Analysis of two years of ASCAT-and SMOS-derived soil moisture estimates over Europe and North Africa

Abstract

More than two years of soil moisture data derived from the Advanced SCATterometer (ASCAT) and from the Soil Moisture and Ocean Salinity (SMOS) radiometer are analysed and compared. The comparison has been performed within the framework of an activity aiming at validating the EUMETSAT Hydrology Satellite Application Facility (H-SAF) soil moisture product derived from ASCAT. The available database covers a large part of the SMOS mission lifetime (2010, 2011 and partially 2012) and both Europe and North Africa are considered. A specific strategy has been set up in order to enable the comparison between products representing a volumetric soil moisture content, as those derived from SMOS, and a relative saturation index, as those derived from ASCAT. Results demonstrate that the two products show a fairly good degree of correlation. Their consistency has some dependence on season, geographical zone and surface land cover. Additional factors, such as spatial property features, are also preliminary investigated.

Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

“…Conversely, H07 data with quality, snow cover and frozen soil flags greater than 20 were discarded; no threshold was necessary for topographic complexity flag because its maximum value over the Orba basin was found to be small (15%). Also SMOS data with quality index (DQX), expressed in volumetric soil moisture units, greater than 0.045 and Radio Frequency Interference probability (RFI) greater than 1% were not considered (same thresholds used in Albergel et al, 2012 andPierdicca et al, 2013b).…”

A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

“…Using the Advanced Scatterometer (ASCAT) soil moisture product as an example, only count values from zero to one hundred (rather than the real volumetric water content) are provided to represent the extremely dry to extremely wet conditions. Consequently, these values must generally be converted into absolute soil moisture content using time series analysis or auxiliary data such as soil porosity or other soil moisture data sets (Wagner et al, 1999;Ceballos et al, 2005;Draper et al, 2011;Pierdicca et al, 2013). Although SSM data sets such as the commonly used Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) have been developed based on radiative transfer models, the challenge is that they require the input of many physical parameters, some of which are currently not well-defined, e.g.…”

Validation of remote sensing soil moisture products with a distributed continuous hydrological model

“…Consequently, only satellite remote sensing allows for estimating soil moisture with complete and frequent coverage. In particular, remote sensing measurements present a direct sensitivity to surface soil moisture (~5 cm) at microwave bands (especially in the low frequency range, i.e., [1][2][3][4][5], where it influences the soil electrical permittivity, and the atmosphere can be considered as fairly transparent. Microwave remote sensing encompasses both active and passive forms.…”