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Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region

Received: 16 June 2023     Accepted: 7 July 2023     Published: 17 July 2023
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Abstract

Clouds have a significant impact on the planet's energy balance, climate, and weather. They serve as the primary temperature regulator and function as a blanket to absorb thermal energy or longwave radiation. The present study estimates the percentage of rainfall clouds within a 100-kilometer radius of Fujairah City on the Gulf of Oman using image processing based on machine learning and digital image processing. The data for 9 months starting from January 2022 to October 2022 has been retrieved from the Copernicus satellite data component through the Sentinel 3 LST F2 channel. K-mean cluster analysis has been used to validate the accuracy of an algorithm which is applied to determine cloud cover, with a precision rate of 99.9% for clear weather and 95.5% for overcast conditions. The findings indicate that most of the rainy clouds were observed during the months of January and July. The remaining duration of the year exhibits a reduced occurrence of these clouds. Beginning in February, the region of interest experiences cloud cover accompanied by precipitation subsequent to the month of January. Similarly, the month of July exhibited cloud covers with moisture. Throughout the year, dry clouds are observed with moderate coverage percentages. However, there are no observations of any of these clouds during the months of May and December. In summary, automated systems for observing clouds in the atmosphere are a valuable method for detecting cloud cover and predicting climatic patterns in diverse geographical locations.

Published in American Journal of Remote Sensing (Volume 11, Issue 2)
DOI 10.11648/j.ajrs.20231102.11
Page(s) 32-35
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Cloud Coverage, LST, Land Surface Temperature, K-Mean Clustering, Sentinel-3, Copernicus, UAE, Fujairah

References
[1] A. Ward, “NASA Facts the Importance of Understanding Clouds.” [Online]. Available: www.nasa.gov
[2] “Digital image processing - Wikipedia.” https://en.wikipedia.org/wiki/Digital_image_processing (accessed Oct. 25, 2022).
[3] C. W. Chen, J. Luo, and K. J. Parker, “Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications,” IEEE transactions on image processing, vol. 7, no. 12, pp. 1673–1683, 1998.
[4] “Sentinel-3 - Wikipedia.” https://en.wikipedia.org/wiki/Sentinel-3 (accessed Oct. 27, 2022).
[5] “Land Surface Temperature - Applications - Sentinel-3 SLSTR - Sentinel Online - Sentinel Online.” https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature (accessed Dec. 08, 2022).
[6] J. Yang, J. Zhou, F.-M. Göttsche, Z. Long, J. Ma, and R. Luo, “Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data,” International Journal of Applied Earth Observation and Geoinformation, vol. 91, p. 102136, 2020.
[7] M. Blazek and P. Pata, “Colour transformations and K-means segmentation for automatic cloud detection,” Meteorologische Zeitschrift, vol. 24, no. 5, pp. 503–509, 2015.
[8] N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image segmentation using K-means clustering algorithm and subtractive clustering algorithm,” Procedia Comput Sci, vol. 54, pp. 764–771, 2015.
[9] E. Grilli, F. Menna, and F. Remondino, “A review of point clouds segmentation and classification algorithms,” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, p. 339, 2017.
[10] B.-Y. Kim, J. W. Cha, and K.-H. Chang, “Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning,” Atmos Meas Tech, vol. 14, no. 10, pp. 6695–6710, 2021.
[11] F. Rashed, M. O. Alhefeiti, and S. B. Mirza, “Investigation of the spatiotemporal changes in the vegetation of the emirates of Fujairah using normalized difference vegetation index (NDVI) and geographical information system (GIS),” 2022, [Online]. Available: www.biosciencejournals.com
[12] A. Saeed, S. Aldhanhani, M. Sirajul, H. Kalathingal, S. B. Mirza, and F. L. Ridouane, “Efficient comprehension of weather pattern from the meteorological dataset acquired from airport sectors of Fujairah, UAE, using machine learning and data mining approaches,” 2023, [Online]. Available: https://www.windfinder.com/forecast/fujairah
[13] M. A. M. Alblooshi, F. R. M. O. Alhefeiti, M. S. Huda, S. B. M. Kalathingal, and F. L. Ridouane, “Assessment of the potential of various types of long short-term memory (LSTM) artificial neural networks and its application in weather forecasting,” International Journal of Advanced Engineering and Technology, 2023.
[14] A. S. J. Alabdouli, M. S. H. Kalathingal, S. B. Mirza, and F. L. Ridouane, “Effective Oil Spill Monitoring Approach over the Gulf of Oman by Using Advanced Machine Learning and Data Mining Tools,” Int J Swarm Evol Comput, vol. 12, p. 276, 2023.
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  • APA Style

