H33A:
Applications of Machine Learning and Data Cubes in Remote Sensing I


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Session ID#: 62349

Session Description:
Rich satellite observations over large spatio-temporal scales provide invaluable big datasets for monitoring the Earth and its changing environment. Advancements in data-driven techniques, in particular machine learning (ML), enables us to harness these datasets and advance geoscience and environmental sciences in unique and unprecedented ways. Such applications include scientific discoveries that lead to new findings and practical applications that lead to improved decision making. Furthermore, development of new data hosting technologies such as Data Cubes makes it easier to apply ML and time series analysis at larger spatial scales on the cloud computing platforms or supercomputers. This session solicits recent advancements of using ML and remote sensing for applications in geoscience and environmental sciences and opportunities that data cubes provide. We welcome presentations in either technology advancements or application-driven case studies in the geoscience context. We also welcome presentations that address challenges in generating representative training data for ML algorithms.
Primary Convener:  Hamed Alemohammad, Radiant Earth Foundation, Washington, United States
Conveners:  Steven Brumby, Descartes Labs, Inc., Los Alamos, United States; World Resources Institute, Washington, DC, United States, Brian Killough, NASA Langley Research Center, Hampton, VA, United States and Kaiyu Guan, University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States
Primary Liaison:  Hamed Alemohammad, Radiant Earth Foundation, Washington, United States
Chairs:  Hamed Alemohammad, Radiant Earth Foundation, Washington, United States, Brian Killough, NASA Langley Research Center, Hampton, VA, United States, Kaiyu Guan, University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States and Steven Brumby, Descartes Labs, Inc., Los Alamos, United States
OSPA Liaison:  Kaiyu Guan, University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States
Co-Organized with:
Hydrology, and Earth and Space Science Informatics

Cross-Listed:
  • A - Atmospheric Sciences
  • B - Biogeosciences
  • GC - Global Environmental Change
  • IN - Earth and Space Science Informatics

Proposed Co-Organized Session with:
  • B - Biogeosciences
  • GC - Global Environmental Change
  • GH - GeoHealth
  • IN - Earth and Space Science Informatics
Index Terms:

0434 Data sets [BIOGEOSCIENCES]
1855 Remote sensing [HYDROLOGY]
1926 Geospatial [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

Reconstructed Solar-induced Fluorescence - a machine learning approach to vegetation productivity (Invited) (362075)
Pierre Gentine, Columbia University, Department of Earth and Environmental Engineering, New York, United States, Hamed Alemohammad, Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States and Yao Zhang, University of Oklahoma Norman Campus, Norman, OK, United States
Harnessing Advances in Earth Observation and Machine Learning for Agricultural Informatics (Invited) (350857)
Matthew F McCabe, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia and Rasmus Houborg, South Dakota State University, Geospatial Sciences Center of Excellence (GSCE), Brookings, SD, United States
Using a Multi-Temporal Data Cube with Random Forest near real-time Classification of Major Crops in USA (439665)
Beth L Ziniti, Applied Geosolutions, LLC, Durham, NH, United States, Nathan Torbick, Mitti Labs Limited, Mumbai, India, Xiaodong Huang, Applied Geosolutions, Newmarket, NH, United States, David Johnson, USDA National Agricultural Statistics Service, Washington, DC, United States, Jeffrey G Masek, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Michele L Reba, USDA, ARS, Delta Water Management Research Unit, Jonesboro, AR, United States
Un-Mixing Satellite Solar-Induced Chlorophyll Fluorescence (SIF) For Agricultural Applications Using Artificial Neural Network (408074)
Oz Kira, Cornell University, School of Integrative Plant Science, Soil and Crop Sciences Section, Ithaca, NY, United States and Ying Sun, Cornell University, School of Integrative Plant Science, Soil and Crop Sciences Section, Ithaca, United States
Mapping Soil Organic Matter at Scale from Remote Sensing and Environmental Conditions using Machine Learning (440625)
Ángeles Casas Planes, Climate Corporation San Francisco, San Francisco, CA, United States and Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States

See more of: Hydrology