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Multi Sensor Data Integration for Atmospheric Composition Analysis

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

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 5782

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Special Issue Editors

Dr. Ugo Cortesi

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Guest Editor

Institute for Applied Physics “Nello Carrara” (IFAC-CNR), 50019 Sesto Fiorentino (Firenze), Italy
Interests: Earth system observation; remote sensing; atmospheric radiative transfer; data fusion
Special Issues, Collections and Topics in MDPI journals

Dr. Arno Keppens

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Guest Editor

Department of Atmospheric Composition, Royal Belgian Institute for Space Aeronomy (BIRA-IASB), 1180 Brussels, Belgium
Interests: Earth system observation; remote sensing; nadir profile retrievals; data harmonization and comparison

Special Issue Information

Dear Colleagues,

The quantity and quality of current and planned LEO/GEO satellite missions and of airborne and ground-based sensors devoted to monitoring atmospheric composition is making available an unprecedented amount of highly valuable information. This information is key to improving our understanding of processes and trends affecting the Earth's atmosphere and climate, but is sometimes over-abundant in terms of scientific interpretability. Multi-sensor data fusion, denoting techniques and tools to combine (nearly) coincident profile and/or column observations into an integrated output, provides powerful and possibly optimized strategies for the merging and exploitation of this wealth of information, while reducing the data entries (and volume) for the fused products. The purpose of this Special Issue is to stimulate the discussion of scientific and technological aspects of atmospheric data fusion, in connection with the new opportunities and challenges posed by the rapidly evolving atmospheric and climate sciences, as driven by an ever-growing variety of observing systems. Research and review articles are welcome which cover state-of-the-art and innovative algorithms and methods for multi-sensor data fusion, describing their features and performance, along with significant instances of application. Reports on strategies for the harmonization and synergistic use of measured and/or model data, including joint retrieval algorithms and data assimilation systems, or on comparison studies of data fusion techniques are most relevant contributions as well. This comprehensive but not exhaustive list of pertinent topics cannot exclude research papers on the quality assessment and validation of fused products that are of major importance, e.g., when dealing with processing atmospheric data in an operational context.

Dr. Ugo Cortesi
Dr. Arno Keppens
Guest Editors

Manuscript Submission Information

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Keywords

multi-sensor observations
information content merging
data fusion methodology and applications
joint retrieval schemes
assimilation approaches
data regridding and harmonization
multi-sensor product validation

Published Papers (3 papers)

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Research

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15 pages, 2962 KiB

Open AccessArticle

Dust Characteristics Observed by Unmanned Aerial Vehicle over the Taklimakan Desert

by Chenglong Zhou, Yuzhi Liu, Qing He, Xinjie Zhong, Qingzhe Zhu, Fan Yang, Wen Huo, Ali Mamtimin, Xinghua Yang, Yu Wang and Lu Meng

Remote Sens. 2022, 14(4), 990; https://doi.org/10.3390/rs14040990 - 17 Feb 2022

Cited by 9 | Viewed by 1600

Abstract

Based on observations from the Unmanned Aerial Vehicle (UAV) together with an environmental particulate matter analyzer (Grimm-180) and Global Positioning System (GPS) sounding balloons, the vertical structure of dust with different particle sizes was explored over the Taklimakan Desert (TD) during an intensive observation from 1 July 2021 to 31 July 2021. The power functions were fitted between the particle counts and particle sizes, indicating negative correlations with an R² higher than 0.99 under different dust pollution conditions in Tazhong (TZ). The dust concentrations show a sharp vertical increase over the TD during dust pollution; however, more particles with larger sizes are entrained into the air in TZ compared with Minfeng (MF). The total solar radiation during dust pollution days is significantly weakened, accompanied by major modifications in the temperature stratification, which were characterized by low-level cooling (with −2.71 K mean intensity) and high-level heating (with +0.70 K mean intensity). On clear days, the average convective boundary layer (CBL) heights at the TZ and MF are approximately 3.94 and 2.84 km, respectively, and the average stable boundary layer (SBL) height at the TZ and MF are approximately 0.19 and 0.14 km, respectively. With the increasing dust pollution level, the CBL height decreases rapidly while the SBL height shows the opposite trend. The unique ultra-high atmospheric boundary layer structure in daytime provides beneficial conditions for the suspension and vertical transportation of dust over TD. Moreover, a negative correlation between the CBL height and near-surface PM₁₀/PM_2.5/PM_1.0 concentration in TD is revealed by power function fittings. Full article

