10-week dual capacity building feasibility study focused around data cube algorithm creation
Preliminary conversations between Kenton Ross, Sergio Camacho, and Sydney Neugebauer began in early-mid 2020 as a partnership with the NASA DEVELOP program, but the timing of the deliverable was not optimal. This deliverable will push out 2 to 3 quarters and will be part of the brainstorming discussion at the September work planning meeting.
A collaborative feasibility study was conducted by WGCapD members from NASA DEVELOP and the United Nation’s Regional Centre for Space Science and Technology Education for Latin American and the Caribbean (CRECTEALC), and the CEOS Systems Engineering Office and CSIRO team, in support of the Mexico’s National Institute of Statistics, Geography and Informatics (INEGI), to investigate the accuracy and effectiveness of different flood detection approaches in Mexico. The project compared two flood detection methods for the Mexican states of Hildalgo, Tabasco, and Chiapas: CEOS’s Open Data Cube (ODC) and NASA SERVIR’s Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods). These methods leveraged surface reflectance data and backscatter data from NASA’s Landsat 8 Operational Land Imager (OLI) sensor and the European Space Agency’s Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) sensor. The team applied the two flood detection tools to flood event case studies in the areas of interest created by the European Commission's Joint Research Centre Global Surface Water Explorer. The team found that ODC and HYDRAFloods were mostly aligned in their detection of flood waters, though ODC picked up more flooding in high-elevation areas because it does not include radiometric terrain correction. HYDRAFloods tended to show more flooding in scenes with a small amount of water due to its automated thresholding for surface water detection. The project produced a Jupyter notebook utilizing ODC, historical surface water maps, a case study analysis of the two methods, and a code video tutorial to support the expedient use of Earth observation data for disaster planning and response. To read more about this project, including the findings report, visit: https://appliedsciences.nasa.gov/what-we-do/projects/comparing-feasibility-flood-detection-methods-using-google-earth-engine-and.