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Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements

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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: 15 June 2025 | Viewed by 12175

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Dr. Matthew McGill

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Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA
Interests: cloud & aerosol remote sensing; lidar remote sensing; radiative transfer

Special Issue Information

Dear Colleagues,

Clouds are the primary modifier of the Earth’s surface temperature. Aerosols, especially dense aerosol emissions from fires, volcanic eruptions, and dust storms, also provide a modulating effect on the Earth’s temperature. Acting as cloud condensation nuclei, aerosols provide sources for cloud formation, leading to complex interactions between clouds and aerosols that are still poorly understood. In addition to radiative impacts, aerosols impact air quality, especially in the planetary boundary layer (PBL). Remote sensing of clouds and aerosols, both active and passive, provide a means to study clouds, aerosols, and their interactions on both local and global scales. This Special Issue will publish papers highlighting emerging concepts, new instruments and technologies, and scientific results related to remotely sensed measurement of clouds and aerosols in the Earth’s atmosphere.

The objective of this Special Issue is to highlight emerging concepts, new instruments and technologies, and scientific results related to remotely sensed measurement of clouds and aerosols in the Earth’s atmosphere. The Special Issue will highlight the following topics:

Emerging concepts that can provide improved measurements and understanding of cloud and aerosol processes in the Earth’s atmosphere;
Recent sensor and technology developments that enable new or enhanced measurements and understanding of cloud and aerosol properties including distributions, radiative properties, and interactions;
Original scientific results from analysis of data, with emphasis on (1) diurnal variability of clouds and aerosols, (2) application of advanced machine learning techniques, and (3) synergy of active and passive remote sensing techniques.

Dr. Matthew McGill
Guest Editor

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.

Keywords

clouds
cloud radiative effects
aerosols
aerosol radiative effects
cloud–aerosol interactions
aerosol transport
diurnal variability
remote sensing

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Published Papers (9 papers)

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22 pages, 35962 KiB

Open AccessArticle

Evaluation of ICESat-2 ATL09 Atmospheric Products Using CALIOP and MODIS Space-Based Observations

by Kenneth E. Christian, Stephen P. Palm, John E. Yorks and Edward P. Nowottnick

Remote Sens. 2025, 17(3), 482; https://doi.org/10.3390/rs17030482 - 30 Jan 2025

Viewed by 700

Abstract

Since its launch in 2018, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has provided atmospheric products, including calibrated backscatter profiles and cloud and aerosol layer detection. While not the primary focus of the mission, these products garnered more interest after the end of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data collection in 2023. In comparing the cloud and aerosol detection frequencies from CALIOP and ICESat-2, we find general agreement in the global patterns. The global cloud detection frequencies were similar in June, July, and August of 2019 (64.7% for ICESat-2 and 59.8% for CALIOP), as were the location and altitude of the tropical maximum; however, low daytime signal-to-noise ratios (SNRs) reduced ICESat-2’s detection frequencies compared to those of CALIOP. The ICESat-2 global aerosol detection frequencies were likewise lower. ICESat-2 generally retrieved a higher average global aerosol optical depth compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) over the ocean, but the two were in closer agreement over regions with higher aerosol concentrations such as the Eastern Atlantic Ocean and the Northern Indian Ocean. The ICESat-2 and CALIOP orbital coincidences reveal highly correlated backscatter profiles as well as similar cloud and aerosol layer top altitudes. Future work with machine learning denoising techniques may allow for improved feature detection, especially during daytime. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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18 pages, 2990 KiB

Open AccessArticle

Statistics of Smoke Sphericity and Optical Properties Using Spaceborne Lidar Measurements

by Natalie Midzak, John E. Yorks and Jianglong Zhang

Remote Sens. 2025, 17(3), 409; https://doi.org/10.3390/rs17030409 - 25 Jan 2025

Viewed by 721

Abstract

Smoke particles from biomass burning events are typically assumed to be spherical despite previous observations of non-spherical smoke. As such, large uncertainties exist in some physical and optical parameters used in lidar aerosol retrievals, including depolarization and lidar ratio of non-spherical smoke aerosols. In this analysis, using NASA’s Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data during the biomass burning season over Africa from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles to compare with findings of smoke particle non-sphericity from the Cloud-Aerosol Transport System (CATS) lidar. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles, which might otherwise be misclassified as desert dust, polluted dust, or dusty marine by the CALIOP standard aerosol typing algorithm. Then, the relationships between smoke particle sphericity, lidar ratio, and relative humidity are analyzed for CATS and CALIOP observations over Africa. Approximately 18% of smoke layers observed by CALIOP over Africa are non-spherical (depolarization ratio > 0.075) and agree with spatial distributions of non-spherical smoke found in CATS observations. A dependance of lidar ratio on relative humidity was found for layers of spherical smoke over Africa in both CATS and CALIOP data; however, no such dependance was evident for the depolarization ratio and layer relative humidity. While the supplemental smoke aerosol typing algorithm presented in this analysis was targeted only for specific biomass burning regions during biomass burning seasons and is not meant for global operational use, it presents one potential method for improved backscatter lidar aerosol typing. These results suggest that a dynamic lidar ratio, based on layer-relative humidity for spherical smoke, could be used to reduce uncertainties in smoke aerosol extinction retrievals for future backscatter lidars. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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24 pages, 6596 KiB

