Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5637-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-5637-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2025
Research article |
| 21 Oct 2025
| 21 Oct 2025
Cloud fraction estimation using random forest classifier on sky images
Sougat Kumar Sarangi
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
Chandan Sarangi
CORRESPONDING AUTHOR
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
School of Sustainability, Indian Institute of Technology Madras, Chennai, India
Niravkumar Patel
Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
Bomidi Lakshmi Madhavan
Aerosols, Radiation and Trace Gases Group, National Atmospheric Research Laboratory, Gadanki, India
Shantikumar Singh Ningombam
Sun and Solar System Group, Indian Institute of Astrophysics, Bangalore, India
Belur Ravindra
Sun and Solar System Group, Indian Institute of Astrophysics, Bangalore, India
Madineni Venkat Ratnam
Aerosols, Radiation and Trace Gases Group, National Atmospheric Research Laboratory, Gadanki, India
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Cited articles
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Calbó, J., Calbó, J., and Sabburg, J.: Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition, J. Atmos. Ocean. Tech., https://doi.org/10.1175/2007jtecha959.1, 2008.
Chauvin, R., Nou, J., Thil, S., Traoré, A., and Grieu, S.: Cloud Detection Methodology Based on a Sky-imaging System, Energy Procedia, 69, 1970–1980, https://doi.org/10.1016/j.egypro.2015.03.198, 2015.
Chow, C. W., Urquhart, B., Lave, M., Dominguez, A., Kleissl, J., Shields, J., and Washom, B.: Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed, Solar Energy, 85, 2881–2893, https://doi.org/10.1016/j.solener.2011.08.025, 2011.
Dietterich, T. G.: Ensemble Methods in Machine Learning, in: Multiple Classifier Systems, 15 pp., https://doi.org/10.1007/3-540-45014-9_1, 2000.
Fa, T., Xie, W., Wang, Y., and Xia, Y.: Development of an all-sky imaging system for cloud cover assessment, Appl. Opt., 58, 5516, https://doi.org/10.1364/ao.58.005516, 2019.
Fabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Applying self-supervised learning for semantic cloud segmentation of all-sky images, Atmos. Meas. Tech., 15, 797–809, https://doi.org/10.5194/amt-15-797-2022, 2022.
Fu, H., Shen, Y., Liu, J., He, G., Chen, J., Liu, P., Qian, J., and Li, J.: Cloud Detection for FY Meteorology Satellite Based on Ensemble Thresholds and Random Forests Approach, Remote Sens (Basel), 11, https://doi.org/10.3390/rs11010044, 2019.
Ghasemian, N. and Akhoondzadeh, M.: Introducing two Random Forest based methods for cloud detection in remote sensing images, Advances in Space Research, 62, 288–303, https://doi.org/10.1016/j.asr.2018.04.030, 2018.
Ghonima, M. S., Urquhart, B., Chow, C. W., Shields, J. E., Cazorla, A., and Kleissl, J.: A method for cloud detection and opacity classification based on ground based sky imagery, Atmos. Meas. Tech., 5, 2881–2892, https://doi.org/10.5194/amt-5-2881-2012, 2012.
Heinle, A., Macke, A., and Srivastav, A.: Automatic cloud classification of whole sky images, Atmos. Meas. Tech., 3, 557–567, https://doi.org/10.5194/amt-3-557-2010, 2010.
Huang, Y. I., Siems, S., Manton, M., Protat, A., Majewski, L., and Nguyen, H.: Evaluating himawari-8 cloud products using shipborne and CALIPSO observations: Cloud-top height and cloud-top temperature, J. Atmos. Ocean. Tech., 36, 2327–2347, https://doi.org/10.1175/JTECH-D-18-0231.1, 2019.
Jayadevan, V. T., Jayadevan, V., Rodriguez, J. J., and Cronin, A. D.: A New Contrast-Enhancing Feature for Cloud Detection in Ground-Based Sky Images, J. Atmos. Ocean. Tech., https://doi.org/10.1175/jtech-d-14-00053.1, 2015.
Kazantzidis, A., Tzoumanikas, P., Tzoumanikas, P., Bais, A. F., Fotopoulos, S., and Economou, G.: Cloud detection and classification with the use of whole-sky ground-based images, Atmos. Res., https://doi.org/10.1016/j.atmosres.2012.05.005, 2012.
