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Title: A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

item PALOSCIA, S. - National Research Council - Italy
item SANTI, E. - National Research Council - Italy
item PETTINATO, S. - National Research Council - Italy
item MLADENOVA, I. - Science Systems, Inc
item Jackson, Thomas
item BINDLISH, R. - Science Systems, Inc
item Cosh, Michael

Submitted to: Frontiers of Earth Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2015
Publication Date: 4/15/2015
Citation: Paloscia, S., Santi, E., Pettinato, S., Mladenova, I., Jackson, T.J., Bindlish, R., Cosh, M.H. 2015. A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data. Frontiers of Earth Science. 3(16):1-10.

Interpretive Summary: Two algorithms capable of estimating soil moisture from satellite data were compared using in situ observations from experimental watershed sites with in situ observations. Each algorithm is based on the same radiative transfer model but takes a very different path to implementation. Analysis revealed that each yielded approximately the same results in terms of the statistical metrics examined and could meet or exceed the mission soil moisture accuracy requirement for the conditions examined. The systematic and timely monitoring of land surface parameters that affect the hydrological cycle at a variety of spatial scales is of great importance in gaining a better understanding of geophysical processes and for the management of environmental resources and natural disasters. These results provide a basis for greater confidence in the satellite products and their use in agricultural decision making.

Technical Abstract: A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Global Change Observation Mission–Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) =0.06 m3/m3 and Bias <0.02 m3/m3. These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.