Special Issue "Aerosol and Cloud Properties Retrieval by Satellite Sensors"

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

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editor

Dr. Simon Proud
E-Mail Website
Guest Editor
National Centre for Earth Observation, Oxford, UK
Interests: cloud properties; aerosol properties; radiative transfer; convection

Special Issue Information

Dear Colleagues,

Satellite sensors are a key component in our ability to monitor aerosol and cloud properties on a global scale, providing data that are vital for weather nowcasting and forecasting, climate science, and as an input to further applications, such as hydrological or agricultural models. The capabilities of satellite sensors are rapidly evolving, which presents both opportunities for improving our ability to retrieve aerosol and cloud properties as well as challenges in working with the ever-increasing volume and complexity of data that are available.

This Special Issue aims to bring together the latest retrieval techniques, highlight new cloud and aerosol property datasets, and explore the potential of newly-launched or upcoming satellite sensors—both to improve existing retrievals and to enable the retrieval of new properties. Applications of cloud and aerosol property datasets are also welcomed, as are review papers that summarise the current state-of-the-art.

Dr. Simon Proud
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 papers will be 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 2000 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
  • Aerosols
  • Climate
  • Convection
  • Nowcasting
  • Aerosol–cloud interactions

Published Papers (2 papers)

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Research

Open AccessArticle
A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images
Remote Sens. 2020, 12(3), 450; https://doi.org/10.3390/rs12030450 - 01 Feb 2020
Abstract
Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing [...] Read more.
Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images. Full article
(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)
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Open AccessArticle
Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo
Remote Sens. 2019, 11(24), 2958; https://doi.org/10.3390/rs11242958 - 10 Dec 2019
Abstract
Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region [...] Read more.
Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region of the Earth several times per day with varying geometry. This provides a wealth of information that makes aerosol remote sensing possible when combined with the multi-spectral capabilities of the on-board imagers. Nonetheless, the suitability of geostationary observations for AOD retrieval may vary significantly depending on their spatial, spectral, and temporal characteristics. In this work, the potential of geostationary satellites was assessed based on the concept of critical surface albedo (CSA). CSA is linked to the sensitivity of each spaceborne observation to the aerosol signal, as it is defined as the value of surface albedo for which a varying AOD does not alter the satellite measurement. In this study, the sensitivity to aerosols was determined by estimating the difference between the surface albedo of the observed surface and the corresponding CSA (referred to as dCSA). The values of dCSA were calculated for one year of observations from the Meteosat Second Generation (MSG) spacecraft, based on radiative transfer simulations and information on the satellite acquisition geometry and the properties of the observed surface and aerosols. Different spectral channels from MSG and the future Meteosat Third Generation-Imager were used to study their distinct capabilities for aerosol remote sensing. Results highlight the significant but varying potential of geostationary observations across the observed Earth disk and for different time scales (i.e., diurnal, seasonal, and yearly). For example, the capability of sensing multiples times during the day is revealed to be a notable strength. Indeed, the value of dCSA often fluctuates significantly for a given day, which makes some instants of time more suitable for aerosol retrieval than others. This study determines these instants of time as well as the seasons and the sensing wavelengths that increase the chances for aerosol remote sensing thanks to the variations of dCSA. The outcomes of this work can be used for the development and refinement of AOD retrieval algorithms through the use of the concept of CSA. Furthermore, results can be extrapolated to other present-day geostationary satellites such as Himawari-8/9 and GOES-16/17. Full article
(This article belongs to the Special Issue Aerosol and Cloud Properties Retrieval by Satellite Sensors)
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