The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on Nov. 2, 2009. Its main objective is to provide accurate global soil moisture estimation to a wide range of applications, including hydrological modeling. This is because soil moisture is a key state variable in hydrological models. The global Level-3 soil moisture data set generated from the SMOS was released by the Barcelona Expert Center. This study particularly focuses on its basin-scale evaluation, over the Pontiac basin in the central United States. In addition, a comparison of the capability of four North American Land Data Assimilation System-Phase 2 (NLDAS-2) land surface models’ soil moisture outputs (i.e., Noah, Mosaic, Sacramento (SAC), and variable infiltration capacity (VIC) models) in hydrological modeling is also conducted. The soil moisture deficit derived from a three-layer Xinanjiang (XAJ) model is used as a hydrological benchmark for all the comparisons. It is found that SMOS retrievals are not reliable for hydrological usage when there is frozen soil. Generally speaking, the descending orbit shows a stronger potential for improved hydrological predictions; however, it has a relatively sparse data availability. For NLDAS-2 soil moisture outputs, the SAC model shows a significant correlation with the XAJ soil moisture information. Furthermore SAC shows no distinct performance difference between frozen and unfrozen data sets. The VIC model demonstrates less seasonality and has a rather poor performance for frozen data set. The superiorities from different soil moisture products could be fused to provide the optimal information content for its application in hydrological modeling, which is discussed in the chapter.

Wildfires impact the environment and the human economy and are expected to increase in number and extension due to climate change. Thus, improving fire prediction tools is highly important. The availability for the first time of global and frequent information of remotely sensed soil moisture opens a new way in the study of the drought-wildfire relationship, since soil moisture is a key variable conditioning fuels' flammability and consequently fires ignition and propagation. A review of soil moisture as a fire precondition is provided, focused in the nature of soil moisture satellite data and on how dry soils favor the occurrence and extension of fires. Specific examples are presented for the Iberian Peninsula, where a drought-fire risk index based on spaceborne soil moisture data is developed and put in operational use (http://cp34-bec.cmima.csic.es/NRT). New satellite measurements from the ESA's SMOS and the NASA's SMAP missions provide unprecedented capabilities to detect dry conditions posing a risk of fire and their use in fire prevention services is very promising.

Earth Observation (EO) has played an imperative role in extending our abilities for obtaining information on the spatio-temporal distribution of surface soil moisture (SSM). A wide range of techniques have been proposed for this purpose. Some of those techniques have based on the integration of satellite-derived estimates of Fractional Vegetation Cover (Fr) and Land Surface Temperature (Ts) in the form of a scatterplot domain, often combining land surface process model simulations. These techniques aim at combining the horizontal coverage and spectral resolution of EO imagery with the vertical coverage and fine temporal continuity of the process models.

The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology, and climatic modeling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) climate change initiative (CCI), using finer spatial resolution synthetic aperture radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse-scale CCI essential climate variable (ECV) soil moisture (ECV SM) product in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture data sets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture data sets at the Finnish sites. Overall, the two different satellite-derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse-scale ECV SM product to track soil moisture variability in time.

Monitoring evapotranspiration (ET) over large areas is required in environmental applications that need frequent information about soil water loss. Such information, if delivered daily, could benefit different sectors relying on drought warning systems. Daily ET estimations can be obtained by the time integration of forecasts by numerical weather prediction models. However, the limited access to observation data and the coarse resolution of the products constrains their use. An operational monitoring system based on Earth observation satellite data and numerical weather forecasts has been set up in the EUMETSAT Satellite Application Facility on Land Surface Analysis: half-hourly and daily ET maps are delivered in near-real time at 3–5km resolution. Although successful over large parts of Europe and Africa, difficulties of this monitoring system arise in some regions from the strong dependence of the ET model to the numerical weather forecasts used, and especially the modeled soil water status. Therefore, we have developed a new algorithm that assimilates more remote sensing products: the daily vegetation status and the daily soil moisture retrieved from the land surface temperature data of the Meteosat geostationary satellites. The results show a large improvement of the capabilities in ET monitoring in the semiarid regions of Africa.

Soil moisture acts as a key variable in the exchanges of water and energy from land surface to atmosphere. The work presented here showed the first-time comprehensive evaluation of land surface temperature (LST) derived from moderate resolution imaging spectroradiometer (MODIS) and soil moisture from soil moisture and ocean salinity (SMOS) for prediction hydrological soil moisture deficit (SMD).

This chapter describes the emerging applications for satellite-based soil moisture (SM) data and proposes possible future uses, backed up by some preliminary research which has helped to identify this need. Applications across the ecological and hydrological sciences, in a wide range of discipline areas, are considered. The more established application areas of numerical weather prediction and climate modelling are not considered in this chapter. The chapter starts with applications of global relevance, and then goes on to focussing on the potential applications in Africa, where the needs for accurate information on water resources are particularly urgent in order to address issues of food and water security. Finally, the current limitations and future developments influencing the application of satellite SM data are discussed.

A dielectric model for thawed and frozen Arctic organic-rich soil (90% organic matter) has been developed. The model is based on soil dielectric measurements that were collected over ranges of gravimetric moisture from 0.01 to 0.94g/g, dry soil density from 0.56 to 0.67g/cm3, and temperature from 25°C to −30°C (cooling run) in the frequency range of 0.05–16GHz. The refractive mixing dielectric model was applied with the Debye multirelaxation equations to fit the measurements of the soil's complex dielectric constant (CDC) as a function of soil moisture and wave frequency. The spectroscopic parameters of the dielectric relaxations for the bound, transient bound, and unbound soil water components were derived and were complimented by the thermodynamic parameters to obtain a complete set of parameters for the proposed temperature-dependent multirelaxation spectroscopic dielectric model for moist soils. To calculate the CDC of the soil, the following input variables must be assigned: (1) density of dry soil, (2) gravimetric moisture, (3) wave frequency, and (4) temperature. The error of the dielectric model was evaluated and yielded root mean square error values of 0.266 and 0.214 for the soil dielectric constant and the loss factor, respectively. These values are on the order of the dielectric measurement error itself. The proposed dielectric model can be applied in active and passive microwave remote sensing techniques to develop algorithms for retrieving the soil moisture and the freeze/thaw state of organic-rich topsoil in the Arctic regions.

The mitigation of landslide risk is needed for improving the resilience of our society to extreme events, particularly under climatic and global change conditions. The two main climatic drivers triggering shallow landslides can be identified in precipitation and soil moisture, as clearly recognized in the recent scientific literature. Surprisingly, the use of measurements of soil moisture is rarely employed in Early Warning systems for landslides prediction, and only a couple studies have considered satellite soil moisture observations in this context.

Currently, the retrieval of soil moisture is necessary through microwave remote sensing and it is the simplest and most reliable model for applications such as drought, flood, irrigation management, and scheduling. For this purpose, a soil test site has been prepared with different moisture content and constant soil surface roughness. The bistatic scatterometer measurements have been carried out in the angular range of 20–70 degrees steps of 10 degrees at HH- and VV-polarization for the X-band. The observed data sets (bistatic scattering coefficients and soil moisture content) have been divided into calibration (75%) and validation (25%) of data sets. The radial basis function artificial neural network (RBFANN) has been calibrated for the retrieval of soil moisture using bistatic scatterometer data. The performance of the RBFANN model has been evaluated using statistical performance indices %Bias, root mean square error, and Nash-Sutcliffe efficiency. The estimated values of soil moisture by the RBFANN model and the experimentally observed values of soil moisture are found very close during calibration and validation of the model.