Improving AquaCrop Root‐Zone Soil Moisture by Assimilating SMAP Surface Soil Moisture
Michel Bechtold, Louise Busschaert, Zdenko Heyvaert, Sujay Kumar, Dirk Raes, Christian Massari, Gabriëlle J. M. De LannoyAbstract
Root‐zone soil moisture (RZSM) strongly controls crop development in process‐based crop models, yet most satellite missions observe only near‐surface moisture. We propose a framework to assimilate soil moisture active passive (SMAP) Level‐2 surface soil moisture retrievals into AquaCrop to improve RZSM over European croplands (0.1° resolution, 2015–2023). We assimilate either these SMAP retrievals directly, or exponentially filtered SMAP retrievals optimized for specific target depths (0–30, 0–60, and 0–100 cm), always bias‐corrected using seasonally varying cumulative distribution function matching within an ensemble Kalman filter. AquaCrop's cascading, threshold‐based soil water balance exhibits much weaker short‐term vertical coupling than in situ observations, limiting the downward propagation of surface‐only updates. Consistent with this behavior, assimilation of both surface retrievals and exponentially filtered SMAP data improves topsoil moisture (0–30 cm), while subsoil (30–100 cm) and RZSM (0–100 cm) improve most when assimilating exponentially filtered observations that embed temporal persistence of deeper soil depths. Best overall RZSM performance is obtained by assimilating SMAP data exponentially filtered to a target depth of 0–60 cm, yielding widespread increases in anomaly correlation against in situ observations. Improvements in soil moisture translate only weakly and inconsistently to vegetation space: updated soil moisture better explains interannual variability in satellite vegetation indicators but improvements in canopy cover and biomass simulated by AquaCrop remain limited. These results indicate that while exponential‐filter‐based soil moisture assimilation robustly improves deeper moisture, joint assimilation of soil moisture and vegetation observations is needed to better constrain soil–plant coupling and ensure that soil moisture corrections propagate more effectively into crop growth predictions.