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Remote Sensing in Meteorology

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Dr. Yahui Che

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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

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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

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Keywords

satellite remote sensing
retrieval algorithm
dust storm detection
machine learning
validation and evaluation

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17 pages, 18972 KiB

Open AccessArticle

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 3 | Viewed by 2365

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) 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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