Impact of Hillslope-Scale Organization of Topography, Soil Moisture, Soil Temperature, and Vegetation on Modeling Surface Microwave Radiation Emission

Abstract

Abstract-Microwave radiometry will emerge as an important tool for global remote sensing of near-surface soil moisture in the coming decade. In this modeling study, we find that hillslopescale topography (tens of meters) influences microwave brightness temperatures in a way that produces bias at coarser scales (kilometers). The physics underlying soil moisture remote sensing suggests that the effects of topography on brightness temperature observations are twofold: 1) the spatial distribution of vegetation, moisture, and surface and canopy temperature depends on topography and 2) topography determines the incidence angle and polarization rotation that the observing sensor makes with the local land surface. Here, we incorporate the important correlations between factors that affect emission (e.g., moisture, temperature, and vegetation) and topographic slope and aspect. Inputs to the radiative transfer model are obtained at hillslope scales from a mass-, energy-, and carbon-balance-resolving ecohydrology model. Local incidence and polarization rotation angles are explicitly computed, with knowledge of the local terrain slope and aspect as well as the sky position of the sensor. We investigate both the spatial organization of hillslope-scale brightness temperatures and the sensitivity of spatially aggregated brightness temperatures to satellite sky position. For one computational domain considered, hillslope-scale brightness temperatures vary from approximately 121 to 317 K in the horizontal polarization and from approximately 117 to 320 K in the vertical polarization. Including hillslope-scale heterogeneity in factors effecting emission can change watershed-aggregated brightness temperature by more than 2 K, depending on topographic ruggedness. These findings have implications for soil moisture data assimilation and disaggregation of brightness temperature observations to hillslope scales.

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

“…As such, it requires both extension and synthesis of existing hydrologic modeling techniques. While existing mechanistic hydrologic models can be coupled, through the interdependence of their parameters, to models of other physical or biological systems (Flores et al, 2009;Qu and Duffy, 2007;Tucker et al, 2001;Beven, 2007), and while many existing land surface modeling schemes already link ecological, climatological and hydrological fluxes (Gerten, 2013), indiscriminately extending such complex models rapidly becomes intractable. Thus, other modeling approaches are essential.…”

“…Ultimately, we will wish to use detailed mechanistic (bottom up) models of coupled systems to generate quantitative predictive insights. Several such models already exist for different eco-hydrological, hydro-geomorphological and other applications (Flores et al, 2009;Qu and Duffy, 2007;Tucker et al, 2001). It is unclear, however, whether existing models are well prepared to cope with non-stationary systems (Wagener et al, 2010).…”

Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

“…A full description of the tRIBS-VEGGIE model is outside the scope of this applied study. The reader is referred to [27][28][29][30] for a more extensive overview of the model physics and [8,18,26] for its application in the development of the data assimilation framework used in this study.…”

“…Output from the ecohydrology model is supplied as input to the Integral Equation Model [7,[21][22][23]] to yield synthesized images of radar backscatter in two polarization states (see [8]). Because topography is correlated with factors affecting reflection of microwave energy like soil moisture (e.g., [24]) and controls the local incidence and polarization, the observation synthesis approach explicitly accounts for topography (e.g., [25,26]). …”

Spatial horizontal correlation characteristics in the land data assimilation of soil moisture

“…Examples are the surface soil moisture products of the microwave sensors AMSR-E (Advanced Microwave Scanning Radiometer for EOS) (Njoku and Chan, 2006), ASCAT (Advanced Scatterometer) , SMOS (Soil Moisture and Ocean Salinity) (Kerr et al, 2010), the upcoming SMAP mission (Soil Moisture Active Passive) (Entekhabi et al, 2010), and the land surface temperature of thermal infrared sensor MODIS (Moderate Resolution Imaging Spectroradiometer) (Wan and Li, 2008). However, soil moisture retrieval based on microwave measurements is often hampered by the presence of vegetation cover (Dorigo et al, 2010;Njoku and Chan, 2006) or topography (Flores et al, 2009;Matzler and Standley, 2000;Pellenq et al, 2003). Moreover, passive microwave sensor records can be contaminated by radio frequency interferences and accordant pixels have to be excluded from the analysis (Anterrieu, 2011;Skou et al, 2010).…”