The lack of proper understanding of multi-layer soil moisture (SM) profile (signals) remains a persistent challenge in sustainable agricultural water management and food security, especially during drought conditions. We develop a machine-learning algorithm using the concept of learning from patterns to estimate the multi-layer SM information in ungauged locations firmly based on local knowledge of the climatic and landscape controls. The Contiguous United States (CONUS) is clustered into homogeneous regions based on the association between SM and climate and landscape controls. Extreme Gradient Boosting (XGBoost) algorithm is applied to homogenous regions to capture the complex relationship between appropriate predictor variables and in-situ SM at multiple layers over the CONUS. Soil Moisture Active Passive (SMAP) Level 4 (L4) surface (0–5 cm) and rootzone (0–100 cm) SM along with climate and landscape datasets are used as predictor variables. In-situ multi-layer SM recorded by Soil Climate Analysis Network (SCAN), Snow Telemetry (SNOTEL), and U.S. Climate Reference Network (USCRN) networks are utilized as predictands. XGBoost models are then trained region-wise and layer-wise to estimate multi-layer SM information at 5, 10, 20, 50, and 100 cm depths (five layers) at 1-km spatial resolution. Results indicate that the predictor variables have varying levels of influence on SM with changing soil depth, and meteorological variables have the least importance. Validation at 79 independent locations indicates the multi-layer SM estimates successfully capture temporal dynamics of SM, with most locations achieving ubRMSE less than 0.04 m³/m³. The high-resolution SM estimates offer spatial sub-grid heterogeneity compared to SMAP L4 SM.

缺乏对多层土壤水分 (SM) 剖面（信号）的正确理解仍然是可持续农业水资源管理和粮食安全的持续挑战，尤其是在干旱条件下。我们使用从模式中学习的概念开发了一种机器学习算法根据当地气候和景观控制的知识，可靠地估计未测量位置的多层 SM 信息。根据 SM 与气候和景观控制之间的关联，美国本土 (CONUS) 被聚类为同质区域。极限梯度提升 (XGBoost) 算法应用于同质区域，以捕获 CONUS 上多个层的适当预测变量与原位 SM 之间的复杂关系。土壤水分主动被动 (SMAP) 4 级 (L4) 表面（0-5 厘米）和根区（0-100 厘米）SM 以及气候和景观数据集被用作预测变量。由土壤气候分析网络 (SCAN)、雪遥测 (SNOTEL) 和美国气候参考网络 (USCRN) 网络记录的原位多层 SM 被用作预测对象。然后对 XGBoost 模型进行区域和分层训练，以 1 公里空间分辨率估计 5、10、20、50 和 100 厘米深度（五层）的多层 SM 信息。结果表明，随着土壤深度的变化，预测变量对 SM 的影响程度不同，气象变量的重要性最低。在 79 个独立位置的验证表明多层 SM 估计成功捕获了 SM 的时间动态，大多数位置实现了小于 0.04 m 的 ubRMSE³ /米³。与 SMAP L4 SM 相比，高分辨率 SM 估计提供了空间子网格异质性。