Open AccessArticle

A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection

by Patrick Selmer, John E. Yorks, Edward P. Nowottnick, Amanda Cresanti and Kenneth E. Christian

Remote Sens. 2024, 16(15), 2735; https://doi.org/10.3390/rs16152735 - 26 Jul 2024

Cited by 2 | Viewed by 2430

Abstract

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~

7.3 \times 10^{- 4}

km⁻¹sr⁻¹, which is lower than the CALIOP 1064 nm night MDB value of

8.6 \times 10^{- 4}

km⁻¹sr⁻¹. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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21 pages, 11372 KiB

Open AccessFeature PaperArticle

Leveraging Deep Learning as a New Approach to Layer Detection and Cloud–Aerosol Classification Using ICESat-2 Atmospheric Data

by Bolaji Oladipo, Joseph Gomes, Matthew McGill and Patrick Selmer

Remote Sens. 2024, 16(13), 2344; https://doi.org/10.3390/rs16132344 - 27 Jun 2024

Cited by 2 | Viewed by 1995

Abstract

NASA’s Ice, Cloud, and land Elevation Satellite (ICESat-2), designed for surface altimetry, plays a pivotal role in providing precise ice sheet elevation measurements. While its primary focus is altimetry, ICESat-2 also offers valuable atmospheric data. Current conventional processing methods for producing atmospheric data products encounter challenges, particularly in conditions with low signal or high background noise. The thresholding technique traditionally used for atmospheric feature detection in lidar data uses a threshold value to accept signals while rejecting noise, which may result in signal loss or false detection in the presence of excessive noise. Traditional approaches for improving feature detection, such as averaging, lead to a trade-off between detection resolution and accuracy. In addition, the discrimination of cloud from aerosol in the identified features is difficult given ICESat-2’s single wavelength and lack of depolarization measurement capability. To address these challenges, we demonstrate atmospheric feature detection and cloud–aerosol discrimination using deep learning-based semantic segmentation by a convolutional neural network (CNN). The key findings from our research are the effectiveness of a deep learning model for feature detection and cloud–aerosol classification in ICESat-2 atmospheric data and the model’s surprising capability to detect complex atmospheric features at a finer resolution than is currently possible with traditional processing techniques. We identify several examples where the traditional feature detection and cloud–aerosol discrimination algorithms struggle, like in scenarios with several layers of vertically stacked clouds, or in the presence of clouds embedded within aerosol, and demonstrate the ability of the CNN model to detect such features, resolving the boundaries between adjacent layers and detecting clouds hidden within aerosol layers at a fine resolution. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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18 pages, 1739 KiB

Open AccessArticle

Polar Stratospheric Cloud Observations at Concordia Station by Remotely Controlled Lidar Observatory

by Luca Di Liberto, Francesco Colao, Federico Serva, Alessandro Bracci, Francesco Cairo and Marcel Snels

Remote Sens. 2024, 16(12), 2228; https://doi.org/10.3390/rs16122228 - 19 Jun 2024

Viewed by 967

Abstract

Polar stratospheric clouds (PSCs) form in polar regions, typically between 15 and 25 km above mean sea level, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT), and supercooled ternary solutions (STSs), the latter being liquid droplets consisting of water, nitric acid, and sulfuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 onward. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. To facilitate the operation of the observatory, several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows. PSC observations in 2021 are shown as an example of the capability and functionality of the lidar observatory. A comparison of the observations with the satellite-borne CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar has been made to demonstrate the quality of the data and their representativeness for the Antarctic Plateau. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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18 pages, 3858 KiB

Open AccessArticle

Improvement of Aerosol Coarse-Mode Detection through Additional Use of Infrared Wavelengths in the Inversion of Arctic Lidar Data

by Christine Böckmann, Christoph Ritter and Sandra Graßl

Remote Sens. 2024, 16(9), 1576; https://doi.org/10.3390/rs16091576 - 29 Apr 2024

Cited by 2 | Viewed by 1201

Abstract

An Nd:YAG-based Raman lidar provides a mature technology to derive profiles of the optical properties of aerosols over a wide altitude range. However, the derivation of micro-physical parameters is an ill-posed problem. Hence, increasing the information content of lidar data is desirable. Recently, ceilometers and wind lidar systems, both operating in the near-infrared region, have been successfully employed in aerosol research. In this study, we demonstrate that the inclusion of additional backscatter coefficients from these two latter instruments clearly improves the inversion of micro-physical parameters such as volume distribution function, effective radius, or single-scattering albedo. We focus on the Arctic aerosol and start with the typical volume distribution functions of Arctic haze and boreal biomass burning. We forward calculate the optical coefficients that the lidar systems should have seen and include or exclude the backscatter coefficients of the ceilometer (910 nm) and wind lidar data (1500 nm) to analyze the value of these wavelengths in their ability to reproduce the volume distribution function, which may be mono- or bimodal. We found that not only the coarse mode but also the properties of the accumulation mode improved when the additional wavelengths were considered. Generally, the 1500 nm wavelength has greater value in correctly reproducing the aerosol properties. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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11 pages, 4826 KiB

