Grillakis et al. (2021) Regionalizing Root‐Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. This study addresses the limitation of shallow sensing depth (2–5 cm) in the ESA CCI soil moisture dataset by developing a methodology to estimate root-zone soil moisture (RZSM). By calibrating the Soil Water Index (SWI) using in situ observations and leveraging machine learning techniques with global physical descriptors, the researchers successfully derived RZSM for the period 2001–2018, demonstrating good agreement with established reanalysis products like ERA5 Land, particularly over mid-latitudes....
