A global long-term daily multilayer soil moisture dataset derived from machine learning

Soil moisture is a critical component of the Earth's energy and water cycles. However, most existing products focus solely on surface layers, and continuous, high-resolution datasets for deep soil horizons remain scarce. To address this gap, we generated a global, daily, seamless multilayer soil moisture dataset (SWSM) for the period 2002-2021 by leveraging a machine learning approach (XGBoost). The SWSM dataset provides estimates at a 0.05° spatial resolution for three depth horizons: 0-10 cm, 10-30 cm, and 30-60 cm. Rigorous validation against in situ observations demonstrated the dataset's high accuracy, with Pearson correlation coefficients exceeding 0.90 and root mean square errors below 0.05 across all depths. A feature importance assessment verified the dataset's physical consistency, revealing depth-dependent patterns aligned with established hydrological understanding. The SWSM dataset, with its long-term temporal coverage, fine spatial resolution, and multi-layer structure, is a valuable resource for applications in hydrologic modeling, agricultural water management, and climate change studies.