    Manar Ahmed Mohammed Alblooshi, Sirajul Huda Kalathingal, Shaher Bano Mirza, Fouad Lamghari Ridouane. (2023). Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region. American Journal of Remote Sensing, 11(2), 32-35. https://doi.org/10.11648/j.ajrs.20231102.11

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    ACS Style

    Manar Ahmed Mohammed Alblooshi; Sirajul Huda Kalathingal; Shaher Bano Mirza; Fouad Lamghari Ridouane. Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region. Am. J. Remote Sens. 2023, 11(2), 32-35. doi: 10.11648/j.ajrs.20231102.11

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    AMA Style

    Manar Ahmed Mohammed Alblooshi, Sirajul Huda Kalathingal, Shaher Bano Mirza, Fouad Lamghari Ridouane. Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region. Am J Remote Sens. 2023;11(2):32-35. doi: 10.11648/j.ajrs.20231102.11

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  • @article{10.11648/j.ajrs.20231102.11,
      author = {Manar Ahmed Mohammed Alblooshi and Sirajul Huda Kalathingal and Shaher Bano Mirza and Fouad Lamghari Ridouane},
      title = {Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region},
      journal = {American Journal of Remote Sensing},
      volume = {11},
      number = {2},
      pages = {32-35},
      doi = {10.11648/j.ajrs.20231102.11},
      url = {https://doi.org/10.11648/j.ajrs.20231102.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20231102.11},
      abstract = {Clouds have a significant impact on the planet's energy balance, climate, and weather. They serve as the primary temperature regulator and function as a blanket to absorb thermal energy or longwave radiation. The present study estimates the percentage of rainfall clouds within a 100-kilometer radius of Fujairah City on the Gulf of Oman using image processing based on machine learning and digital image processing. The data for 9 months starting from January 2022 to October 2022 has been retrieved from the Copernicus satellite data component through the Sentinel 3 LST F2 channel. K-mean cluster analysis has been used to validate the accuracy of an algorithm which is applied to determine cloud cover, with a precision rate of 99.9% for clear weather and 95.5% for overcast conditions. The findings indicate that most of the rainy clouds were observed during the months of January and July. The remaining duration of the year exhibits a reduced occurrence of these clouds. Beginning in February, the region of interest experiences cloud cover accompanied by precipitation subsequent to the month of January. Similarly, the month of July exhibited cloud covers with moisture. Throughout the year, dry clouds are observed with moderate coverage percentages. However, there are no observations of any of these clouds during the months of May and December. In summary, automated systems for observing clouds in the atmosphere are a valuable method for detecting cloud cover and predicting climatic patterns in diverse geographical locations.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region
    AU  - Manar Ahmed Mohammed Alblooshi
    AU  - Sirajul Huda Kalathingal
    AU  - Shaher Bano Mirza
    AU  - Fouad Lamghari Ridouane
    Y1  - 2023/07/17
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajrs.20231102.11
    DO  - 10.11648/j.ajrs.20231102.11
    T2  - American Journal of Remote Sensing
    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
    SP  - 32
    EP  - 35
    PB  - Science Publishing Group
    SN  - 2328-580X
    UR  - https://doi.org/10.11648/j.ajrs.20231102.11
    AB  - Clouds have a significant impact on the planet's energy balance, climate, and weather. They serve as the primary temperature regulator and function as a blanket to absorb thermal energy or longwave radiation. The present study estimates the percentage of rainfall clouds within a 100-kilometer radius of Fujairah City on the Gulf of Oman using image processing based on machine learning and digital image processing. The data for 9 months starting from January 2022 to October 2022 has been retrieved from the Copernicus satellite data component through the Sentinel 3 LST F2 channel. K-mean cluster analysis has been used to validate the accuracy of an algorithm which is applied to determine cloud cover, with a precision rate of 99.9% for clear weather and 95.5% for overcast conditions. The findings indicate that most of the rainy clouds were observed during the months of January and July. The remaining duration of the year exhibits a reduced occurrence of these clouds. Beginning in February, the region of interest experiences cloud cover accompanied by precipitation subsequent to the month of January. Similarly, the month of July exhibited cloud covers with moisture. Throughout the year, dry clouds are observed with moderate coverage percentages. However, there are no observations of any of these clouds during the months of May and December. In summary, automated systems for observing clouds in the atmosphere are a valuable method for detecting cloud cover and predicting climatic patterns in diverse geographical locations.
    VL  - 11
    IS  - 2
    ER  - 

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Author Information
  • Fujairah Research Centre, Fujairah, United Arab Emirates

  • Fujairah Research Centre, Fujairah, United Arab Emirates

  • Fujairah Research Centre, Fujairah, United Arab Emirates

  • Fujairah Research Centre, Fujairah, United Arab Emirates

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