Remote monitoring of soil moisture using passive microwave-based technologies – theoretical basic and overview of selected algorithms for AMSR-E

Authors: Mladenova, Iliana; Jackson, Thomas; NJOKU, ENI - Jet Propulsion Laboratory; BINDLISH, R. - Science Systems, Inc; CHAN, S. - Jet Propulsion Laboratory; Cosh, Michael; Holmes, Thomas; DE JEU, R.A.M. - Vu University Medical Center; JONES, L. - University Of Montana; KIMBALL, J. - University Of Montana; PALOSCIA, S. - National Research Council - Italy; SANTI, E. - National Research Council - Italy

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/27/2013
Publication Date: 3/25/2014
Publication URL: http://handle.nal.usda.gov/10113/59882
Citation: Mladenova, I., Jackson, T.J., Njoku, E., Bindlish, R., Chan, S., Cosh, M.H., Holmes, T.R., De Jeu, R., Jones, L., Kimball, J., Paloscia, S., Santi, E. 2014. Remote monitoring of soil moisture using passive microwave-based technologies – theoretical basic and overview of selected algorithms for AMSR-E. Remote Sensing of Environment. 197-213.

Interpretive Summary: The fundamental basis for differences in satellite-based soil moisture products was examined and causes were identified. Soil moisture estimated using satellite-based technologies have demonstrated the potential to provide information on the spatial and temporal variability of this important hydrologic variable. Currently there are several alternative products generated using the Advanced Microwave Scanning Radiometer sensor system on the Aqua satellite (AMSR-E). Past research had shown that these alternative products differ in their accuracy and reliability and that the operational product provided by NASA exhibits a narrower range of global soil moisture as compared to other alternative soil moisture products. Based upon the investigation, key algorithm components of the NASA approach were identified for modification. The changes will improve the operational AMSR-E product Algorithm improvements would lead to enhanced reliability and accuracy of the available soil moisture datasets, which are essential source of information for numerous agricultural related activities, including irrigation planning, crop health monitoring, and crop production estimation.

Technical Abstract: Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (AMSR-E) has made significant contributions to this application. As the result of agency and individual initiatives, several approaches for the retrieval of soil moisture from AMSR-E have been proposed and implemented. Although the majority of these are based on the same Radiative Transfer Equation, studies have shown that the resulting soil moisture estimates can differ significantly. A goal of this investigation is to develop a suitable approach to improve the algorithm currently used by NASA to produce its operational soil moisture product. In order to achieve this goal, the theoretical bases of several alternative soil moisture retrieval algorithms are examined. Analysis focuses on five established approaches: the operational NASA algorithm, the Single Channel Algorithm, the Land Parameter Retrieval Model, the University of Montana soil moisture algorithm, and the HydroAlgo Artificial Neural Network approach. Comparisons of algorithms have typically focused on the retrieved soil moisture products, and employed different metrics and data sets and have resulted in differing conclusions. In this investigation we attempt to provide a more thorough understanding of the fundamental differences between the algorithms and how these differences affect their performance in terms of range of soil moisture provided. The comparative overview presented in the paper is based on the operating versions of the source codes of the individual algorithms. Analysis indicated that the difference between algorithms lies in the specific parameterizations and assumptions that each algorithm makes. The comparative overview of the theoretical bases of the approaches is then linked to differences found in the soil moisture retrievals, leading to suggestions for improvements and increased reliability in these algorithms.