Kliangsuwan, T. and Heednacram, A.: FFT features and hierarchical classification algorithms for cloud images, Eng. Appl. Artif. Intell., https://doi.org/10.1016/j.engappai.2018.08.008, 2018.
Kuhn, P., Nouri, B., Wilbert, S., Prahl, C., Kozonek, N., Schmidt, T., Yasser, Z., Ramirez, L., Zarzalejo, L., Meyer, A., Vuilleumier, L., Heinemann, D., Blanc, P., and Pitz-Paal, R.: Validation of an all-sky imager–based nowcasting system for industrial PV plants, Progress in Photovoltaics: Research and Applications, 26, 608–621, https://doi.org/10.1002/pip.2968, 2018.
Li, H., Wang, F., Ren, H., Sun, H., Liu, C., Wang, B., Lu, J., Zhen, Z., and Liu, X.: Cloud identification model for sky images based on Otsu, in: International Conference on Renewable Power Generation (RPG 2015), 5 pp., https://doi.org/10.1049/cp.2015.0521, 2015.
Li, Q., Lu, W., and Yang, J.: A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images, J. Atmos. Ocean. Tech., https://doi.org/10.1175/jtech-d-11-00009.1, 2011.
Li, Q., Zhang, Z., Lu, W., Yang, J., Ma, Y., and Yao, W.: From pixels to patches: a cloud classification method based on a bag of micro-structures, Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, 2016.
Li, X., Wang, B., Qiu, B., and Wu, C.: An all-sky camera image classification method using cloud cover features, Atmos. Meas. Tech., 15, 3629–3639, https://doi.org/10.5194/amt-15-3629-2022, 2022.
Long, C. N., Long, C. N., Sabburg, J., Calbó, J., Calbó, J., and Pagès, D.: Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images, J. Atmos. Ocean. Tech., https://doi.org/10.1175/jtech1875.1, 2006.
Lothon, M., Barnéoud, P., Gabella, O., Lohou, F., Derrien, S., Rondi, S., Chiriaco, M., Bastin, S., Dupont, J.-C., Haeffelin, M., Badosa, J., Pascal, N., and Montoux, N.: ELIFAN, an algorithm for the estimation of cloud cover from sky imagers, Atmos. Meas. Tech., 12, 5519–5534, https://doi.org/10.5194/amt-12-5519-2019, 2019.
Morris, V.: Total Sky Imager (TSI) sky images (JPEG), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1025309, 2000.
Mu, X., Ting, K. M., and Zhou, Z.-H.: Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees, IEEE Trans. Knowl. Data Eng., 29, 1605–1618, https://doi.org/10.1109/TKDE.2017.2691702, 2017.
Ningombam, S. S., Srivastava, A. K., Bagare, S. P., Singh, R. B., Kanawade, V. P., and Dorjey, N.: Assessment of aerosol optical and micro-physical features retrieved from direct and diffuse solar irradiance measurements from Skyradiometer at a high altitude station at Merak, Environmental Science and Pollution Research, 22, 16610–16619, https://doi.org/10.1007/s11356-015-4788-9, 2015.
Ningombam, S. S., Song, H.-J., Mugil, S. K., Dumka, U. C., Larson, E. J. L., Kumar, B., and Sagar, R.: Evaluation of fractional clear sky over potential astronomical sites, Mon. Not. R. Astron. Soc., 507, 3745–3760, https://doi.org/10.1093/mnras/stab1971, 2021.
Nouri, B., Kuhn, P., Wilbert, S., Hanrieder, N., Prahl, C., Zarzalejo, L., Kazantzidis, A., Blanc, P., and Pitz-Paal, R.: Cloud height and tracking accuracy of three all sky imager systems for individual clouds, Solar Energy, 177, 213–228, https://doi.org/10.1016/j.solener.2018.10.079, 2019.
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man. Cybern., 9, 62–66, https://doi.org/10.1109/TSMC.1979.4310076, 1979.
Rajini, S. A. and Tamilpavai, G.: Classification of Cloud/sky Images based on kNN and Modified Genetic Algorithm, 2018 International Conference on Intelligent Computing and Communication for Smart World (I2C2SW), https://doi.org/10.1109/i2c2sw45816.2018.8997159, 2018.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R., Ahmad, E., and Hartmann, D.: Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989.
Satilmis, P., Bashford-Rogers, T., Chalmers, A., and Debattista, K.: Per-pixel classification of clouds from whole sky HDR images, Signal Processing-image Communication, https://doi.org/10.1016/j.image.2020.115950, 2020.
Scornet, E.: Tuning parameters in random forests, ESAIM: Procs, 60, 144–162, https://doi.org/10.1051/proc/201760144, 2017.
Sedlar, J., Riihimaki, L. D., Lantz, K., and Turner, D. D.: Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products, J. Appl. Meteorol. Clim., 60, 477–491, https://doi.org/10.1175/JAMC-D-20-0153.1, 2021.
Shields, J. E., Karr, M. E., Burden, A. R., Johnson, R. W., Mikuls, V. W., Streeter, J. R., and Hodgkiss, W. S.: Research toward multi-site characterization of sky obscuration by clouds, Scripps Institution of Oceanography La Jolla Ca Marine Physical Lab: La Jolla, CA, USA, https://apps.dtic.mil/sti/citations/ADA547055 (last access: 17 October 2025), 2009.
Taravat, A., Frate, F. Del, Cornaro, C., and Vergari, S.: Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images, IEEE Geoscience and Remote Sensing Letters, https://doi.org/10.1109/lgrs.2014.2356616, 2015.
The MathWorks Inc.: MATLAB, https://in.mathworks.com/help/vision/ref/imagelabeler-app.html (last access: 4 August 2024), 2022.
Várnai, T. and Marshak, A.: Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product, Remote Sens (Basel), 7, 5283–5299, https://doi.org/10.3390/rs70505283, 2015.
Verma, S., Rao, P. V. N., Shaeb, H. B. K., Seshasai, M. V. R., and PadmaKumari, B.: Cloud fraction retrieval using data from Indian geostationary satellites and validation, Int. J. Remote Sens., 39, 7965–7977, https://doi.org/10.1080/01431161.2018.1479792, 2018.
Wan, X., Wan, X., Du, J., and Du, J.: Cloud Classification for Ground-Based Sky Image Using Random Forest, ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, https://doi.org/10.5194/isprs-archives-xliii-b3-2020-835-2020, 2020.
Wang, C., Platnick, S., Meyer, K., Zhang, Z., and Zhou, Y.: A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations, Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, 2020.
Xia, M., Xia, M., Lu, W., Lu, W., Yang, J., Ma, Y., Yao, W., Yao, W., and Zheng, Z.: A hybrid method based on extreme learning machine and k-nearest neighbor for cloud classification of ground-based visible cloud image, Neurocomputing, https://doi.org/10.1016/j.neucom.2015.02.022, 2015.
Xie, W., Liu, D., Yang, M., Chen, S., Wang, B., Wang, Z., Xia, Y., Liu, Y., Wang, Y., and Zhang, C.: SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation, Atmos. Meas. Tech., 13, 1953–1961, https://doi.org/10.5194/amt-13-1953-2020, 2020.
Yang, J., Lu, W., Lu, W., Ma, Y., Yao, W., and Yao, W.: An Automated Cirrus Cloud Detection Method for a Ground-Based Cloud Image, J. Atmos. Ocean. Tech., https://doi.org/10.1175/jtech-d-11-00002.1, 2012.
Yang, J., Min, Q., Lu, W., Ma, Y., Yao, W., and Lu, T.: An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection, Atmos. Meas. Tech., 10, 1191–1201, https://doi.org/10.5194/amt-10-1191-2017, 2017.
Ye, L., Cao, Z., Yang, Z., and Min, H.: CCAD-Net:A Cascade Cloud Attribute Discrimination Network for Cloud Genera Segmentation in Whole-sky Images, IEEE Geoscience and Remote Sensing Letters, https://doi.org/10.1109/lgrs.2022.3184961, 2022.
Short summary
This study introduces a new approach to measure cloud cover from image data taken by ground-based sky observations. Our method used diverse sky images taken from various locations across the globe to train our machine learning model. We achieved a very high accuracy in detecting cloud cover, even in polluted areas. Our model surpasses traditional methods by running efficiently with minimal computational needs.
This study introduces a new approach to measure cloud cover from image data taken by...
