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The use of remotely sensed near-surface soil moisture for the estimation of evaporation is investigated. Two widely used parameterizations of evaporation, the so-called ␣ and ␤ methods, which use near-surface soil moisture to reduce some measure of potential evaporation, are studied. The near-surface soil moisture is provided by a set of L-and S-band microwave radiometers, which were mounted 13 m above the surface. It is shown that soil moisture measured with a passive microwave sensor in combination with the ␤ method yields reliable estimates of evaporation, whereas the ␣ method is not as robust.

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Through analyses of the model simulated data-base, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multi-polarizations and 40" incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components -the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12,0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations.

An important issue in the development of a dedicated space borne soil moisture sensor has been concern over the reliability of soil moisture retrievals in densely vegetated areas and the global extent over which retrievals will be possible. Errors in retrieved soil moisture can originate from a variety of sources within the measurement and retrieval process. In addition to instrument error, three key contributors to retrieval error are the masking of the soil microwave signal by vegetation, the interplay between nonlinear retrieval physics and the relatively poor spatial resolution of space borne sensors, and retrieval parameter uncertainty. Quantification of these errors requires the realistic specification of land surface soil moisture heterogeneity and spatial vegetation patterns. Since detailed soil moisture patterns are currently difficult to obtain from direct observations, an attractive alternative is the application of an observing system simulation experiment (OSSE) in which simulated land surface states are propagated through the sensor measurement and retrieval process to investigate and constrain expected levels of retrieval error. This manuscript describes results from an OSSE designed out to simulate the impact of land surface heterogeneity, instrument error, and retrieval parameter uncertainty on radiometer-only soil moisture products derived from the NASA ESSP Hydrosphere State (Hydros) mission.