Abstract
Abstract. The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies (if not accounted for) cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies on near-surface soil moisture products derived from actual (non-synthetic) data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for three areas in Oklahoma, USA. Differences between on-ground observations, model estimates and AMSR-E retrievals were related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data, a second derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and a third through inversion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of relatively small areas (<0.05) of open water in or near the sensor footprint, possibly in combination with increased, below-critical vegetation density conditions (optical density <0.8), which contribute to seasonally varying biases in excess of 0.2 (m3 m−3) soil water content. These errors need to be addressed, either through elimination or accurate characterisation, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.
Highlights
Near-surface soil moisture derived from remotely sensed low-frequency microwave emissions has the ability to improve hydrological and meteorological modelling (e.g. Koster et al, 2004; Scipal et al, 2005; Crow, 2007; Brocca et al, 2011)
While the satellite-derived soil moisture data sets make use of the same sensor, the UoM applies a different method to solve the microwave radiative transfer function for land surface variables, using a combination of multi-frequency polarizations and ratios (Jones and Kimball, 2010). It employs an open water fraction derived from higher frequency AMSR-E data to correct for positive bias
This study provides an indication that higher frequency AMSR-E data provide sufficient spatial resolution to correct for open water fraction contribution on a 0.25 degree grid
Summary
Near-surface soil moisture derived from remotely sensed low-frequency microwave emissions has the ability to improve hydrological and meteorological modelling (e.g. Koster et al, 2004; Scipal et al, 2005; Crow, 2007; Brocca et al, 2011). The Land Parameter Retrieval Model (LPRM, Owe et al, 2008) , a radiative transfer-based model, has demonstrated significant potential for providing estimates of land surface parameters, such as (relative) nearsurface moisture, land surface temperature (LST) and vegetation optical depth (VOD), independent of in situ observations Satellite retrievals of these parameters may be combined with simulated and observed data in an assimilation scheme in order to generate the best possible data fields Walker and Houser, 2001; Reichle et al, 2007; Scipal et al, 2008a) These data may be used to initialise numerical weather predictions or land surface models, drive continuous atmospheric forcing correction or, in case of systematic error, assist in model structure development/improvement (Drusch, 2007; Brocca et al, 2010; Van Dijk and Renzullo, 2011). An analytical solution based on error propagation in the partial derivatives of the radiative transfer
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