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A cloud detection Scheme for the Chinese Carbon Dioxide Observation Satellite(TANSAT)

Advances in Atmospheric Sciences 2017年第1期34卷 16-25页

作者： Xi WANG Zheng GUO Yipeng HUANG Hongjie FAN Wanbiao LI Laboratory for Climate and Ocean–Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking University National Satellite Meteorological Center China Meteorological Administration

cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight. During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite （TANSAT）, a cloud-screening scheme was presented for the cloud and Aerosol Polarization Imager （CAPI）, which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum. The scheme for CAPI, based on previous cloud- screening algorithms, defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level. This scheme is proven to be more effective for sensors with few channels. The work relies upon the radiance data from the Visible and Infrared Radiometer （VIRR） onboard the Chinese FengYun-3A Polar-orbiting Meteoro- logical Satellite （FY-3A）, which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements. The scheme has been applied to a number of the VIRR scenes over four target areas （desert, snow, ocean, forest） for all seasons. To assess the screening results, comparisons against the cloud-screening product from MODIS are made. The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. A seasonal analysis reveals that this scheme provides better performance during warmer seasons, except for observations over oceans, where results are much better in colder seasons.

关键词： TANSAT CAPI cloud detection regrouping scheme

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cloud detection and Centroid Extraction of Laser Footprint Image of GF-7 Satellite Laser Altimetry

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Journal of Geodesy and Geoinformation Science 2021年第3期4卷 1-12页

作者： Jiaqi YAO Guoyuan LI Jiyi CHEN Genghua HUANG Xiongdan YANG Shuaitai ZHANG College of Geomatics Shandong University of Science and TechnologyQingdao 266590China Land Satellite Remote Sens-ing Application Center Ministry of Natural Resources of P.R.ChinaBeijing100048China Key Laboratory of Space Active Opto-electronics Technology Chinese Academy of SciencesShanghai200083China College of Mapping and Geomatics Lanzhou Jiaotong UniversityLanzhou 730070China

The laser altimeter loaded on the GaoFen-7(GF-7)satellite is designed to record the full waveform data and footprint image,which can obtain high-precision elevation control points for stereo *** footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls,which can be used to judge whether the laser is affected by the *** the same time,the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing *** this manuscript,a data quality analysis scheme of laser altimetry based on footprint image is ***,the cloud detection of footprint image is realized based on deep *** fusion result of the model is about 5%better than that of the traditional cloud detection algorithm,which can quickly and accurately determine whether the laser spot is affected by ***,according to the characteristics of footprint image,a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period *** on the above method,the change of laser spot centroid since GF-7 satellite was put into operation is analyzed,and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.

关键词： GF-7 quality control satellite laser altimetry laser footprint image cloud detection stability analysis of laser pointing angle

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A cloud detection method based on a time series of MODIS surface reflectance images

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International Journal of Digital Earth 2013年第S01期6卷 157-171页

作者： Hairong Tang Kai Yu Olivier Hagolle Kang Jiang Xiurui Geng Yongchao Zhao Key Laboratory of Technology in Geo-spatial information Processing and Application System Institute of ElectronicsChinese Academy of ScienceBeijingChina CESBIO Universite´de ToulouseUnite´mixte CNES CNRS IRD UPSToulouse Cedex 4France

The Moderate Resolution Imaging Spectroradiometer(MODIS)-Terra surface reflectance product(MOD09A1),with bands 1 to 7,is a gridded,eight-day composite product derived from the MODIS-Terra top of atmosphere reflectance *** performs cloud detection and corrects for the effects of atmospheric gases and *** cloud mask(CM)algorithms for MODIS are based on empirical thresholds on spectral reflectance and brightness *** the spatial resolution of the thermal band is 1000 m,while that of MOD09A1 is 500 m,many undetected and false clouds are observed in *** errors always result in temporal and spatial inconsistencies in higher-level *** this paper,a cloud detection algorithm(TSCD)based on a MOD09A1 time series is *** series cloud detection(TSCD)algorithm is based on the relative stability of ground reflectance and the sudden variations in reflectance that result from cloud *** algorithm first searches the clear-sky reference data,and then discriminates clouded and unclouded pixels by detecting a sudden change of reflectance in the blue wavelength and spectral correlation coefficient at the pixel *** with cloud cover assessments obtained from MODIS’original CM,TSCD provides similar or better discrimination in most situations when the land surface changes slowly.

