Short-Term Probabilistic Forecasting of Irrigation Need Based on Relative Soil-Moisture-Deficit Persistence Using Copula Models

Soil-moisture deficits affect irrigation management in arid irrigated systems, yet their short-term persistence and uncertainty are difficult to quantify at the field scale. This study developed a spatially explicit probabilistic framework combining high-resolution root-zone soil-moisture estimation with Copula-based dependence modeling to quantify relative soil-moisture-deficit persistence in the Daheyanzi Irrigation District, Xinjiang, China. Using Google Earth Engine, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model generated daily vegetation-index and land-surface-temperature products at 30 m resolution, while a Random Forest model estimated daily 0–40 cm soil moisture. A percentile-based relative soil-moisture-deficit index (RSDI) was constructed, and four Copula models were evaluated. The Frank Copula provided the best statistical fit. Kendall’s τ decreased from 0.505 at a 1-day lead to 0.085 at a 7-day lead, indicating that persistence information was concentrated mainly within 1–3 days. When current RSDI exceeded 0.80, the probabilities of future RSDI exceeding 0.90 were 28.0% and 19.0% at 1- and 3-day leads, respectively. Rolling-window validation showed the clearest predictive advantage at 1 day, with improved discrimination at 3 days. The framework provides spatially explicit deficit probabilities to support irrigation-need assessment and field prioritization when short-term irrigation-water availability is limited.