Abstract Soil moisture (SM) is a key water source for vegetation, directly determining the success of revegetation efforts in water‐limited regions. However, accurate profile SM data remain lacking for the Chinese Loess Plateau (LP), limiting ecological restoration efforts. (a) Direct field measurements provide highly accurate SM information but are restricted to point scales. (b) Satellite remote sensing offers full regional coverage but is limited to shallow depths (∼5 cm). (c) Land surface models can estimate regional soil profiles, yet their accuracy is often constrained by insufficient parameter calibration. To address these limitations, we integrated satellite‐derived leaf area index (LAI) data through data assimilation (DA), incorporated measured runoff parameters and in situ measured soil hydraulic parameters into the Noah‐MP model, thereby improving SM simulations across the LP. Results show that calibration with field data increased SM simulation accuracy by improving correlation by 24% and reducing error by 15%. Compared with mainstream data sets, the model also reduced overestimation by 13%. Further analysis of model outputs revealed that soil available water (SAW) is more strongly correlated with LAI than SM, and that the inverse texture effect in loam soils deviates from the conventional understanding. This deviation is likely due to concurrent changes in vegetation composition and soil hydraulic properties since the implementation of revegetation policies on the LP. Based on these findings, we recommend regulating the scale of revegetation on loam soils, selecting suitable plant species, and continuously monitoring SAW to sustain SM balance and ensure long‐term ecosystem restoration in the region.
Zhou et al. (Fri,) studied this question.