Improving the accuracy of the Water Detect algorithm using Sentinel-2, Planetscope and sharpened imagery: a case study in an intermittent river

Mapping surface water using remotely sensed optical imagery is a particular challenge in intermittent rivers because water contracts down to narrow linear features and isolated pools, which require accurate water detection methods and reliable image datasets. Of the many methods that use optical sensors to identify water, the Water Detect algorithm stands out as one of the best options due to its classification accuracy, open-source code, and because it does not require ancillary data. However, in the original study, the Water Detect algorithm was only tested with Sentinel-2 imagery. High-resolution and high-frequency imagery, such as Planetscope, combined with sharpening and band synthesizing techniques have the potential to improve the accuracy of surface water mapping, but their benefit to the Water Detect algorithm remains unknown. Uncertainty also exists about the extent to which different input parameters (i.e. maximum clustering and regularization) influence the accuracy of Water Detect. Practitioners seeking to map surface water in intermittent rivers need guidance on a best-practice approach to improve the accuracy of Water Detect. To meet this need, we automated an existing method for sharpening and synthesizing bands and applied it to a series of multispectral Sentinel-2 and Planetscope images. We then developed a sensitivity analysis algorithm that compared the accuracy for all possible combinations of input parameters in a given range for the water detection process - enabling optimal parameters to be identified. We applied this workflow to an 81 km stretch of the lower Fitzroy River (Western Australia) to periods when spatial water extent varied markedly, i.e. mid-wet (February), early-dry (June), and late-dry season (October), across three years with variable wet season flow. We found that the ability to accurately detect surface water using multispectral imagery was increased by using input parameters identified by the sensitivity analysis and using Visible + Near-infrared (VNIR) bands, with relatively little gained by image sharpening unless the area of interest was burnt or experienced considerable shading. Also, the regularization parameter exerted less influence on results than maximum clustering. Importantly, the accuracy of the Water Detect algorithm can vary drastically if input parameters are not calibrated to local conditions. Results also revealed that our approach was adept at detecting linear features in intermittent rivers. We recommend that practitioners using Water Detect to identify surface water undertake a workflow similar to that described here to improve the accuracy of the Water Detect algorithm. The automated routines provided by this study will significantly assist practitioners in doing so. Increasing the accuracy with which we detect and map water in intermittent rivers will improve our understanding and management of these important systems which are under increasing threat.