Monitoring of soil moisture using operational microwave satellites

Authors: Mladenova, Iliana; Jackson, Thomas

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/27/2012
Publication Date: 4/1/2012
Citation: Mladenova, I., Jackson, T.J. 2012. Monitoring of soil moisture using operational microwave satellites [abstract]. BARC Poster Day. 2012 CDROM.

Interpretive Summary:

Technical Abstract: Accurate and timely knowledge of the water availability in the soil column is essential for water recourse management and agricultural decision making. Soil water information is a crucial model input as well as it is an important source of information for the proper understanding and interpretation of crop conditions and (expected) yield estimates. Current large scale forecasting systems rely heavily on daily satellite derived retrievals of environmental variables, such as precipitation, soil and air temperature, soil moisture content, etc. Several agencies routinely generate daily soil moisture estimates that are publically accessible and have been extensively used for a board range of applications including agriculture. These operational products are based on utilizing mostly passive microwave remote sensing data. Retrieval techniques employed by each research group working in this area are based on the same electromagnetic principles and make use of the Radiative Transfer set of equations. However, the resulting products differ significantly in terms of dynamic range and exhibit certain biases that appear to be dependent on roughness conditions and vegetation cover. Consequently, this calls for careful evaluation of the existing approaches. Our main goal is to assess the standard NASA retrieval product. As part of our effort to improve the baseline NASA algorithm, we will carefully examine each of the alternative algorithms/products to better understand what elements of each might be combined in a new approach. A total of eight algorithms/products are currently being assembled and implemented so that they can be run under the same conditions. In order to understand why these techniques differ in performance, it is necessary that we [1] gain an indepth knowledge of the differences between the theoretical basis of the algorithms, [2] analyze the impact of the assumptions and simplifications made in deriving these solutions, and [3] assess algorithm sensitivity to input data, parameters and modules (i.e. roughness/temperature correction, dielectric model, etc.). Thus, in addition to improving the baseline algorithm, this gives the opportunity to better understand the merits of the existing approaches as well as develop and test validation techniques. Hopefully these efforts will lead to establishment of a more robust and transferable algorithm applicable to multiple instruments and platforms. In this poster we will present an overview of the algorithms selected and preliminary results of the inter-comparisons.