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Cloud Detection for FY Meteorology Satellite Based on Ensemble Thresholds and Random Forests Approach

Hualian Fu, Yuan Shen, Jun Liu, Guangjun He, Jinsong Chen, Ping Liu, Jing Qian, Jun Li
2018 Remote Sensing  
Therefore, this paper proposes a cloud detection method trying to improve NSMC's products based on ensemble threshold and random forest.  ...  For Chinese FY serial satellite, the National Meteorological Satellite Center (NSMC) officially provides the cloud detection products.  ...  Acknowledgments: We would like to thank Shanxin Guo for his great help in the image processing, methodology analysis and manuscript editing.  ... 
doi:10.3390/rs11010044 fatcat:bwt3rizypfeejdtn3vt7hunglu

Cloud detection methodologies: variants and development—a review

Seema Mahajan, Bhavin Fataniya
2019 Complex & Intelligent Systems  
Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection.  ...  The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement. 3 The literature survey reviewed various forms of cloud detection  ...  random forest (RF) for fusion of visible-infrared (VIR) and thermal classifiers 98% [62] Cloud FY-2G satellite Based on ensemble thresholds and random forests approach NA [24] Ice/snow  ... 
doi:10.1007/s40747-019-00128-0 fatcat:ftol5w36vzdwzpuqeijsz2dct4

Fengyun-3D/MERSI-II Cloud Thermodynamic Phase Determination Using a Machine-Learning Approach

Dexin Zhao, Lin Zhu, Hongfu Sun, Jun Li, Weishi Wang
2021 Remote Sensing  
To reduce the algorithm dependence on spectral properties and empirical thresholds for CP retrieval, a machine learning (ML)-based methodology was developed for retrieving CP data from China's new-generation  ...  CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), MODIS, and FY-3D/MERSI-II CP products were used together for cross-validation.  ...  Acknowledgments: We would like to thank anonymous reviewers for their valuable suggestions and comments, which helped the authors think deeply about some theoretical and technical issues and significantly  ... 
doi:10.3390/rs13122251 fatcat:gdvnrrrasfg5ndstcgbbm2bjmu

Cloud Detection from FY-4A's Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches

Qi Zhang, Yi Yu, Weimin Zhang, Tengling Luo, Xiang Wang
2019 Remote Sensing  
Due to differences in surface cover and meteorological elements between land and sea, we chose logistic regression (lr) model for the land and extremely randomized tree (et) model for the sea respectively  ...  FengYun-4A (FY-4A)'s Geostationary Interferometric Infrared Sounder (GIIRS) is the first hyperspectral infrared sounder on board a geostationary satellite, enabling the collection of infrared detection  ...  Acknowledgments: The data support from National Satellite Meteorological Centre of China (http://www. is acknowledged.  ... 
doi:10.3390/rs11243035 fatcat:34rqwve25zcpxmcmas3swt7yq4

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, Jianya Gong
2021 The Cryosphere  
clouds and solar illumination.  ...  Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover.  ...  However, polar regions contend with clouds and limited solar illumination which are the greatest challenges for snow cover detection using optical satellite data.  ... 
doi:10.5194/tc-15-835-2021 fatcat:4kn7qndy35d6pfzg6dzw4ki74i

Estimating Rainfall with Multi-resource Data over East Asia Based on Machine Learning

Yushan Zhang, Kun Wu, Jinglin Zhang, Feng Zhang, Haixia Xiao, Fuchang Wang, Jianyin Zhou, Yi Song, Liang Peng
2021 Remote Sensing  
Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational  ...  Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and  ...  Acknowledgments: We gratefully thank JMA for freely offering the Himawari-8 satellite data and NOAA for providing the NCEP GFS historical Forecast data archive.  ... 
doi:10.3390/rs13163332 fatcat:5ubkzuyfwrhwxjk6ysgqj336nu

Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program

Jeffrey S. Reid, Edward J. Hyer, Elaine M. Prins, Douglas L. Westphal, Jianglong Zhang, Jun Wang, Sundar A. Christopher, Cynthia A. Curtis, Christopher C. Schmidt, Daniel P. Eleuterio, Kim A. Richardson, Jay P. Hoffman
2009 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
While new fire monitoring systems are based on fundamental Earth Systems Science (ESS) research, adaptation to the forecasting problem requires special procedures and simplifications.  ...  To help exploit research and data products in climate, ESS, meteorology and air quality biomass burning communities, the joint Navy, NASA, NOAA, and University Fire Locating and Modeling of Burning Emissions  ...  ACKNOWLEDGMENT The authors would like to thank NOAA NESDIS for the operational transition and distribution of WF_ABBA.  ... 
doi:10.1109/jstars.2009.2027443 fatcat:syw4k4jppffajm3exuahlmsvje

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Foerster, S  ...  ., +, JSTARS April 2014 1116-1126 Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field.  ...  Ling, F., +, JSTARS May 2014 1816-1825 Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Terrestrial ecosystem model studies and their contributions to AsiaFlux

