Automated surface water detection from space: a Canada-wide, open-source, automated, near-real time solution

Abstract The goal of this research was to develop a fully automated method to map open water extent that is operationally practical on a national scale. Such a system needs to produce acceptable results in all regions of the country and particularly in the Prairie Potholes Region where understanding water surface dynamics is important for predicting flooding, agriculture/water availability and for evaporation calculations in weather models. A system was developed to automate, ingest, and process Radarsat-2 (RS2) imagery, from which mapping open water body extents in near-real time was carried out using a machine learning classification technique. A Random Forest classification algorithm was trained using the data extracted from the Global Surface Water (GSW) occurrence dataset. The GSW occurrence thresholds used to extract the training data were examined and there was little influence of uncertainty on the classification. The quality of classifications generated from RS2 Fine Wide mode imagery improved with increasing incidence angle. All Fine Quad incident angles produced acceptable results, but Standard and Wide mode imagery produced results below the accuracy thresholds deemed acceptable for this operational solution. Validation was carried out by comparing mapped water extents to temporally coincident high resolution multi-spectral imagery and to the USGS Global Land Cover Characteristics dataset, that is currently used as a land-water mask by ECCC in weather numerical weather modelling. The system that has been developed will allow new image datasets (e.g. Radarsat Constellation Mission) or training data that becomes available to be used to improve models. The open source code will be made available on Github.