We developed a sensitivity analysis algorithm to test all possible combinations of inputs parameters (i.e., spectral indices, maximum clustering, and regularization) for Water Detect within a specified range and assessed accuracy, determining the most accurate inputs for each specific case, and producing a most-to-least accuracy ranking. The developed sensitivity algorithm has four main inputs: 1) the Water Detect default initialization file as per Cordeiro, Martinez, and Peña-Luque (2021); 2) the range of maximum clustering and regularization given by lowest, highest, and step values; 3) the images to be tested, and 4) the ground truth raster to be used in the accuracy assessment.
Tayer T.C., Douglas M.M., Cordeiro M.C.R., Tayer A.D.N., Callow J.N., Beesley L. & McFarlane D. (2023) Improving the accuracy of the Water Detect algorithm using Sentinel-2, Planetscope and sharpened imagery: a case study in an intermittent river, GIScience & Remote Sensing, 60:1, DOI: 10.1080/15481603.2023.2168676
Tayer T.C., Douglas M.M., Cordeiro M.C.R., Tayer A.D.N., Callow J.N., Beesley L. & McFarlane D. (2023) Improving the accuracy of the Water Detect algorithm using Sentinel-2, Planetscope and sharpened imagery: a case study in an intermittent river, GIScience & Remote Sensing, 60:1, DOI: 10.1080/15481603.2023.2168676
Cordeiro, M. C. R.; Martinez, J.-M.; Peña-Luque, S. Automatic Water Detection from Multidimensional Hierarchical Clustering for Sentinel-2 Images and a Comparison with Level 2A Processors. Remote Sensing of Environment 2021, 253, 112209. https://doi.org/10.1016/j.rse.2020.112209.