Keywords: Tutorial, Airborne, Data Management, Python, SAR
On May 4th and 5th, 2022, the Delta-X Science Team developed and conducted a Delta-X Applications Workshop which was held virtually and in person at The Estuary at the Water Campus Baton Rouge, Louisiana. In this two-day workshop, the scientists covered an introduction to Delta-X datasets and steps for analyzing field, airborne, and modeling datasets. Scientists presented material in the form of lecture presentation, hands-on data access demonstrations, and data analysis methods tutorials mostly in the form of Jupyter Notebook content. The Delta-X Science Team has provided videos of presentations, slide content, and Notebook material. That material is organized and available from the ORNL DAAC from the Workshop Content repository link below. The ORNL DAAC archives and distributes datasets from the Delta-X EVS-3 Mission. Read more about the mission at the Delta-X website.
- Delta-X Overview - Marc Simard
- Data Management Plan & Data Archive - Cathleen Jones
- Field Data Overview, Access & Analysis - Alex Christensen
- AVIRIS-NG Data Overview, Access & Application - Daniel Jensen
- AirSWOT Data Overview, Access & Application - Michael Denbina
- AirSWOT Application - Michael Denbina
- UAVSAR Data Overview, Access & Application - Talib Oliver Cabrera
- ANUGA Model - Kyle Wright
- Delft3D Model - Luca Cortese
ORNL DAAC Project Page
The Delta-X mission is a 5-year NASA Earth Venture Suborbital-3 mission to study the Mississippi River Delta in the United States, which is growing and sinking in different areas. River deltas and their wetlands are drowning as a result of sea level rise and reduced sediment inputs. The Delta-X mission will determine which parts will survive and continue to grow, and which parts will be lost. Delta-X begins with airborne and in situ data acquisition and carries through data analysis, model integration, and validation to predict the extent and spatial patterns of future deltaic land loss or gain.
Related Learning Resources
More tutorials related to ORNL DAAC data and web services can be found on the ORNL DAAC's Learning page.