Adaptive water body detection: Integrating deep learning, normalised difference water index, and vector data for farm dam water monitoring with OmniWaterMask

作者：Nicholas F. Wright, John Duncan, Nik Callow, Sally Thompson, Richard J George · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2025 · DOI：10.1016/j.isprsjprs.2025.07.007 · 被引用次数：7 · 研究领域：Flood Risk Assessment and Management、Hydrology and Sediment Transport Processes、Hydrology and Watershed Management Studies

Farm dams are important water security features supporting both agricultural production and the natural environment. In Australia alone, over two million farm dams provide the water resources underpinning rural and regional primary industries with an annual export value of $80 billion. However, monitoring these water bodies to understand water security and vulnerability is challenging, primarily because of their large quantity, size and highly variable spectral signatures. These characteristics result in difficulty determining thresholds for index-based water detection methods and add to the difficulty of creating adequate training datasets for deep learning methods. We present an adaptive approach named OmniWaterMask (OWM) that uses existing mapped water features to optimise the combination of deep learning outputs and a common water index (Normalised Difference Water Index, NDWI) to achieve robust water detection, for both agricultural and other water resources. OWM demonstrates strong performance across multiple datasets and spatial scales, achieving Intersection over Union (IoU) scores of 96.9 % (Sentinel-2), 73.8 % (Landsat) and 90.9 % (National Agriculture Imagery Program, NAIP). When applied to farm dam monitoring in Western Australia using Sentinel-2 imagery, the approach successfully tracks water extent across a range of dam sizes, with Mean Absolute Error (MAE) of 587 m 2 when using Sentinel-2 and 785 m 2 when using PlanetScope. Our two case studies demonstrate the ...