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Satellite Remote Sensing Applied in Atmosphere (3rd Edition)

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A Special Issue of Atmosphere (ISSN 2073-4433) belonging to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (20 October 2025) | Viewed by 12453

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Editors

Dr. Stavros Kolios

E-Mail Website
Guest Editor

Department of Aerospace Science and Technology, National and Kapodistrian University of Athens, 10679 Athens, Greece
Interests: satellite remote sensing; satellite meteorology; satellite climatology; GIS analysis; atmospheric environment
Special Issues, Collections and Topics in MDPI journals

Dr. Nikos Hatzianastassiou

E-Mail Website
Guest Editor

Laboratory of Meteorology, Physics Department, University of Ioannina, 45110 Ioannina, Greece
Interests: aerosol physical properties; aerosol–cloud interactions; aerosol–radiation interactions; radiation and climate; shortwave and longwave radiation transfer and budgets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the second Special Issue entitled “Satellite Remote Sensing Applied in Atmosphere (2nd Edition)” (https://www.mdpi.com/journal/atmosphere/special_issues/X0M0XHE546), published in Atmosphere in 2024, and will cover all aspects of the applications of satellite remote sensing in atmosphere.

Satellite remote sensing has increasing potential applications in a wide range of atmospheric sciences, thanks to continuous improvements in modern satellite sensors, which provide high-quality data and products and are capable of monitoring even the most remote areas across the world.

The Earth’s atmosphere is where weather and climate are created and evolve, and changes in the atmospheric composition modulate weather phenomena. More specifically, natural and anthropogenic sources of particulates and gases, as well as different cloud types, precipitation patterns and extreme weather events, are of great importance and can be efficiently monitored remotely. Aerosols have catalytic impacts on the solar radiation budget, cloud formation and microphysics, affecting the weather and climate worldwide, and therefore need to be efficiently and accurately monitored from space. The accuracy assessment of any type of satellite data and products, spatiotemporal analyses in different topics of atmospheric sciences and meteorology, relative satellite-based applications and innovative techniques and methods that promote satellite remote sensing in the atmosphere and for weather events are challenging research areas.

This Special Issue welcomes studies that address these topics, based on remotely sensed data and products derived from satellites, and authors are invited to submit and publish their research findings.

Dr. Stavros Kolios
Dr. Nikos Hatzianastassiou
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

satellite remote sensing
mapping and monitoring atmosphere
satellite meteorology
satellite climatology
remotely sensed data
satellite-based applications
satellites
aerosols
extreme weather events
storm activity

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Published Papers (6 papers)

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Research

24 pages, 8373 KB

Open AccessArticle

Sensitivity of Airborne Methane Retrieval Algorithms (MF, ACRWL1MF, and DOAS) to Surface Albedo and Types: Hyperspectral Simulation Assessment

by Jidai Chen, Ding Wang, Lizhou Huang and Jiasong Shi

Atmosphere 2025, 16(11), 1224; https://doi.org/10.3390/atmos16111224 - 22 Oct 2025

Cited by 1 | Viewed by 1083

Abstract

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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17 pages, 7328 KB

Open AccessEditor’s ChoiceArticle

Coastal Wind in East Iceland Using Sentinel-1 and Model Data Reanalysis

by Eduard Khachatrian, Yngve Birkelund and Andrea Marinoni

Atmosphere 2025, 16(8), 962; https://doi.org/10.3390/atmos16080962 - 12 Aug 2025

Cited by 3 | Viewed by 1704

Abstract

This study evaluates three wind data sources in East Iceland’s coastal environment: the high-resolution Synthetic Aperture Radar (SAR)-based Sentinel-1, the regional reanalysis Copernicus Arctic Regional Reanalysis (CARRA), and the global reanalysis ECMWF Reanalysis v5 (ERA5). We focus on assessing the advantages and limitations of each dataset, especially considering their differences in spatial and temporal resolutions. While ERA5 aligns well with CARRA and Sentinel-1 offshore, it tends to underestimate wind speeds and misrepresent wind directions near complex coastlines and fjords, with Root Mean Squared Difference (RMSD) values reaching up to 3.98 m/s in these areas. CARRA’s higher resolution allows it to better capture coastal wind dynamics and shows strong agreement with Sentinel-1. Sentinel-1 excels in revealing detailed local wind features, such as katabatic winds in fjords, highlighting the value of satellite observations in complex terrain. By combining these complementary datasets, this study enhances understanding of coastal wind variability and supports improved hazard assessment in Iceland’s challenging coastal environments. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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17 pages, 4672 KB