关键词： MODIS surface reflectance time series cloud detection BRDF

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A Machine Learning-based cloud detection Algorithm for the Himawari-8 Spectral Image

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Advances in Atmospheric Sciences 2022年第12期39卷 1994-2007页

作者： Chao LIU Shu YANG Di DI Yuanjian YANG Chen ZHOU Xiuqing HU Byung-Ju SOHN Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044China Key Laboratory for Aerosol-cloud-Precipitation of China Meteorological Administration School of Atmospheric PhysicsNanjing University of Information Science&TechnologyNanjing 210044China School of Atmospheric Sciences Nanjing UniversityNanjing 210046China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological CenterChina Meteorological AdministrationBeijing 100081China School of Earth and Environmental Sciences Seoul National UniversitySeoul 151747South Korea

cloud Masking is one of the most essential products for satellite remote sensing and downstream *** study develops machine learning-based(ML-based)cloud detection algorithms using spectral observations for the Advanced Himawari Imager(AHI)onboard the Himawari-8 geostationary *** active observations from cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP)are used to provide reference labels for model development and *** introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included,and the artificial neural network(ANN)and random forest(RF)techniques are adopted for *** eliminate the influences of surface conditions on cloud detection,we introduce three models with different treatments of the *** of developing independent ML-based algorithms,we add surface variables in a binary way that enhances the ML-based algorithm accuracy by~5%.Validated against CALIOP observations,we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by~5%,while at the same time,reducing misjudgment by~3%.The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy ***,our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI *** furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.

关键词： cloud detection machine learning surface type Himawari-8 CALIPSO

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Development and Initial Assessment of a New Land Index for Microwave Humidity Sounder cloud detection

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Journal of Meteorological Research 2016年第1期30卷 12-37页

作者：秦正坤邹晓蕾 Center of Data Assimilation for Research and Application Nanjing University of Information Science & Technology Earth System Science Interdisciplinary Center University of Maryland

This paper describes a new quality control （QC） scheme for microwave humidity sounder （MHS） data assimilation. It consists of a cloud detection step and an O-B （i.e., differences of brightness temperatures between observations and model simulations） check. Over ocean, cloud detection can be carried out based on two MHS window channels and two Advanced Microwave Sounding Unit-A （AMSU-A） window channels, which can be used for obtaining cloud ice water path （IWP） and liquid water path （LWP）, respectively. Over land, cloud detection of microwave data becomes much more challenging due to a much larger emission contribution from land surface than that from cloud. The current MHS cloud detection over land employs an 0-]3 based method, which could fail to identify cloudy radiances when there is mismatch between actual clouds and model clouds. In this study, a new MHS observation based index is developed for identifying MHS cloudy radiances over land. The new land index for cloud detection exploits the large variability of brightness temperature observations among MHS channels over different clouds, It is shown that those MHS cloudy radiances that were otherwise missed by the current O-B based QC method can be successfully identified by the new land index. An O-B check can then be employed to the remaining data after cloud detection to remove additional outliers with model simulations deviated greatly from observations. It is shown that MHS channel correlations are significantly reduced by the newly proposed QC scheme.

关键词： microwave humidity sounder cloud detection data assimilation

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Comparisons of cloud detection among four satellite sensors on severe haze days in eastern China

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Atmospheric and Oceanic Science Letters 2018年第1期11卷 86-93页

作者： TAN Sai-Chun ZHANG Xiao WANG Hong CHEN Bin SHI Guang-Yu SHI Chong State Key Laboratory of Numerical Modeling of Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China College of Earth Science University of Chinese Academy of Sciences Beijing China State Key Laboratory of Severe Weather/Institute of Atmospheric Composition Chinese Academy of Meteorological Sciences China Meteorological Administration Beijing China Key Laboratory of cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

Satellite remote sensing is very important to obtain a variety of cloud properties. However, the data quality from satellites varies with different satellite characteristics. From December 2015 to January 2016, ground-based air quality index （AQI） data showed severe haze events occurred successively in eastern China, particularly in the Beijing-Tianjin-Hebei region. During those days, a red alert （the most serious level）, orange alert （the second-highest level）, and yellow alert （the third-highest level） for haze, were issued in Beijing. cloud detection from four sensors onboard the ＇A-Train＇satellite constellation were compared for two severe haze episodes, on 21 and 30 December 2015 respectively. Results showed that the MODIS sensor onboard the Aqua satellite misclassified aerosol as cloud, while the other three sensors-AIRS onboard Aqua, the cloud profiling radar onboard cloudSat, and CALIOP onboard CALIPSO-did not observe cloud over the same location. Through the high-AQI haze region in the CALIPSO and cloudSat orbit track, MODIS marked cloud close to the surface, while the MODIS true-color image and CALIOP observed an aerosol layer over the same location, suggesting MODIS falsely observed cloud there. Over the haze region in eastern China, MODIS observed 36% on average greater cloud fraction than AIRS, suggesting haze pollution induces a greater MODIS cloud amount.

