- Related Research Areas
- Water & Energy Cycles
Microwave radiometer satellite missions have been and are being proposed to space agencies for monitoring soil moisture on a global scale (e.g. Soil Moisture and Ocean Salinity (SMOS) mission, Aquarius and Soil Moisture Active Passive (SMAP)). Availability of accurate global soil moisture products has the potential of improving forecasts of land surface processes (e.g. evaporation and runoff) , which have various field of application, such as surface flood forecasting, drought monitoring and agriculture. Among the challenges of retrieving soil moisture from passive microwave satellite observations is, however, the requirement to account for the effects of vegetation. Elimination of the vegetation effects is based on the parameterization of the transmissivity coefficient (γ) and the single scattering albedo (ω). For large scale soil moisture retrieval applications, the ω is assumed to be time-invariant constant depending only on the vegetation morphology, while the γ is implemented as a time-dependent variable affected by the vegetation morphology and the density of the vegetation. In general, single channel retrieval algorithms are considered as the most robust solution, which make use of the ancillary data approach to derive the γ The ancillary data approach is based upon the formulation of the γ as a function of the vegetation water content and an empirical constant, the b parameter. Experimental investigations have, however, shown that the empirical constant is specific for each crop type and may depend on the morphology of the vegetation cover. Although the empirical constant is, typically, implemented as a single time-invariant parameter, scientific evidence shows that temporal variations in the vegetation morphology throughout the growth cycle may affect the empirical constant and, thus, the determination of the γ and the retrieval of soil moisture. In the proposed research, the seasonal variability in the empirical constant will be determined through the analysis of existing radiometer data base collected over fields grown with corn and soybean. In addition, physically based emission models will be utilized to simulate seasonal evolution of the empirical constant based on the vegetation morphology measured during field campaigns. Through determination of the variability in the empirical constant derived from the radiometer observations and theoretical simulations, the uncertainty in the soil moisture retrievals imposed by the empirical constant can be determined. This improved knowledge about the behavior of the empirical constant could be used for implementation within global soil moisture retrieval algorithms. Moreover, quantification of the soil moisture retrieval uncertainty induced by the empirical constant can be employed for the assimilation of soil moisture products into hydrological and weather prediction models resulting in more accurate forecasts.
Project PI: Alicia Joseph/NASA Goddard Space Flight Center
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