Akihiko ITO, Kazuhito ICHII
2021 Journal of Agricultural Meteorology  
The development and use of data-driven (statistical and machine learning) models has further enhanced the utilization of field survey and satellite remote sensing data.  ...  Model intercomparison studies were also conducted by using the AsiaFlux dataset for uncertainty analyses and benchmarking.  ...  of Environment, and the Environmental Restoration and Conservation Agency, Japan.  ... 
doi:10.2480/agrmet.d-20-00024 fatcat:wbytwszntrhnlf2ooovyhsvpcm

An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China

Yanlin Wei, Xiaofeng Li, Li Li, Lingjia Gu, Xingming Zheng, Tao Jiang, Xiaojie Li
2022 Remote Sensing  
Accurate snow depth (SD) data are important for research on hydrologic processes, climate change, and natural disaster prediction.  ...  For other SD products (WESTDC and FY), the multifactor SD downscaling model still has good applicability, it could further improve the performance of the WESTDC and FY SD products in time and space and  ...  Acknowledgments: The authors would like to thank the Science and Technology Basic Resources Survey Project of China for providing snow test data for this article.  ... 
doi:10.3390/rs14061480 fatcat:6ed6nhiuybgx3j6c43edoow6ra

An overview of MATISSE-v2.0

Luc Labarre, Karine Caillault, Sandrine Fauqueux, Claire Malherbe, Antoine Roblin, Bernard Rosier, Pierre Simoneau, Karin Stein, John D. Gonglewski
2010 Optics in Atmospheric Propagation and Adaptive Systems XIII  
This Programme is based on commitments received up to the time of publication and is subject to change without notice.  ...  The symposium, like our other conferences and activities, would not be possible without the dedicated contribution of our participants and members.  ...  Acknowledgement: Thanks to Total E&P Norge AS for funding this study, thanks to NOFO for letting us participate in the exercise, and thanks to KSAT, InfoTerra and The Norwegian Meteorological Institute  ... 
doi:10.1117/12.868183 fatcat:5anlqspzzzcftfufaxvlr45zve

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Gianpaolo Balsamo, Anna Agustì-Parareda, Clément Albergel, Gabriele Arduini, Anton Beljaars, Jean Bidlot, Nicolas Bousserez, Souhail Boussetta, Andy Brown, Roberto Buizza, Carlo Buontempo, Frédéric Chevallier (+33 others)
2018 Remote Sensing  
In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling.  ...  This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications.  ...  Adrian Simmons and Alan Betts are thanked for their precious advice and review of early versions of this manuscript.  ... 
doi:10.3390/rs10122038 fatcat:qovifbs5crgbhhpwolysj4tuya

LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion

Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, Di Han
2022 Earth System Science Data  
Specifically, data gaps in daily AOD imageries from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on a set of AOD data tensors acquired from diverse satellites  ...  approach.  ...  On the one hand, the operational AOD product from the recent Chinese FY-4 satellite is still unavailable.  ... 
doi:10.5194/essd-14-907-2022 fatcat:lpl7g6rtibarpig76ko4xy5dem

Ice Fog in Arctic During FRAM–Ice Fog Project: Aviation and Nowcasting Applications

I. Gultepe, T. Kuhn, M. Pavolonis, C. Calvert, J. Gurka, A. J. Heymsfield, P. S. K. Liu, B. Zhou, R. Ware, B. Ferrier, J. Milbrandt, B. Bernstein
2014 Bulletin of The American Meteorological Society - (BAMS)  
beam (0.780 µm, not visible) to detect the ice crystals and snow particles GOES-R APPROACH TO FOG/LOW CLOUD (also for droplets).  ...  An Aqua MODIS overpass at 1030 UTC 18 Dec 2010 is used to demonstrate cloud phase and low cloud base detection algorithms developed for GOES-R. (a) A false color image.  ... 
doi:10.1175/bams-d-11-00071.1 fatcat:f2xcu4ne6vdfnksoocwx2ejz5u

The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas

Saulo R. Freitas, Jairo Panetta, Karla M. Longo, Luiz F. Rodrigues, Demerval S. Moreira, Nilton E. Rosário, Pedro L. Silva Dias, Maria A. F. Silva Dias, Enio P. Souza, Edmilson D. Freitas, Marcos Longo, Ariane Frassoni (+24 others)
2017 Geoscientific Model Development  
</strong> We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS), in which different previous versions for weather, chemistry, and carbon cycle were  ...  The description of the main model features includes several examples illustrating the quality of the transport scheme for scalars, radiative fluxes on surface, and model simulation of rainfall systems  ...  based on satellite remote sensing fire detections .  ... 
doi:10.5194/gmd-10-189-2017 pmid:32818049 pmcid:PMC7430531 fatcat:y2pkwj555bbvrc3ca5ipdigedu
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