[1] A historical climatology of continuous satellite-derived global land surface soil moisture is being developed. The data consist of surface soil moisture retrievals derived from all available historical and active satellite microwave sensors, including Nimbus-7 Scanning Multichannel Microwave Radiometer, Defense Meteorological Satellites Program Special Sensor Microwave Imager, Tropical Rainfall Measuring Mission Microwave Imager, and Aqua Advanced Microwave Scanning Radiometer for EOS, and span the period from November 1978 through the end of 2007. This new data set is a global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer-based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large-scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community.

Is it possible to solve the radiative transfer equation to derive surface soil moisture without information on the vegetation cover or soil moisture ground observations for calibration. Approach:A methodology for retrieving surface soil moisture and vegetation optical from satellite microwave radiometer data has been developed.The approach uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth with a nonlinear iterative optimization procedure.Results compared well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors. Significance and Implications of Findings:This approach does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes, and is totally independent of wavelength. It permits the retrieval of global surface moisture fields from satellite microwave observations. This procedure can provide historical data sets of global surface moisture from archived satellite microwave data, near-real time estimates, and could be valuable for initialization and as an input parameter for General Circulation Models. Relation to Earth Science Enterprise:The interpretation of satellite microwave observations for soil moisture determination has strong relevance within the Earth Science Enterprise Program, especially in land cover and use change, seasonal to interannual climate variability and prediction, and climate change research.The significance of this methodology increases with the inclusion of a microwave instrument on the new AQUA platform. A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference IndexManfred Owe, Richard de Jeu and Jeffrey Walker Popular SummaryA new procedure for estimating global soil moisture from microwave sensors on Earthorbiting satellites has been developed. This method uses a physically based equation, known as a radiative transfer relationship, and is unique in that it does not require measurements of ground data that have traditionally been necessary for calibration purposes.In addition, the procedure also estimates the vegetation optical depth. The optical depth is a measure of the amount of vegetation which overlies the surface. Together, these two variables can provide researchers with valuable information about the moisture status of the Earth's surface. Such information may be important for a variety of applications, such as drought monitoring, determining flooding potential, various agricultural applications, and estimating fire danger.

[1] Two data sets of satellite surface soil moisture retrievals are first compared and then assimilated into the NASA Catchment land surface model. The first satellite data set is derived from 4 years of X-band (10.7 GHz) passive microwave brightness temperature observations by the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and the second is from 9 years of C-band (6.6 GHz) brightness temperature observations by the Scanning Multichannel Microwave Radiometer (SMMR). Despite the similarity in the satellite instruments, the retrieved soil moisture data exhibit very large differences in their multiyear means and temporal variability, primarily because they are computed with different retrieval algorithms. The satellite retrievals are also compared to a soil moisture product generated by the NASA Catchment land surface model when driven with surface meteorological data derived from observations. The climatologies of both satellite data sets are different from those of the model products. Prior to assimilation of the satellite retrievals into the land model, satellite-model biases are removed by scaling the satellite retrievals into the land model's climatology through matching of the respective cumulative distribution functions. Validation against in situ data shows that for both data sets the soil moisture fields from the assimilation are superior to either satellite data or model data alone. A global analysis of the innovations (defined as the difference between the observations and the corresponding model values prior to the assimilation update) reveals how changes in model and observations error parameters may enhance filter performance in future experiments.

[1] An alternative to thermal infrared satellite sensors for measuring land surface temperature (T s ) is presented. The 37 GHz vertical polarized brightness temperature is used to derive T s because it is considered the most appropriate microwave frequency for temperature retrieval. This channel balances a reduced sensitivity to soil surface characteristics with a relatively high atmospheric transmissivity. It is shown that with a simple linear relationship, accurate values for T s can be obtained from this frequency, with a theoretical bias of within 1 K for 70% of vegetated land areas of the globe. Barren, sparsely vegetated, and open shrublands cannot be accurately described with this single channel approach because variable surface conditions become important. The precision of the retrieved land surface temperature is expected to be better than 2.5 K for forests and 3.5 K for low vegetation. This method can be used to complement existing infrared derived temperature products, especially during clouded conditions. With several microwave radiometers currently in orbit, this method can be used to observe the diurnal temperature cycles with surprising accuracy.

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Based on Kohsiek's fast air circulation chamber, a method has been developed to measure the surface resistance to vapor diffusion in a drying topsoil. This resistance is important to estimate evaporation from bare soils using an aerodynamic resistance formulation. Measurements were done for a fine sandy loam during a dry down after artificial wetting. Surface resistance started to increase at a moisture content of 15% by volume in the 1‐cm top layer, which is 50% of its moisture content at field capacity. Calculations of the aerodynamic resistance were corrected for stability and were used to isolate the real surface resistance from the bulk resistance. Resistances could be modeled as a function of the top 1 cm soil moisture and varied between approximately 10 s/m for a wet and several thousand seconds per meter for a dry top layer. The measurements demonstrated a very pronounced diurnal course due to drying of the very top layer during the day and recovery of the moisture profile during nighttime hours.

A numerical solution for the canopy optical depth in an existing microwave-based land surface parameter retrieval model is presented. The optical depth is derived from the microwave polarization difference index and the dielectric constant of the soil. The original procedure used an approximation in the form of a logarithmic decay function to define this relationship and was derived through a series of lengthy polynomials. These polynomials had to be recalculated when the scattering albedo or antenna incidence angle changes. The new procedure is computationally more efficient and accurate.Index Terms-Microwave polarization difference index (MPDI), microwave radiometry, remote sensing, vegetation optical depth.

[1] Two field data sets are used to model near-surface soil temperature profiles in a bare soil. It is shown that the commonly used solutions to the heat flow equations by Van Wijk perform well when applied at deeper soil layers, but result in large errors when applied to near surface layers, where more extreme variations in temperature occur. The reason for this is that these approaches do not consider heat sources or sinks below the surface. This paper proposes a new approach for modeling the surface soil temperature profiles from a single observation depth. This approach consists of two parts: 1) modeling an instantaneous ground flux profile based on net radiation and the ground heat flux at 5 cm depth; and 2) use of this ground heat flux profile to extrapolate a single temperature observation to a complete surface temperature profile. The new model is validated under different field and weather conditions showing low RMS errors of 1-3 K for wet to dry conditions. Finally, the proposed model is tested under limitations in input data that are associated with remote sensing applications. It is shown that these limitations result in only small increases in the overall error. This approach may be useful for satellite-based global energy balance applications.