H31G:
Machine Learning Applications in Earth Science and Remote Sensing III Posters

Submit an Abstract to this Session



Session ID#: 22660

Session Description:
Remote sensing technology has facilitated data acquisition over large and small spatio-temporal scales resulting in valuable large datasets. Present advances in technology with availability of large computing and scalable storage allow us to explore these datasets in a unique way to use data-driven techniques to learn patterns, understand physical processes, and characterize feedbacks in the Earth system. Among the many fields within data exploration, Machine Learning (ML) has had a major impact on not only scientific thinking but also in commercial applications. ML's advantages include, application flexibility and scaling, fast running time, and ability to represent complex relationships from historical data in conjunction with physics-guided processes. This session is a platform to discuss advancements in the applications of ML in Earth science and remote sensing as well as challenges and future opportunities for ML applications. Presentations will address the merger of discrete mathematics, statistics, physics, and non-linear optimization techniques.
Primary Convener:  Hamed Alemohammad, Radiant Earth Foundation, Washington, United States
Conveners:  Pierre Gentine, Columbia University, Earth Institute, New York, United States, Joel Agustin Gongora, Boise State University, Geosciences, Boise, ID, United States and Sangram Ganguly, NASA Ames Research Center, Moffett Field, CA, United States
Chairs:  Hamed Alemohammad, Radiant Earth Foundation, Washington, United States and Joel Agustin Gongora, Boise State University, Geosciences, Boise, ID, United States
OSPA Liaison:  Joel Agustin Gongora, Boise State University, Geosciences, Boise, ID, United States
Co-Organized with:
Hydrology, Atmospheric Sciences, Biogeosciences, Cryosphere, and Global Environmental Change

Cross-Listed:
  • A - Atmospheric Sciences
  • B - Biogeosciences
  • C - Cryosphere
  • GC - Global Environmental Change
Index Terms:

0430 Computational methods and data processing [BIOGEOSCIENCES]
1622 Earth system modeling [GLOBAL CHANGE]
1855 Remote sensing [HYDROLOGY]
3399 General or miscellaneous [ATMOSPHERIC PROCESSES]

Abstracts Submitted to this Session:

Predicting Power Outages Using Multi-Model Ensemble Forecasts (239175)
Diego Cerrai, University of Connecticut, School of Civil and Environmental Engineering, Storrs, United States, Emmanouil N Anagnostou, Professor, Civil and Environmental Engineering, Storrs, United States, Jaemo Yang, University of Connecticut, Civil & Environmental Engineering, Storrs, CT, United States and Marina Astitha, University of Connecticut, School of Civil and Environmental Engineering, Storrs-Mansfield, United States
Random Forest Application for NEXRAD Radar Data Quality Control (255946)
Munsung Keem, University of Iowa, Iowa City, IA, United States, Dr. Bong Chul Seo, Ph.D., University of Iowa, Iowa City, United States and Witold F Krajewski, University of Iowa, Iowa Flood Center and IIHR-Hydroscience and Engineering, Iowa City, United States
Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent LER Calculations (259832)
Zachary Fasnacht1, Wenhan Qin2, David P Haffner3, Diego G Loyola4, Joanna Joiner5, Nickolay Anatoly Krotkov6, Alexander P Vasilkov1 and Robert J D Spurr7, (1)Science Systems and Applications, Inc., Lanham, MD, United States, (2)SSAI, Lanham, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany, (5)NASA GSFC, Greenbelt, United States, (6)Code 614, Greenbelt, MD, United States, (7)RT Solutions, Inc., Cambridge, MA, United States
Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations (265450)
Md Abul Ehsan Bhuiyan, University of Connecticut, Natural Resources and the Environment, Groton, CT, United States, Efthymios Ioannis Nikolopoulos, University of Connecticut, Civil and Environmental Engineering, Groton, CT, United States and Emmanouil N Anagnostou, Professor, Civil and Environmental Engineering, Storrs, United States
Application of deep learning in determining IR precipitation occurrence: a Convolutional Neural Network model (267658)
Cunguang Wang and Yang Hong, Tsinghua University, Department of Hydraulic Engineering, Beijing, China
Retrieval of Surface Ozone from UV-MFRSR Irradiances using Deep Learning (280605)
Maosi Chen1, Zhibin Sun2, John Davis3, Melina Zempila3, Chaoshun Liu4 and Wei Gao1, (1)Colorado State University, UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Fort Collins, United States, (2)Colorado State University, Fort Collins, CO, United States, (3)Colorado State University, Natural Resource Ecology Laboratory, Fort Collins, CO, United States, (4)Key Laboratory of Geographic Information Science, Ministry of Education, East China University, Shanghai, China
MODIS Snow Cover Recovery Using Variational Interpolation (286331)
Hoang Tran1, Phu Nguyen2, Kuo-lin Hsu3 and Soroosh Sorooshian3, (1)Colorado School of Mines, Department of Geology and Geological Engineering, Golden, United States, (2)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (3)UC Irvine, Irvine, CA, United States
Estimation of MODIS-like Surface-Spectral Reflectance from Geostationary Satellites using Deep Neural Networks (289090)
Matthew Mage1, Sangram Ganguly2, Thomas Vandal1, Ramakrishna R Nemani3, Shuang Li3, Subodh Kalia4 and Auroop R Ganguly5, (1)Northeastern University, Boston, MA, United States, (2)Bay Area Environmental Research Institute Moffett Field, Moffett Field, CA, United States, (3)NASA Ames Research Center, Moffett Field, CA, United States, (4)BAER Institute, Mountain View, CA, United States, (5)Northeastern University, Civil and Environmental Engineering Department, Boston, United States
Machine Learing Applications on a Radar Wind Profiler Deployment During the ARM GoAmazon2014/5 Campaign (290123)
Scott E Giangrande1, Dié WANG2, Joseph Clinton Hardin3 and Jeffery Mitchell2, (1)Brookhaven National Laboratory, Environmental Science and Technologies Department, Upton, United States, (2)Brookhaven National Laboratory, Upton, NY, United States, (3)Pacific Northwest National Laboratory, Richland, United States
A Machine Learning-based Rainfall System for GPM Dual-frequency Radar  (290402)
Haiming Tan, Colorado State University, Fort Collins, CO, United States, V Chandrasekar, Cooperative Institute for Research in the Atmosphere, Fort Collins, United States and Haonan Chen, Colorado State University and NOAA Physical Sciences Laboratory, Boulder, United States
DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites (292774)
Subodh Kalia1, Shuang Li1, Sangram Ganguly2 and Ramakrishna R Nemani1, (1)NASA Ames Research Center, Moffett Field, CA, United States, (2)Bay Area Environmental Research Institute Moffett Field, Moffett Field, CA, United States
Quantifying Melt Ponds in the Beaufort MIZ using Linear Support Vector Machines from High Resolution Panchromatic Images (294772)
Macarena Ortiz, University of Miami, Miami, FL, United States, Hans Christian Graber, University of Miami, Center for Southeastern Tropical Advanced Remote Sensing, Miami, United States, Jeremy Wilkinson, British Antarctic Survey, Cambridge, United Kingdom, Lisa Marie Nyman, University of Miami RSMAS, Ocean Sciences, Miami, FL, United States and Bjoern Lund, Univ of Miami, Miami, FL, United States

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