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United States Department of Agriculture

Agricultural Research Service

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Research Project: Application of the Auto-Tuned Land Assimilation System (ATLAS) Algorithm to ASCAT Soil Moisture Evaluation

Location: Hydrology and Remote Sensing Laboratory

Project Number: 8042-13610-028-66-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Jan 1, 2014
End Date: Dec 31, 2014

The European Space Agency (ESA) is currently funding the development of a long-term, satellite-based surface soil moisture dataset to serve as essential climate variable (ECV) for climate studies. One key aspect of establishing an ECV is conducting an appropriate evaluation of the product to determine its reliability. ARS will apply a recently-developed algorithm – called the Auto-Tuned Land Assimilation System or ATLAS – to globally evaluate the quality of remotely-sensed surface soil moisture retrievals serving as the basis of the ESA soil moisture ECV dataset.

The Auto-Tuned Land Assimilation System (ATLAS) is a recently-developed USDA ARS algorithm for estimating the statistical properties of error in remotely-sensed surface soil moisture retrievals. The technique is based on the merging of an adaptive Kalman Filter and triple collocation analysis and - unlike any existing validation approach - is robust to the presence of both auto-correlated and cross-correlated errors in remotely-sensed surface soil moisture products. As a result, it represents the state-of-the-art in terms of validating remotely-sensed soil moisture retrievals over large areas of the world in which ground-based soil moisture observations are not available. In this project, ARS will globally apply ATLAS using three separate soil moisture products (a water balance-based product, an active microwave satellite product and a passive microwave satellite product) in order to access errors in (both of the) the satellite-based products. This assessment will, in turn, be used by the European Space Agency (ESA) to validate an Essential Climate Variable (ECV) data product currently being developed based on satellite-derived surface soil moisture retrievals.

Last Modified: 06/23/2017
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