Open AccessEditor’s ChoiceArticle

Identification and Correction for Sun Glint Contamination in Microwave Radiation Imager-Rainfall Mission Global Ocean Observations Onboard the FY-3G Satellite

by Qiumeng Xue, Xuanyuan Yang, Qiang Zhang and Zhenxing Liu

Atmosphere 2025, 16(6), 630; https://doi.org/10.3390/atmos16060630 - 22 May 2025

Viewed by 1229

Abstract

Microwave radiometers are vital for global ocean observations, yet they are prone to errors from radio frequency interference, sun glint, and other contamination. This paper focuses on the newly launched Chinese FY-3G satellite’s Microwave Radiation Imager-Rainfall Mission (MWRI-RM) instrument, aiming to detect sun glint contamination and set a critical angle for data quality control. The model regression difference method is employed to simulate uncontaminated brightness temperatures at 10.65 GHz. By comparing the observed and simulated values, this study finds that sun glint contamination, which causes a 0–5 K increase in brightness temperature, is strongly related to sun glint angle. Based on the statistical analysis of contaminated pixels from November 2023 to July 2024, it is recommended that a critical angle of 25° be used to flag contaminated areas. The method also identifies persistent television frequency interference along the U.S. coastline at 18.7 GHz, which the radio frequency interference (RFI) Flag in Level 1 data failed to detect. Through the utilization of the model regression difference method, the warm biases in the MWRI-RM observations can be corrected. This research offers a practical way to enhance the accuracy of the MWRI-RM data and can be applied to other microwave radiometry missions. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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14 pages, 3709 KB

Open AccessArticle

Microphysical Characteristics of Summer Precipitation over the Taklamakan Desert Based on GPM-DPR Data from 2014 to 2023

by Wentao Zhang, Guiling Ye, Jeremy Cheuk-Hin Leung and Banglin Zhang

Atmosphere 2025, 16(4), 354; https://doi.org/10.3390/atmos16040354 - 21 Mar 2025

Cited by 6 | Viewed by 1683

Abstract

Precipitation events have been occurring more frequently in the hyper-arid region of the Taklamakan Desert (TD) under recent climate change. However, in this water-limited environment, the microphysical characteristics of precipitation, as well as their link to rainfall intensity, remain unclear. To address this, this study utilizes dual-frequency precipitation radar (DPR) data of the Global Precipitation Measurement (GPM) satellite from 2014 to 2023 to analyze the microphysical characteristics of different precipitation types (stratiform and convective) in the TD during the summer. The results show that liquid water path (LWP) is a key factor influencing precipitation type: when LWP is insufficient, stratiform precipitation is more likely to occur (84.1%), while convective precipitation is difficult to occur (15.9%). Microphysical process analysis indicates that in convective precipitation, abundant low-level moisture leads to the growth of liquid particles primarily through the collision–coalescence process (59.7%), resulting in larger raindrop diameters (1.7 mm) and lower concentrations (31.9 mm⁻¹ m⁻³). In contrast, stratiform precipitation, with limited LWP, primarily involves the melting and breaking-up of high-level ice-phase particles, leading to smaller raindrop diameters (1.2 mm) and higher concentrations (34.3 mm⁻¹ m⁻³). The warm rain process plays a significant role in raindrop formation in both types of precipitation. The greater (lesser) the amount of LWP, the larger (smaller) the contribution of collision–coalescence (break-up) processes, and the larger (smaller) the raindrop diameter and precipitation intensity. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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14 pages, 2671 KB

Open AccessArticle

Analysis of Cross-Polarization Discrimination Due to Rain for Earth–Space Satellite Links Operating at Millimetre-Wave Frequencies in Pretoria, South Africa

by Yusuf Babatunde Lawal, Pius Adewale Owolawi, Chunling Tu, Etienne Van Wyk and Joseph Sunday Ojo

Atmosphere 2025, 16(3), 256; https://doi.org/10.3390/atmos16030256 - 24 Feb 2025

Cited by 3 | Viewed by 3063

Abstract

This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth–space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) over a certain location. This research utilized seasonal rain height data obtained from a recent study and the latest ITU-R P.618-14 guidelines, to compute and analyze XPD variations across six selected frequencies (11.7 GHz to 35 GHz) for different percentages of time exceedance in Pretoria. The study reveals significant seasonal dependencies of rain heights, with XPD reaching its maximum during winter due to lower rain height, and lower rain-induced attenuation and its minimum during summer, characterized by intense convective rainfall and maximum rain height. For instance, the estimated XPD for a 35 GHz signal at 0.01% of the time in the summer, spring, winter, and autumn are 13, 14, 15, and 14 dB, respectively. This implies that radio signals suffer severe attenuation caused by low XPD in the summer. The relationship between CPA and XPD highlights the need for increased XPD margins at higher frequencies to mitigate signal degradation caused by rain depolarization. Practical recommendations include the adoption of adaptive modulation and coding schemes to maintain link reliability during adverse weather conditions, particularly in summer. This research highlights the significance of incorporating frequency-dependent parameters and rain height variability in XPD estimation to enhance the design of satellite communication systems, ensuring optimized performance and reliable operation in a tropical climate. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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21 pages, 6622 KB

Open AccessArticle

Random Forest-Based Retrieval of XCO₂ Concentration from Satellite-Borne Shortwave Infrared Hyperspectral

by Wenhao Zhang, Zhengyong Wang, Tong Li, Bo Li, Yao Li and Zhihua Han

Atmosphere 2025, 16(3), 238; https://doi.org/10.3390/atmos16030238 - 20 Feb 2025

Cited by 5 | Viewed by 2609

Abstract

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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