In the past years, the scientific community has placed a special interest in remotely sensing soil moisture and vegetation parameters. Radiometry and radar techniques have been widely used for years. Global Navigation Satellite Systems opportunity signals Reflected (GNSS-R) over the earth's surface are younger, but they have already shown their potential to perform these observations. This paper presents a GNSS-R technique, based on Global Positioning System (GPS) measurements, that allows the retrieval of several geophysical parameters from land surfaces. This technique measures the power of the interference signal between the direct GPS signal and the reflected one after scattering over the land, so it is called Interference Pattern Technique (IPT). This paper presents the results obtained after applying the IPT for topography, soil moisture, and vegetation height retrievals over vegetation-covered soils.

The Level 2 soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) mission have been re- leased. The data must be validated under different scenarios of biophysical and climatic conditions. For the current study, the data from January to December 2010 from 20 in situ soil moisture stations from the REMEDHUS soil moisture measurement station network (Spain) were used. A comparison analysis was carried out in terms of the soil moisture content, its spatial variability, and temporal stability. The results show an acceptable level of agreement (R = 0.73, RMSD = 0.069 m ³ · m ^-3 , and bias = 0.053 m ³ · m ^-3 ) between the in situ and satellite data. A slight constant underestimation from the SMOS data set was detected. A centered (bias removed) root-mean-square difference was calculated to account for this persistent bias (RMSDc = 0.044 m ³ · m ^-3 ). This result is close to the SMOS accuracy objective of 0.04 m ³ · m ^-3 . Two conclusions can be drawn: First, SMOS is close to meet the mission accuracy requirements in REMEDHUS, and second, SMOS is able to detect temporal anomalies and the temporal evolution of ground soil moisture, even though the soil moisture was slightly underestimated. Despite a noticeably reduced spatial variability among the SMOS grid cells, the remotely sensed soil moisture shows a spatial pattern of the soil moisture fields on the area scale, in agreement with the site-specific characteristics of REMEDHUS. No differences were found between the use of ascending and descending orbits. In addition, no differences were detected between the use of time-overpass values of in situ soil moisture and that of the daily average.

The ESA's Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite devoted to measure the Earth's surface soil moisture. It has a spatial resolution of ~ 40 km and a 3-day revisit. In this paper, a downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations. This algorithm is applied to combine 2 years of SMOS and MODIS Terra/Aqua data over the Iberian Peninsula into fine-scale (1 km) soil moisture estimates. Disaggregated soil moisture maps are compared to 0-5 cm ground-based measurements from the REMEDHUS network. Three matching strategies are employed: 1) a comparison at 40 km spatial resolution is undertaken to ensure SMOS sensitivity is preserved in the downscaled maps; 2) the spatio-temporal correlation of downscaled maps is analyzed through comparison with point-scale observations; and 3) high-resolution maps and ground-based observations are aggregated per land-use to identify spatial patterns related with vegetation activity and soil type. Results show that the downscaling method improves the spatial representation of SMOS coarse soil moisture estimates while maintaining temporal correlation and root mean squared differences with ground-based measurements. The dynamic range of in situ soil moisture measurements is reproduced in the high-resolution maps, including stations with different mean soil wetness conditions. Downscaled maps capture the soil moisture dynamics of general land uses, with the exception of irrigated crops. This evaluation study supports the use of this downscaling approach to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula.