Articles | Volume 8, issue 11
https://doi.org/10.5194/amt-8-4671-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-4671-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
05 Nov 2015
Research article |
| 05 Nov 2015
An automated cloud detection method based on the green channel of total-sky visible images
J. Yang
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA
Q. Min
CORRESPONDING AUTHOR
Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
W. Lu
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
W. Yao
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Y. Ma
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
J. Du
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
T. Lu
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
G. Liu
Smart Grid Operation Research Center, China Electric Power Research Institute, Beijing 100192, China
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Cited
39 citations as recorded by crossref.
- Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. https://doi.org/10.1175/JTECH-D-21-0159.1
- Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera J. Alonso-Montesinos https://doi.org/10.3390/rs12091382
- Raw Image Based Over-Exposure Correction Using Channel-Guidance Strategy Y. Fu et al. https://doi.org/10.1109/TCSVT.2023.3311766
- A total sky cloud detection method using real clear sky background J. Yang et al. https://doi.org/10.5194/amt-9-587-2016
- On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval M. Krinitskiy et al. https://doi.org/10.3390/rs13020326
- An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection J. Yang et al. https://doi.org/10.5194/amt-10-1191-2017
- ELIFAN, an algorithm for the estimation of cloud cover from sky imagers M. Lothon et al. https://doi.org/10.5194/amt-12-5519-2019
- Detecting clear sky images P. Pawar et al. https://doi.org/10.1016/j.solener.2019.02.069
- Assessment of Nighttime Cloud Cover Products from MODIS and Himawari-8 Data with Ground-Based Camera Observations N. Lagrosas et al. https://doi.org/10.3390/rs14040960
- Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting W. Kong et al. https://doi.org/10.1016/j.apenergy.2020.115875
- Cloud fraction retrieval and its variability during daytime from ground-based sky imagery over a tropical station in India A. Nikumbh et al. https://doi.org/10.1016/j.jastp.2019.05.002
- Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network B. Kim et al. https://doi.org/10.5194/amt-16-5403-2023
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al. https://doi.org/10.5194/amt-12-4713-2019
- Ten-Minute Prediction of Solar Irradiance Based Cloud Detection and a Long Short-Term Memory (LSTM) Model H. Zuo et al. https://doi.org/10.2139/ssrn.3944467
- Very short-term solar irradiance forecasting based on open-source low-cost sky imager and hybrid deep-learning techniques M. Ansong et al. https://doi.org/10.1016/j.solener.2025.113516
- Deep Guided Exposure Correction with Knowledge Distillation S. Liu & T. Zhang https://doi.org/10.3390/s25247606
- Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model H. Zuo et al. https://doi.org/10.1016/j.egyr.2022.03.182
- Cloud behavior prediction for solar power applications: A bibliometric analysis, categorized literature review, and future research directions M. Nejati et al. https://doi.org/10.1016/j.prime.2025.101119
- Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images N. Lagrosas et al. https://doi.org/10.1029/2021JD034772
- Deep Convolutional Neural Network for Cloud Coverage Estimation from Snapshot Camera Images R. Onishi & D. Sugiyama https://doi.org/10.2151/sola.2017-043
- Cloud Observation and Cloud Cover Calculation at Nighttime Using the Automatic Cloud Observation System (ACOS) Package B. Kim & J. Cha https://doi.org/10.3390/rs12142314
- Cloud Detection Method Based on Spatial–Spectral Features and Encoder–Decoder Feature Fusion J. Zhang et al. https://doi.org/10.1109/TGRS.2023.3329206
- Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images J. Yang et al. https://doi.org/10.1175/JAMC-D-18-0095.1
- A comprehensive analysis of blue-to-red atmospheric signatures from all-sky imagery: CLARIS, an adaptive cloud-cover framework P. Valdelomar et al. https://doi.org/10.1016/j.atmosres.2026.109131
- Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning B. Kim et al. https://doi.org/10.5194/amt-14-6695-2021
- FFT features and hierarchical classification algorithms for cloud images T. Kliangsuwan & A. Heednacram https://doi.org/10.1016/j.engappai.2018.08.008
- Photovoltaic power forecast based on satellite images considering effects of solar position Z. Si et al. https://doi.org/10.1016/j.apenergy.2021.117514
- Cloud Detection Method Using CNN Based on Cascaded Feature Attention and Channel Attention J. Zhang et al. https://doi.org/10.1109/TGRS.2021.3120752
- Renewable energy powered membrane technology: Integration of solar irradiance forecasting for predictive control of photovoltaic-powered brackish water desalination system M. Ansong et al. https://doi.org/10.1016/j.apenergy.2025.126651
- Cloud detection methodologies: variants and development—a review S. Mahajan & B. Fataniya https://doi.org/10.1007/s40747-019-00128-0
- Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold Y. Niu et al. https://doi.org/10.1016/j.renene.2024.120452
- From pixels to patches: a cloud classification method based on a bag of micro-structures Q. Li et al. https://doi.org/10.5194/amt-9-753-2016
- Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory M. Azhar et al. https://doi.org/10.3390/universe7020041
- Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet W. Boonpook et al. https://doi.org/10.3390/ijgi15020066
- Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model J. Du et al. https://doi.org/10.3390/en11051107
- Cloud segmentation property extraction from total sky image repositories using Python D. Igoe et al. https://doi.org/10.1080/10739149.2019.1603996
- Pixel‐Based Image Processing for CIE Standard Sky Classification through ANN D. Granados-López et al. https://doi.org/10.1155/2021/2636157
- Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba C. Cai et al. https://doi.org/10.1016/j.rineng.2024.103022
- Vision transformers for estimating irradiance using data scarce sky images D. Hamlyn et al. https://doi.org/10.1016/j.egyai.2025.100560
39 citations as recorded by crossref.
- Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition F. Li et al. https://doi.org/10.1175/JTECH-D-21-0159.1
- Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera J. Alonso-Montesinos https://doi.org/10.3390/rs12091382
- Raw Image Based Over-Exposure Correction Using Channel-Guidance Strategy Y. Fu et al. https://doi.org/10.1109/TCSVT.2023.3311766
- A total sky cloud detection method using real clear sky background J. Yang et al. https://doi.org/10.5194/amt-9-587-2016
- On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval M. Krinitskiy et al. https://doi.org/10.3390/rs13020326
- An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection J. Yang et al. https://doi.org/10.5194/amt-10-1191-2017
- ELIFAN, an algorithm for the estimation of cloud cover from sky imagers M. Lothon et al. https://doi.org/10.5194/amt-12-5519-2019
- Detecting clear sky images P. Pawar et al. https://doi.org/10.1016/j.solener.2019.02.069
- Assessment of Nighttime Cloud Cover Products from MODIS and Himawari-8 Data with Ground-Based Camera Observations N. Lagrosas et al. https://doi.org/10.3390/rs14040960
- Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting W. Kong et al. https://doi.org/10.1016/j.apenergy.2020.115875
- Cloud fraction retrieval and its variability during daytime from ground-based sky imagery over a tropical station in India A. Nikumbh et al. https://doi.org/10.1016/j.jastp.2019.05.002
- Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network B. Kim et al. https://doi.org/10.5194/amt-16-5403-2023
- Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks C. Shi et al. https://doi.org/10.5194/amt-12-4713-2019
- Ten-Minute Prediction of Solar Irradiance Based Cloud Detection and a Long Short-Term Memory (LSTM) Model H. Zuo et al. https://doi.org/10.2139/ssrn.3944467
- Very short-term solar irradiance forecasting based on open-source low-cost sky imager and hybrid deep-learning techniques M. Ansong et al. https://doi.org/10.1016/j.solener.2025.113516
- Deep Guided Exposure Correction with Knowledge Distillation S. Liu & T. Zhang https://doi.org/10.3390/s25247606
- Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model H. Zuo et al. https://doi.org/10.1016/j.egyr.2022.03.182
- Cloud behavior prediction for solar power applications: A bibliometric analysis, categorized literature review, and future research directions M. Nejati et al. https://doi.org/10.1016/j.prime.2025.101119
- Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images N. Lagrosas et al. https://doi.org/10.1029/2021JD034772
- Deep Convolutional Neural Network for Cloud Coverage Estimation from Snapshot Camera Images R. Onishi & D. Sugiyama https://doi.org/10.2151/sola.2017-043
- Cloud Observation and Cloud Cover Calculation at Nighttime Using the Automatic Cloud Observation System (ACOS) Package B. Kim & J. Cha https://doi.org/10.3390/rs12142314
- Cloud Detection Method Based on Spatial–Spectral Features and Encoder–Decoder Feature Fusion J. Zhang et al. https://doi.org/10.1109/TGRS.2023.3329206
- Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images J. Yang et al. https://doi.org/10.1175/JAMC-D-18-0095.1
- A comprehensive analysis of blue-to-red atmospheric signatures from all-sky imagery: CLARIS, an adaptive cloud-cover framework P. Valdelomar et al. https://doi.org/10.1016/j.atmosres.2026.109131
- Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning B. Kim et al. https://doi.org/10.5194/amt-14-6695-2021
- FFT features and hierarchical classification algorithms for cloud images T. Kliangsuwan & A. Heednacram https://doi.org/10.1016/j.engappai.2018.08.008
- Photovoltaic power forecast based on satellite images considering effects of solar position Z. Si et al. https://doi.org/10.1016/j.apenergy.2021.117514
- Cloud Detection Method Using CNN Based on Cascaded Feature Attention and Channel Attention J. Zhang et al. https://doi.org/10.1109/TGRS.2021.3120752
- Renewable energy powered membrane technology: Integration of solar irradiance forecasting for predictive control of photovoltaic-powered brackish water desalination system M. Ansong et al. https://doi.org/10.1016/j.apenergy.2025.126651
- Cloud detection methodologies: variants and development—a review S. Mahajan & B. Fataniya https://doi.org/10.1007/s40747-019-00128-0
- Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold Y. Niu et al. https://doi.org/10.1016/j.renene.2024.120452
- From pixels to patches: a cloud classification method based on a bag of micro-structures Q. Li et al. https://doi.org/10.5194/amt-9-753-2016
- Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory M. Azhar et al. https://doi.org/10.3390/universe7020041
- Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet W. Boonpook et al. https://doi.org/10.3390/ijgi15020066
- Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model J. Du et al. https://doi.org/10.3390/en11051107
- Cloud segmentation property extraction from total sky image repositories using Python D. Igoe et al. https://doi.org/10.1080/10739149.2019.1603996
- Pixel‐Based Image Processing for CIE Standard Sky Classification through ANN D. Granados-López et al. https://doi.org/10.1155/2021/2636157
- Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba C. Cai et al. https://doi.org/10.1016/j.rineng.2024.103022
- Vision transformers for estimating irradiance using data scarce sky images D. Hamlyn et al. https://doi.org/10.1016/j.egyai.2025.100560
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