(This article belongs to the Special Issue Multi Sensor Data Integration for Atmospheric Composition Analysis)

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22 pages, 8488 KiB

Open AccessArticle

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

by Chunlin Huang, Junzhang Li, Weiwei Sun, Qixiang Chen, Qian-Jun Mao and Yuan Yuan

Remote Sens. 2021, 13(16), 3116; https://doi.org/10.3390/rs13163116 - 06 Aug 2021

Cited by 6 | Viewed by 1960

Abstract

Long-term (2000–2019) assessment of aerosol loads and dominant aerosol types at spatiotemporal scales using multi-source datasets can provide a strong impetus to the investigation of aerosol loads and to the targeted prevention control of atmospheric pollution in densely populated regions with frequent anthropogenic activities and heavy aerosol emissions. This study uses multi-source aerosol datasets, including Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET), to conduct a long-term variation assessment of aerosol load, high aerosol load frequency, and dominant aerosol types over Asia. The results indicate that regional aerosol type information with adequate spatial resolution can be combined with aerosol optical depth (AOD) values and heavy aerosol load frequency characterization results to explore the key contributors to air pollution. During the study period, the aerosol load over the North China Plain, Central China, Yangtze River Delta, Red River Delta, Sichuan Basin, and Pearl River Delta exhibited an increasing trend from 2000–2009 due to a sharp rise in aerosol emissions with economic development and a declining trend from 2010–2019 under stricter energy conservation controls and emissions reductions. The growth of urban/industrial (UI) type and biomass burning (BB) type aerosol emissions hindered the improvement of the atmospheric environment. Therefore, in future pollution mitigation efforts, focus should be on the control of UI-type and BB-type aerosol emissions. The Indus–Ganges River Plain, Deccan Plateau, and Eastern Ghats show a continuously increasing trend; however, the aerosol load growth rate of the last decade was lower than that of the first decade, which was mainly due to the decrease in the proportion of the mixed type aerosols. Full article

(This article belongs to the Special Issue Multi Sensor Data Integration for Atmospheric Composition Analysis)

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Other

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12 pages, 1144 KiB

Open AccessTechnical Note

Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework

by Arno Keppens, Steven Compernolle, Daan Hubert, Tijl Verhoelst, José Granville and Jean-Christopher Lambert

Remote Sens. 2022, 14(9), 2197; https://doi.org/10.3390/rs14092197 - 04 May 2022

Viewed by 1283

Abstract

A method is developed that removes a priori information from remotely sensed atmospheric state profiles. This consists of a Wiener deconvolution, whereby the required cost function is obtained from the complete data fusion framework. Asserting that the deconvoluted averaging kernel matrix has to equal the unit matrix, results in an iterative process for determining a profile-specific deconvolution matrix. In contrast with previous deconvolution approaches, only the dimensions of this matrix have to be fixed beforehand, while the iteration process optimizes the vertical grid. This method is applied to ozone profile retrievals from simulated and real measurements co-located with the Izaña ground station. Individual profile deconvolutions yield strong outliers, including negative ozone concentration values, but their spatiotemporal averaging results in prior-free atmospheric state representations that correspond to the initial retrievals within their uncertainty. Averaging deconvoluted profiles thus looks like a viable alternative in the creation of harmonized Level-3 data, avoiding vertical smoothing difference errors and the difficulties that arise with averaged averaging kernels. Full article

(This article belongs to the Special Issue Multi Sensor Data Integration for Atmospheric Composition Analysis)

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