Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil

The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.