and lines). Colored boxplots represent all 12 values of growth rates from monthly averages.</p> Full article ">Figure 5
<p>Seasonal cycles from STL-decomposed seasonal components of the different CO<sub>2</sub> and XCO<sub>2</sub> series of this study.</p> Full article ">Figure 6
<p>Scatter plots of annual mean growth rates from STL trend components in (<b>a</b>) CO<sub>2_INSITU_ZSF</sub> and (<b>b</b>) CO<sub>2_INSITU_ZSF_ADVS</sub> versus CO<sub>2</sub>/XCO<sub>2</sub> from other measurement data sets. Pearson’s product-moment correlation coefficients (<span class="html-italic">r</span>) are listed accordingly for each pair. The significance levels are shown in symbols as 0.001 (***), 0.01 (**), 0.05 (*), and 0.1 (.). The 95% confidence intervals are shown as error bars on both the x and y-axis with dashed lines representing the 1:1 line.</p> Full article ">Figure A1
<p>Histogram of STL-decomposed remainder components from all CO<sub>2</sub> and XCO<sub>2</sub> data sets of this study. Red dashed line shows the mean of each distribution.</p> Full article ">Figure A2
<p>Scatter plots of annual mean growth rates from STL trend components in (<b>a</b>) CO<sub>2_CT_merge_L6-10</sub> and (<b>b</b>) XCO<sub>2_CT_merge_L1-25</sub> versus CO<sub>2</sub>/XCO<sub>2</sub> measurements. Pearson’s product-moment correlation coefficients (<span class="html-italic">r</span>) are listed accordingly for each pair. The significance levels are shown in symbols as 0.001 (***), 0.01 (**), and 0.05 (*). The 95% confidence intervals are shown as error bars on both x- and y-axis with dashed lines representing the 1:1 line.</p> Full article ">
20 pages, 6937 KiB  
Article
Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China
by Jianjun Liu, Fuzhong Weng, Zhanqing Li and Maureen C. Cribb
Remote Sens. 2019, 11(18), 2120; https://doi.org/10.3390/rs11182120 - 12 Sep 2019
Cited by 21 | Viewed by 4044
Abstract
Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting [...] Read more.
Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Probability distribution functions (PDFs, bars) and cumulative distribution functions (CDFs, lines) with descriptive statistics of the modeling variables in the training dataset. The modeling variables are aerosol optical depth (AOD), particulate matter with diameters less than 2.5 μm (PM<sub>2.5</sub>), 2-m temperature (TEMP), surface pressure (PRESS), relative humidity (RH), total column water (TCW), the east–west component of the wind vector (U-Wind), the north–south component of the wind vector (V-Wind), and the planetary boundary layer height (PBLH).</p> Full article ">Figure 2
<p>Scatter plots of the (<b>a</b>) model fitting and (<b>b</b>) cross-validation of the model. The dashed line is the 1:1 line. R<sup>2</sup>: coefficient of determination; RMSE: root-mean-square error (μg m<sup>−3</sup>); MPE: mean prediction error (μg m<sup>−3</sup>); RPE: relative prediction error.</p> Full article ">Figure 3
<p>Probability distribution functions (PDFs, bars) and cumulative distribution functions (CDFs, lines) of the cross-validation (<b>a</b>) coefficient of determination (R<sup>2</sup>) and (<b>b</b>) root-mean-square error (RMSE) for hourly (in blue) and daily (in red) mean PM<sub>2.5</sub> concentrations.</p> Full article ">Figure 4
<p>Spatial distributions of the cross-validation (<b>a</b>) coefficient of determination (R<sup>2</sup>), (<b>b</b>) root-mean-square error (RMSE, μg m<sup>−3</sup>), (<b>c</b>) mean prediction error (MPE, μg m<sup>−3</sup>) and (<b>d</b>) relative prediction error (RPE, %).</p> Full article ">Figure 5
<p>Scatter plots of estimated PM<sub>2.5</sub> concentrations as a function of surface-measured PM<sub>2.5</sub> concentrations at (<b>a</b>–<b>i</b>) different local times (8:00-16:00 LT). The dashed line is the 1:1 line. LT: local time; N: Number of samples; R<sup>2</sup>: Coefficient of determination; RMSE: Root-mean-square error (μg m<sup>−3</sup>); MPE: Mean prediction error (μg m<sup>−3</sup>); RPE: Relative prediction error (%); OPM<sub>2.5</sub>: Mean and standard deviation of observed PM<sub>2.5</sub> concentrations (μg m<sup>−3</sup>); EPM<sub>2.5</sub>: Mean and standard deviation of estimated PM<sub>2.5</sub> concentrations (μg m<sup>−3</sup>).</p> Full article ">Figure 6
<p>Relative prediction errors [(PPM<sub>2.5</sub> − OPM<sub>2.5</sub>)/OPM<sub>2.5</sub>] as a function of observed PM<sub>2.5</sub> concentrations. PPM<sub>2.5</sub> and OPM<sub>2.5</sub> represent the CV of model-estimated PM<sub>2.5</sub> concentrations and observed PM<sub>2.5</sub> concentrations, respectively.</p> Full article ">Figure 7
<p>The importance assessment for predictor variables: (<b>a</b>) Increase in mean-square errors (%IncMSE) and (<b>b</b>) increase in node purities (IncNodePurity). The variables are aerosol optical depth (AOD), hour of the day (HOUR), latitude (LAT), planetary boundary layer height (PBLH), month (Month), day in the month (Day), relative humidity (RH), total column water (TCW), longitude (LONG), 2-m temperature (TEMP), the north–south component of the wind vector (V10), surface pressure (PRESS), and the east–west component of the wind vector (U10).</p> Full article ">Figure 8
<p>Model-estimated PM<sub>2.5</sub> concentrations (μg m<sup>−3</sup>) over central East China: (<b>a</b>) For the whole year of 2016, (<b>b</b>) March, April, and May (MAM), (<b>c</b>) June, July, and August (JJA), (<b>d</b>) September, October, and November (SON), and (<b>e</b>) December, January, and February (DJF).</p> Full article ">Figure 9
<p>Spatial distributions of annual mean model-estimated PM<sub>2.5</sub> concentrations (μg m<sup>−3</sup>) over central East China for (<b>a</b>–<b>i</b>) different hours of the day (8:00–16:00 LT). LT: Local time.</p> Full article ">Figure 10
<p>Spatial distributions of model-estimated hourly PM<sub>2.5</sub> concentrations (μg m<sup>−3</sup>) for a high pollution episode that occurred on 14 January 2016 over the North China Plain for (<b>a</b>–<b>i</b>) different hours of the day (8:00-16:00 LT). LT: local time.</p> Full article ">Figure 11
<p>(<b>a</b>) Scatter plot of the global Moran Index and (<b>b</b>) spatial agglomeration diagram of annual model-estimated PM<sub>2.5</sub> concentrations over central East China. The numbers in (<b>a</b>) are the percentages of samples with aggregation patterns of I, II, III, and IV. The spatial agglomeration diagram passes the significance test at a significance level of 0.05. The legend in (<b>b</b>) gives the spatial agglomeration category: high–low (HL), low–high (LH), low–low (LL), high–high (HH), and no significance (NS).</p> Full article ">
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