Groundwater-dependent vegetation (GDV) provides vital ecological functions across arid and semiarid areas. The unsustainable depletion of groundwater, however, has led GDVs to face severe degradation, creating an urgent need for accurate identification and targeted management of these critical species. Although many methods have been applied for identifying either positions or potentials of GDVs-such as field measurements, geospatial datasets or remote sensing-the water-stable isotope (WSI) method is the only direct means of tracking water sources of plants. However, the granular depth groupings and samplings for soil WSI samples are labor-intensive and time-consuming, making it impractical for GDVs dispersedly distributed over large areas. Steady-state soil WSI profiles that exponentially decrease with depth, however, can usually be observed within a few dry days. Accordingly, this study hypothesized that soil samples located at a few points along the soil profile could determine the water sources for GDVs. A simple soil WSI sampling strategy for GDV identification was thus proposed, with equivalent pore diameter as the sole input. In practice, the equivalent pore diameter could be easily determined by either soil mechanical analysis or soil hydraulic parameters. Tests were conducted based on a steady-state exponentially decreasing estimation equation against the measurements on soil WSI profiles. It was found that explanation on soil WSI profiles could reach 70 % accuracy (R-2 > 0.7, RMSE < 7 parts per thousand), without exceeding the general variation, by limiting soil samples to two natural isotopic break points along the soil WSI profile, with one distributed near the evaporation front (Z(EF)) and another near maximum capillary rise height (h(max)). The proposed strategy creates a tradeoff between soil WSI sampling process simplification and estimation errors within an acceptable range (nRMSE < 30 %), for degraded GDVs distributed dispersedly, making for their accurate identification and effective management at diverse spatial scales, at a much lower total cost.