An FY-4A/AGRI cloud detection model based on the naive Bayes algorithm

YAN Junjie; GUO Xuexing; QU Jianhua; HAN Min

doi:10.6046/zrzyyg.2021259

2022 Vol. 34, No. 3

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(3): 33-42

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YAN Junjie, GUO Xuexing, QU Jianhua, HAN Min. 2022. An FY-4A/AGRI cloud detection model based on the naive Bayes algorithm. Remote Sensing for Natural Resources, 34(3): 33-42. doi: 10.6046/zrzyyg.2021259

Citation:

YAN Junjie, GUO Xuexing, QU Jianhua, HAN Min. 2022. An FY-4A/AGRI cloud detection model based on the naive Bayes algorithm. Remote Sensing for Natural Resources, 34(3): 33-42. doi: 10.6046/zrzyyg.2021259

An FY-4A/AGRI cloud detection model based on the naive Bayes algorithm

Beijing Huayun Shinetek Science and Technology Co., Ltd., Beijing 100081, China

Received Date: 19 August 2021

Revised Date: 15 September 2022

Publish Date: 21 September 2022

Abstract

Abstract

This study developed an automatic cloud detection method based on the naive Bayes algorithm for the cloud detection of the advanced geosynchronous radiation imager (AGRI) aboard the FY-4A satellite. In this method, the naive Bayes algorithm serves as the core structure, and appropriate infrared channels are selected as the parameters of the characteristic classifier according to the basic cloud detection principle of optical payload to ensure the consistency of cloud detection between day and night. After the classified training and construction for different surface types and different months, a cloud detection model based on the naive Bayes algorithm was finally established. Moreover, the classifier for FY-4A/AGRI data used in the method was established considering seven typical cloud detection characteristics and one characteristic based on the infrared composite images. As indicated by the learning tests and verification using the business cloud detection product of the National Satellite Meteorological Center (NSMC) in 2019, the classifier yielded a probability of detection (POD) greater than 98% for land, desert, shallow water, and deep sea, greater than 80% for snow cover, and greater than 80% for North and South poles. The comparison between the cloud detection results of this study and those obtained using the NSMC business system showed that the cloud detection results of this study had an average monthly POD of the whole year greater than 98%, a false alarm ratio (FAR) less than 5%, and all Kuiper’s skill scores (KSSs) greater than 90%.
- FY-4A/AGRI /
- Naive Bayes Algorithm /
- cloud detection /
- POD

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References

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