Retrieving root-zone soil moisture from land surface modelling and GRACE/-FO and validating its dynamics with in-situ data over West Africa

Rainfall variability in West Africa, driven by the West African Monsoon, poses significant challenges to agricultural productivity and livelihoods. In this context, understanding root-zone soil moisture (RZSM) dynamics is crucial since it serves as the primary water source for crops. While surface soil moisture (SSM) has been widely studied, research on RZSM remains limited. This study investigates RZSM dynamics across West Africa from 2003 to 2019 using multiple satellite-derived and model-based datasets, including ESA CCI v0.81, GLWS2.0, WaterGAP, CLM5.0, and in-situ observations. Results indicate that ESA CCI exhibits the strongest temporal and spatial alignment with ground measurements, whereas CLM5.0 and GLWS2.0 effectively capture latitudinal soil moisture gradients associated with climatic zones. A novel application of an analytical solution to Richards' equation was employed to translate surface moisture signals to deeper soil layers, demonstrating GLWS2.0's superior ability to reproduce seasonal patterns at various depths, notably in Benin and Niger. Despite challenges posed by sparse in-situ data and vegetation-induced signal attenuation, the study highlights the significant benefits of GRACE/-FO data assimilation in enhancing model accuracy. The proposed depth-projection methodology improves the vertical representation of soil moisture, offering new insights into the dynamics of surface and subsurface water storage. These findings have important implications for agricultural forecasting, sustainable water resource management, and climate adaptation strategies in regions where accurate soil moisture data are essential for resilience planning.