关键词： cloud detection aerosol haze MODIS AIRS cloudSat CALIPSO

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A cloud detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

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Guidance, Navigation and Control 2023年第3期3卷 129-155页

作者： Junping Qiu Peng Cheng Chenxiao Cai School of Automation Nanjing University of Science and TechnologyNanjing 210094P.R.China

cloud detection in remote sensing images is a crucial task in various applications,such as meteorological disaster prediction and earth resource exploration,which require accurate cloud identi¯*** work proposes a cloud detection model based on the cloud detection neural Network(CDNet),incorporating a fusion mechanism of channel and spatial *** separable convolution is adopted to achieve a lightweight network model and enhance the e±ciency of network training and *** addition,the Convolutional Block Attention Module(CBAM)is integrated into the network to train the cloud detection model with attention features in channel and spatial *** were conducted on Landsat 8 imagery to validate the proposed improved *** over all testing images,the overall accuracy(OA),mean Pixel Accuracy(mPA),Kappa coe±cient and Mean Intersection over Union(MIoU)of improved CDNet were 96.38%,81.18%,96.05%,and 84.69%,*** results were better than the original CDNet and DeeplabV3+.Experiment results show that the improved CDNet is e®ective and robust for cloud detection in remote sensing images.

关键词： cloud detection feature fusing of two dimensions lightweight network cloud detection neural network(CDNet) Landsat 8 satellite imagery

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cloud detection and Removal in Satellite Images for Tropical Regions

Cloud Detection and Removal in Satellite Images for Tropical...

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1996 3rd International Conference on Signal Processing(ICSP’96)

作者： Zhenlei Cai Anthony T.S.Ho School of Electrical and Electronic Engineering Nanyang Technological University SINGAPORE 639798

＜正＞We present a practical approach for detecting and localizing clouds in satellite remote sensing images. cloud detection is useful in improving the accuracy of land cover classification when there are clouds present in the *** detection and removal of clouds we can selectively merge classification results from two temporally separate images of the same area to minimise the cloud *** emphasise the ease of implementation of the algorithm so that practitioners can easily adapt the method for their own use.

关键词： cloud detection classification image analysis remote sensing data fusion

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Support Vector Machines for cloud detection over Ice-Snow Areas

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Geo-Spatial Information Science 2007年第2期10卷 117-120页

作者： CHEN Gang E Dongchen Department of Geography University of Calgary Calgary T2N 1N4 Canada Chinese Antarctic Center of Surveying and Mapping Wuhan University 129 Luoyu Road Wuhan 430079 China

In polar regions, cloud and underlying ice-snow areas are difficult to distinguish in satellite images because of their high albedo in the visible band and low surface temperature of ice-snow areas in the infrared band. A cloud detection method over ice-snow covered areas in Antarctica is presented. On account of different texture features of cloud and ice-snow areas, five texture features are extracted based on GLCM. Nonlinear SVM is then used to obtain the optimal classification hyperplane from training data. The experiment results indicate that this algorithm performs well in cloud detection in Antarctica, especially for thin cirrus detection. Furthermore, when images are resampled to a quarter or 1/16 of the full size, cloud percentages are still at the same level, while the processing time decreases exponentially.

关键词： cloud detection SVM texture analysis ice-snow covered area polar region

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Comparisons of Two cloud-detection Schemes for Infrared Radiance Observations

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Atmospheric and Oceanic Science Letters 2014年第4期7卷 358-363页

作者： XU Dong-Mei HUANG Xiang-Yu LIU Zhi-Quan MIN Jin-Zhong Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology National Center for Atmospheric Research

The cloud-detection procedure developed by McNally and Watts(MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual(MMR) scheme for both simulated and real Advanced Infrared Sounder(AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR(Vis/NIR) cloud mask.

关键词： AIRS WRF data assimilation system cloud detection brightness temperature departure

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