Soil Moisture Retrieval Uncertainty From Soil and Vegetation Heterogeneity Over a Topographic Surface
Abstract
At the interface between the land surface and atmosphere, soil moisture governs evapotranspiration, infiltration and runoff processes. Current knowledge of the soil moisture is poor, although a satellite L-band passive microwave mission is planned that will monitor the global surface soil moisture. This Soil Moisture and Ocean Salinity (SMOS) mission uses synthetic aperture techniques to achieve a resolution of 35-50km, and is scheduled for launch in September 2007. Besides noise in the measurements, determination of the soil moisture from the brightness temperature measured by SMOS has a number of sources of error in: physics of the forward model, parameters in the model and from surface heterogeneity in the field-of-view. In fact, to achieve a mission specification of 4% volumetric soil moisture accuracy, the retrieval of soil moisture is more sensitive to surface temperature, open water coverage and melting snow than can reasonably estimated from available data. Therefore, a data assimilation framework is needed to determine soil moisture from passive microwave measurements. Here, we examine soil moisture retrieval error from topographic effects. Local slope affects the pathlength through vegetation and the soil emissivity given by the Fresnel equations. Previous work has shown that the mean error is less than 0.6% for a triangular hillslope with a maximum coverage of 30% slopes at 30° under the assumption that the soil and vegetation is uniform across the scene. For a field-of-view as large as that of SMOS, an assumption of soil and vegetation homogeneity is not realistic. Variation in these parameters may occur with elevation, aspect and location. This paper quantifies soil moisture retrieval error for simple topographic scenarios, and tests the results for a DEM transect. The forward model aggregates brightness temperatures over a scene for both horizontal and vertical polarization at a range of look angles. In the forward model, soil moisture, surface temperature and vegetation optical depth were assumed to be a simple function of either slope or aspect. An inverse model was used to retrieve soil moisture based on the minimization of a cost function. The soil moisture retrieval errors were compared to those expected from a topographically flat scene with comparable parameter heterogeneity. These errors must be taken into account within the overall error budget of the SMOS or similar missions.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2006
- Bibcode:
- 2006AGUFM.H14C..06S
- Keywords:
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- 1855 Remote sensing (1640);
- 1866 Soil moisture;
- 1873 Uncertainty assessment (3275);
- 1894 Instruments and techniques: modeling