Abstract
Abstract. TU Wien’s flood mapping algorithm, used for global operations, utilizes harmonic functions to model the seasonal behavior of backscatter to improve flood classification. In earth observation (EO), temporal harmonic models have been used in various scenarios for vegetation and water mapping in the optical and, recently, synthetic aperture radar (SAR) domains. These models condense EO time series stacks to a few Fourier coefficient images that capture seasonal variability, allowing for variable estimation for each day of the year. TU Wien’s harmonic parameters consist of these coefficients plus the regression standard deviation and number of observations. However, generating harmonic models at large scales and high resolution presents significant logistical and technical challenges. Particularly for SAR, which requires special handling due to acquisition geometry considerations, implementation on a datacube infrastructure is necessary for agile filtering in metadata, temporal and spatial dimensions. In this work, we highlight our harmonic parameter dataset and our software stack of loosely coupled Python packages, which were deployed in a high-performance computing environment to generate these parameters globally.
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