Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Soil moisture time series, SCAN site 2015, New Mex- ico (USA), actual soil moisture (blue line), diagnostic soil moisture equation estimate (red line), and diagnostic soil

In this study, independent estimates of soil moisture derived from passive microwave (AMSR-E; LPRM), thermal infrared (ALEXI), and model- based (Noah) methods were cross-compared

soil moisture, soil temperature, net radiation and heat fluxes (latent, sensible and ground heat fluxes) at land surface. Temperature and moisture of the surface 2.5 cm soil

Using satellite-based MODIS data (land surface temperature data, EVI, etc.), and ground-based on-site soil moisture data and meteorological data (air tempera- ture, relative

However, in case of observations with uncertainties <4 vol.%, the obtained model predictions are in considerable agreement with those obtained from the open loop simulations

To assess the usefulness of coarse resolution soil moisture data for catchment scale modelling, scatterometer derived soil moisture data was compared to hydrometric measure-

Land data assimilation systems (LDAS) are needed to integrate satellite data providing information about land state variables such as the surface soil moisture (SSM) and leaf area

Three different data products from the SMOS mission are assimilated separately into the GEOS-5 land surface model to improve estimates of surface and root-zone soil moisture and