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Remote Sensing in Meteorology
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Remote Sensing in Meteorology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 3639

Special Issue Editors

Dr. Yahui Che

E-Mail Website
Guest Editor
School of Engineering and Built Environment, Griffith University, Kessels Road, Nathan, QLD 4111, Australia
Interests: aerosol remote sensing; wind erosion; dust weather; PM10; dust detection
Dr. Lu She

E-Mail Website
Guest Editor
School of Geography and Planning, Ningxia University, Yinchuan 750021, China
Interests: aerosol remote sensing; dust detection; surface reflectance; aerosol retrieval algorithm; deep neural network

Special Issue Information

Dear Colleagues,

Satellite images provide a wide view of meteorological conditions in a sub-day frequency. With the development of meteorological satellites, a large amount of satellite measurements have been inversed into physical information on clouds, temperature, precipitation, wind field, snow and ice cover, and so on. Retrieval algorithms based on physical mechanism, empirical relationships, and machine learning have all contributed to the developments and applications of meteorological products. Meanwhile, validation work with high-quality ground-based filed observations is necessary for meteorological research and improvement to these algorithms and products.

This Special Issue focuses on the development of retrieval algorithms for meteorological information, validation of meteorological products, and application of satellite measurements on meteorology.

Dr. Yahui Che
Dr. Lu She
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. Applied Sciences 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 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
  • retrieval algorithm
  • dust storm detection
  • machine learning
  • validation and evaluation

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Published Papers (1 paper)

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Research

17 pages, 18972 KiB  
Article
Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
by Lu Yang, Lu She, Yahui Che, Xingwei He, Chen Yang and Zixian Feng
Appl. Sci. 2023, 13(3), 1365; https://doi.org/10.3390/app13031365 - 19 Jan 2023
Cited by 7 | Viewed by 3146
Abstract
Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) [...] Read more.
Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification. Full article
(This article belongs to the Special Issue Remote Sensing in Meteorology)
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