Research on the fusion of FY4A satellite data and station observation data for heavy fog recognition

Satellite observation of fog possesses technical advantages of wide coverage and high spatial-temporal resolution. However, the accuracy of satellite-based fog identification is subject to errors induced by factors such as atmospheric and radiation conditions. This study aims to improve the accuracy...

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Vydané v:	Theoretical and applied climatology Ročník 156; číslo 1; s. 59
Hlavní autori:	Yao, Zhenhai, Wang, Chuanhui, Jiang, Chun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Vienna Springer Vienna 01.01.2025 Springer Nature B.V
Predmet:	Accuracy Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences China Climatology Cloud cover Complementarity Earth and Environmental Science Earth Sciences Fog Identification Machine learning Monitoring methods Multidimensional methods Radiation Radiation-cloud interactions remote sensing Satellite data Satellite observation Satellites Spatial distribution Support vector machines Temporal resolution Waste Water Technology Water Management Water Pollution Control China Fog recognition FY4A Optimal algorithm Station-observation-based correction Anhui province
ISSN:	0177-798X, 1434-4483
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Abstract	Satellite observation of fog possesses technical advantages of wide coverage and high spatial-temporal resolution. However, the accuracy of satellite-based fog identification is subject to errors induced by factors such as atmospheric and radiation conditions. This study aims to improve the accuracy of fog identification by integrating ground-based station observations with the Fengyun-4 A (FY-4 A) satellite data. Taking Anhui Province as the study area, we establish a fog identification model using multiple algorithms, namely threshold method (THD), support vector machine (SVM), random forest (RF) and gradient boosting (XGB). In addition, a nearby pixel method is employed to validate identification results, in order to select the optimal algorithm. The results indicate that machine learning algorithms outperform the THD method in fog identification. Among the SVM, RF and XGB algorithms, the RF method exhibits the highest median KSS (0.66) and excellent robustness, and thus it is the optimal algorithm. Case studies demonstrate that the RF-based identification results effectively reflect the spatial distribution of fog regions. Although the differences between the images of identification results before and after correction are not obvious, the identification accuracy is highly susceptible to instability due to factors such as radiation, cloud cover and fog intensity. After correction based on station observations, the model KSS scores are noticeably improved (up to 67.2%) and become more stable. Compared with single-satellite-data-based fog monitoring methods, the integration of the FY-4 A satellite data and station observations offers multi-dimensional observation complementarity and achieves technological advances in the digitization and spatialization of fog observations.
AbstractList	Satellite observation of fog possesses technical advantages of wide coverage and high spatial-temporal resolution. However, the accuracy of satellite-based fog identification is subject to errors induced by factors such as atmospheric and radiation conditions. This study aims to improve the accuracy of fog identification by integrating ground-based station observations with the Fengyun-4 A (FY-4 A) satellite data. Taking Anhui Province as the study area, we establish a fog identification model using multiple algorithms, namely threshold method (THD), support vector machine (SVM), random forest (RF) and gradient boosting (XGB). In addition, a nearby pixel method is employed to validate identification results, in order to select the optimal algorithm. The results indicate that machine learning algorithms outperform the THD method in fog identification. Among the SVM, RF and XGB algorithms, the RF method exhibits the highest median KSS (0.66) and excellent robustness, and thus it is the optimal algorithm. Case studies demonstrate that the RF-based identification results effectively reflect the spatial distribution of fog regions. Although the differences between the images of identification results before and after correction are not obvious, the identification accuracy is highly susceptible to instability due to factors such as radiation, cloud cover and fog intensity. After correction based on station observations, the model KSS scores are noticeably improved (up to 67.2%) and become more stable. Compared with single-satellite-data-based fog monitoring methods, the integration of the FY-4 A satellite data and station observations offers multi-dimensional observation complementarity and achieves technological advances in the digitization and spatialization of fog observations. Satellite observation of fog possesses technical advantages of wide coverage and high spatial-temporal resolution. However, the accuracy of satellite-based fog identification is subject to errors induced by factors such as atmospheric and radiation conditions. This study aims to improve the accuracy of fog identification by integrating ground-based station observations with the Fengyun-4 A (FY-4 A) satellite data. Taking Anhui Province as the study area, we establish a fog identification model using multiple algorithms, namely threshold method (THD), support vector machine (SVM), random forest (RF) and gradient boosting (XGB). In addition, a nearby pixel method is employed to validate identification results, in order to select the optimal algorithm. The results indicate that machine learning algorithms outperform the THD method in fog identification. Among the SVM, RF and XGB algorithms, the RF method exhibits the highest median KSS (0.66) and excellent robustness, and thus it is the optimal algorithm. Case studies demonstrate that the RF-based identification results effectively reflect the spatial distribution of fog regions. Although the differences between the images of identification results before and after correction are not obvious, the identification accuracy is highly susceptible to instability due to factors such as radiation, cloud cover and fog intensity. After correction based on station observations, the model KSS scores are noticeably improved (up to 67.2%) and become more stable. Compared with single-satellite-data-based fog monitoring methods, the integration of the FY-4 A satellite data and station observations offers multi-dimensional observation complementarity and achieves technological advances in the digitization and spatialization of fog observations.
ArticleNumber	59
Author	Yao, Zhenhai Wang, Chuanhui Jiang, Chun
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SubjectTerms	Accuracy Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences China Climatology Cloud cover Complementarity Earth and Environmental Science Earth Sciences Fog Identification Machine learning Monitoring methods Multidimensional methods Radiation Radiation-cloud interactions remote sensing Satellite data Satellite observation Satellites Spatial distribution Support vector machines Temporal resolution Waste Water Technology Water Management Water Pollution Control
Title	Research on the fusion of FY4A satellite data and station observation data for heavy fog recognition
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