Accurate profiling of deep (20–300 cm) soil moisture is crucial for precision irrigation but remains technically challenging and costly at operational scales. We systematically benchmark eight regression algorithms—including linear regression, Lasso, Ridge, elastic net, support vector regression, multi-layer perceptron (MLP), random forest (RF), and gradient boosting trees (GBDT)—that use easily accessible inputs of 0–20 cm surface soil moisture (SSM) and ten meteorological variables to non-invasively infer soil moisture at fourteen 20 cm layers. Data from a typical agricultural site in Wenxi, Shanxi (2020–2022), were divided into training and testing datasets based on temporal order (2020–2021 for training, 2022 for testing) and standardized prior to modeling. Across depths, non-linear ensemble models significantly outperform linear baselines. Ridge Regression achieves the highest accuracy at 0–20 cm, SVR performs best at 20–40 cm, and MLP yields consistently optimal performance across deep layers from 60 cm to 300 cm (R² = 0.895–0.978, KGE = 0.826–0.985). Although ensemble models like RF and GBDT exhibit strong fitting ability, their generalization performance under temporal validation is relatively limited. Model interpretability combining SHAP, PDP, and ALE shows that surface soil moisture is the dominant predictor across all depths, with a clear attenuation trend and a critical transition zone between 160 and 200 cm. Precipitation and humidity primarily drive shallow to mid-layers (20–140 cm), whereas temperature variables gain relative importance in deeper profiles (200–300 cm). ALE analysis eliminates feature correlation biases while maintaining high predictive accuracy, confirming surface-to-deep information transmission mechanisms. We propose a depth-adaptive modeling strategy by assigning the best-performing model at each soil layer, enabling practical non-invasive deep soil moisture prediction for precision irrigation and water resource management.

Soil profile moisture (0–200 cm) in agricultural fields is a critical variable determining root-zone water storage and irrigation scheduling accuracy, yet continuous deep-layer monitoring is constrained by equipment costs and installation difficulties. This study developed a near-real-time reconstruction model for soil moisture profiles across the 0–200 cm depth based on shallow-layer (0–20 cm, 20–40 cm) real-time monitoring data and multi-day accumulated meteorological features. Using field measurements from 2023 to 2025, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR) models were compared across different input scenarios and cumulative time windows. The results showed that using only surface moisture as input (Scenario A), prediction R² ranged from 0.87 to 0.93 for shallow layers (≤80 cm) but decreased to 0.58 for deep layers (140–200 cm). Incorporating multi-day meteorological accumulation (Scenario B) improved R² by 0.05–0.08. When dual-layer moisture and meteorological drivers were combined (Scenario D), shallow-layer R² reached 0.96–0.98 with RMSE < 7 mm, mid-layer performance maintained at 0.85–0.90, and deep layers still achieved 0.76–0.84. Optimal time windows exhibited depth-dependent patterns: 5–10 days for shallow layers, 10–15 days for mid-layers, and ≥20 days for deep layers. Rolling validation demonstrated high consistency between model predictions and observations in the 0–80 cm range (R² > 0.90, RMSE < 10 mm), enabling stable estimation of 0–200 cm profile dynamics. This approach eliminates the need for deep probes while achieving low-cost, interpretable, and deployable near-real-time deep moisture estimation, providing an effective technical pathway for precision irrigation and water management in semi-arid regions.