Open AccessCommunication

The ARGOS Instrument for Stratospheric Aerosol Measurements

by Matthew T. DeLand, Matthew G. Kowalewski, Peter R. Colarco and Luis Ramos-Izquierdo

Remote Sens. 2024, 16(9), 1531; https://doi.org/10.3390/rs16091531 - 26 Apr 2024

Viewed by 1173

Abstract

Atmospheric aerosols represent an important component of the Earth’s climate system because they can contribute both positive and negative forcing to the energy budget. We are developing the Aerosol Radiometer for Global Observations of the Stratosphere (ARGOS) instrument to provide improved measurements of stratospheric aerosols in a compact package. ARGOS makes limb scattering measurements from space in eight directions simultaneously, using two near-IR wavelengths for each viewing direction. The combination of forward and backward scattering views along the orbit track gives additional information to constrain the aerosol phase function and size distribution. Cross-track views provide expanded spatial coverage. ARGOS will have a demonstration flight through a hosted payload provider in the fall of 2024. The instrument has completed pre-launch environmental testing and radiometric characterization tests. The hosted payload approach offers advantages in size, weight, and power margins for instrument design compared to other approaches, with significant benefits in terms of reducing infrastructure requirements for the instrument team. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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17 pages, 2956 KiB

Open AccessArticle

A Method for Retrieving Cloud Microphysical Properties Using Combined Measurement of Millimeter-Wave Radar and Lidar

by Weiqi Lin, Qianshan He, Tiantao Cheng, Haojun Chen, Chao Liu, Jie Liu, Zhecheng Hong, Xinrong Hu and Yiyuan Guo

Remote Sens. 2024, 16(3), 586; https://doi.org/10.3390/rs16030586 - 4 Feb 2024

Cited by 2 | Viewed by 1658

Abstract

Clouds are an important component of weather systems and are difficult to effectively characterize using current climate models and estimation of radiative forcing. Due to the limitations in observational capabilities, it remains difficult to obtain high-spatiotemporal-resolution, continuous, and accurate observations of clouds. To overcome this issue, we propose a novel and practical combined retrieval method using millimeter-wave radar and lidar, which enables the microphysical properties of thin liquid water clouds, such as cloud droplet effective radius, number concentration, and liquid water content, to be retrieved. This method was utilized to analyze the clouds observed at the Shanghai World Expo Park and was validated through synchronous observations with a microwave radiometer. Furthermore, the most suitable extinction backscatter ratio was determined through sensitivity analysis. This study provides vertical distributions of cloud microphysical properties with a time resolution of 1 min and a spatial resolution of 30 m, demonstrating the scientific potential of this combined retrieval method. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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17 pages, 4399 KiB

Open AccessTechnical Note

Research on Effective Radius Retrievals of Aerosol Particles Based on Dual-Wavelength Lidar

by Zuokun Lv, Dong Liu, Jietai Mao, Zhenzhu Wang, Decheng Wu, Shuai Zhang, Zhiqiang Kuang, Qibing Shi and Yingjian Wang

Remote Sens. 2025, 17(8), 1383; https://doi.org/10.3390/rs17081383 - 13 Apr 2025

Viewed by 247

Abstract

In this study, the effective radius of aerosol particles was experimentally retrieved using a self-developed dual-wavelength atmospheric aerosol lidar. A single-valued lookup table was first established, based on the OPAC database and the Gamma size distribution model, to define the relationship between the extinction coefficient ratio and the effective radius of atmospheric aerosol particles. The extinction coefficients corresponding to the 355 nm and 1064 nm wavelengths were then calculated using the echo signals retrieved horizontally by the lidar, in conjunction with the Mie scattering lidar equation. Subsequently, the lookup table was used to retrieve the real-time effective radius of aerosol particles by inputting the extinction coefficient ratio of the two wavelengths. Finally, the retrieval results were compared with the effective radii measured by an optical particle spectrometer, which had been corrected for relative humidity. An analysis over six months showed a coefficient of determination (R²) greater than 0.83. The results demonstrated that the dual-wavelength lidar exhibits a stable performance, the retrieval method is valid, and the detection results are accurate and reliable. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)

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