Muiti-Sensor Historical Climatology of Satellite-Derived Global Land Surface Moisture

A historical climatology of continuous satellite derived global land surface soil moisture is being developed. The data set consists of surface soil moisture retrievals from observations of both historical and currently active satellite microwave sensors, including Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E. The data sets span the period from November 1978 through the end of 2006. The soil moisture retrievals are made with the Land Parameter Retrieval Model, a physically-based model which was developed jointly by researchers from the above institutions. These data are significant in that they are the longest continuous data record of observational surface soil moisture at a global scale. Furthermore, while previous reports have intimated that higher frequency sensors such as on SSM/I are unable to provide meaningful information on soil moisture, our results indicate that these sensors do provide highly useful soil moisture data over significant parts of the globe, and especially in critical areas located within the Earth's many arid and semi-arid regions

A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index

A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz brightness temperature observations from the Scanning Multichannel Microwave Radiometer over several test sites in Illinois. Results using only nighttime data are presented at this time, due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a non-linear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness. Surface temperature is derived by a procedure using high frequency vertically polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is totally independent of wavelength. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors

Analytical Derivation of the Vegetation Optical Depth from the Microwave Polarization Difference Index

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

Integrating earth observation data in hydrological runoff models

The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely difficult to easily share data among scientists representing these communities without performing some cumbersome conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood forecasting systems are discussed