[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] 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.

[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.

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