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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 37768

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Special Issue Editors

Dr. Jane Liu

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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
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Dr. Liangfu Chen

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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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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
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Dr. Xiaozhen Xiong

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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
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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
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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.

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Published Papers (7 papers)

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Editorial

Jump to: Research

3 pages, 161 KiB

Open AccessEditorial

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 2306

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

Open AccessEditor’s ChoiceArticle

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 32 | Viewed by 7603

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, 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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22 pages, 2896 KiB

Open AccessArticle

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 9244

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 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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18 pages, 3560 KiB

Open AccessArticle

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 3737

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 (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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24 pages, 12339 KiB

Open AccessArticle

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 47 | Viewed by 5864

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 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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16 pages, 3781 KiB

Open AccessArticle

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 12 | Viewed by 3910

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, 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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20 pages, 6937 KiB

Open AccessArticle

Hourly PM_2.5 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 4074

Abstract

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM_2.5) concentrations over areas that do not have PM_2.5 monitoring sites. To date, most studies have focused on estimating daily PM_2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM_2.5 distributions. This study estimates hourly PM_2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM_2.5 concentrations over central East China. The estimated PM_2.5 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 PM_2.5 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. PM_2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM_2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM_2.5 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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