  Groundwater depletion impacts the sustainability of numerous
groundwater-dependent vegetation (GDV) globally, placing significant stress on
their capacity to provide environmental and ecological support for flora,
fauna, and anthropic benefits. Industries such as mining, agriculture, and
plantations are heavily reliant on groundwater, the over-exploitation of which
risks impacting groundwater regimes, quality, and accessibility for nearby
GDVs. Cost effective methods of GDV identification will enable strategic
protection of these critical ecological systems, through improved and
sustainable groundwater management by communities and industry. Recent
application of synthetic aperture radar (SAR) earth observation data in
Australia has demonstrated the utility of radar for identifying terrestrial
groundwater-dependent ecosystems at scale. We propose a robust classification
method to advance identification of GDVs at scale using processed SAR data
products adapted from a recent previous method. The method includes the
development of SARGDV, a binary classification model, which uses the extreme
gradient boosting (XGBoost) algorithm in conjunction with three data cubes
composed of Sentinel-1 SAR interferometric wide images. The images were
collected as a one-year time series over Mount Gambier, a region in South
Australia, known to support GDVs. The SARGDV model demonstrated high
performance for classifying GDVs with 77% precision, 76% true positive rate and
96% accuracy. This method may be used to support the protection of GDV
communities globally by providing a long term, cost-effective solution to
identify GDVs over variable regions and climates, via the use of freely
available, high-resolution, globally available Sentinel-1 SAR data sets.
