Research on Land Cloud Detection Algorithm Based on AGRI of Fengyun-4B Satellite

Authors

  • Chen Zhang

DOI:

https://doi.org/10.62051/ijcsit.v5n2.01

Keywords:

Fengyun-4, FY-4B, AGRI, MODIS, Cloud Detection

Abstract

Clouds are an important component of weather and climate change and have a significant impact on ecology, weather forecasting and aviation safety. Cloud detection is a preprocessing step in many satellite image processing and remote sensing inversion. Clouds not only cover the subsurface, but also absorb and scatter solar radiation, which affects the remote sensing analysis and study of the surface and reduces the utilization of data. Accurate detection of cloud-covered areas in satellite images can effectively reduce the interference of cloud cover on remote sensing data application. The Fengyun-4B Satellite is the latest generation of geostationary meteorological satellites in China, with significant performance advantages. In this paper, a land cloud detection algorithm for AGRI sensors is constructed based on the existing cloud detection algorithm using the AGRI full disk data of Fengyun 4B Satellite. Through the analysis of cloud detection product quality, the algorithm threshold is optimized to significantly improve the accuracy of cloud detection. The experimental results show that the algorithm has good overall cloud detection performance in full disk imagery, especially in the thick cloud region, but there are some misclassifications in the high brightness surface region such as the Tibetan Plateau. This study expands the application scenarios of domestic geostationary meteorological satellites, and provides new technical support for meteorological monitoring and remote sensing applications.

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References

[1] Dessler A E. A determination of the cloud feedback from climate variations over the past decade[J]. Science, 2010, 330(6010): 1523-1527.

[2] Baker M B, Peter T. Small-scale cloud processes and climate[J]. Nature, 2008, 451(7176): 299-300.

[3] Yang, Shu. Cloud Detection Algorithm for AHI imager onboard Himawari-8 Geostationary Satellite based on Machine Learning [D]. Nanjing: Nanjing University of Information Science & Technology, 2022.

[4] Yang, Chenyang. Cloud Detection Algorithm Research and Prototype System Design Based on FY4 Data [D]. Nanjing: Nanjing University of Information Science & Technology, 2021.

[5] Ackerman S A, Strabala K I, Menzel W P, et al. Discriminating clear sky from clouds with MODIS[J]. Journal of Geophysical Research: Atmospheres, 1998, 103(D24): 32141-32157.

[6] Zhu Z, Woodcock C E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012, 118: 83-94.

[7] Gomis-Cebolla J, Jimenez J C, Sobrino J A. MODIS probabilistic cloud masking over the amazonian evergreen tropical forests: a comparison of machine learning-based methods[J]. International Journal of Remote Sensing, 2020, 41(1): 185-210.

[8] Wang C, Platnick S, Meyer K, et al. A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations[J]. Atmospheric Measurement Techniques, 2020, 13(5): 2257-2277.

[9] Zhan Y, Wang J, Shi J, et al. Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1785-1789.

[10] Si Y, Gao L, Chen L, et al. An Adaptive Dark-Target Algorithm for Retrieving Land AOD Applied to FY-4B/AGRI Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 14035-14049.

[11] Zhou D, Wang Q, Li S, et al. Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images[J]. Remote Sensing, 2024, 16(2): 372.

[12] Levy R C, Mattoo S, Munchak L A, et al. The Collection 6 MODIS aerosol products over land and ocean[J]. Atmospheric Measurement Techniques, 2013, 6(11): 2989-3034.

[13] YANG L, HU X, WANG H, et al. Preliminary test of quantitative capability in aerosol retrieval over land from MERSI-II onboard FY-3D[J]. National Remote Sensing Bulletin, 2022, 26(5): 923-940.

[14] Yang L, Ji W, Pei X, et al. Global evaluation of Fengyun-3 MERSI dark target aerosol retrievals over land[J]. International Journal of Digital Earth, 2024, 17(1): 1-24.

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Published

27-02-2025

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Section

Articles

How to Cite

Zhang, C. (2025). Research on Land Cloud Detection Algorithm Based on AGRI of Fengyun-4B Satellite. International Journal of Computer Science and Information Technology, 5(2), 1-6. https://doi.org/10.62051/ijcsit.v5n2.01