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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #300706

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Error characterization of microwave satellite soil moisture data sets using fourier analysis

item Su, Chun-hsu - University Of Melbourne
item Ryu, D - University Of Melbourne
item Western, A - University Of Melbourne
item Crow, Wade
item Wagner, W - Vienna University Of Technology

Submitted to: International Congress on Modeling and Simulation Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 12/1/2013
Publication Date: 12/15/2013
Citation: Su, C.-H., Ryu, D., Western, A., Crow, W.T., Wagner, W. 2013. Error characterization of microwave satellite soil moisture data sets using Fourier analysis. Proceedings of the 20th International Congress of Modeling and Simulation. P. 3120-3126.

Interpretive Summary:

Technical Abstract: Abstract: Soil moisture is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of soil moisture over mesoscale to global scales as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these processes. At present, satellite platforms that provide this capability of mapping global surface soil moisture at sub-daily intervals at mesoscale resolutions. However to correctly interpret observed variations and assimilate them in hydrological and weather models, error structures of their retrieved soil moisture data set needs to be better understood and characterised. In this paper we investigate the utility of a recently proposed method to quantify the variance of stochastic noise in passive and active satellite soil moisture products. While it is typical to analyze the difference between satellite retrievals and ground truth in the time domain, the proposed method is based on quantifying the differences between retrieved soil moisture and standard water-balance equation in the conjugate Fourier domain. The method, which referred to as Spectral Fitting (SF), is applied to estimate the errors in passive and active retrievals over Australia (10-44 degrees South, 112-154 degrees East). In particular we consider AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) LPRM (Land Parameter Retrieval Method), CATDS (Centre Aval de Traitement des Données SMOS) SMOS (Soil Salinity and Ocean Salinity), and TU-WIEN (Vienna University of Technology) ASCAT (Advanced Scatterometer) soil moisture products. The results are compared against the errors estimated using the standard method of triple collocation (TC) with AMSR-E, SMOS and ASCAT as the data triplet. Our analyses show that the SF method is able to recover similar and reasonable error maps that reflect sensitivity of retrieval errors to land surface and climate characteristics over Australia. As expected, more vegetated and wetter areas are usually associated with higher errors. Additionally for SMOS and ASCAT, dry cold desert areas of southern Australia also show higher errors, in contrast to lower errors over hot dry desert of central Australia. It is the reverse for AMSR-E. These are also reflected in the spatial error maps of TC analysis.And the direct comparisons of SF and TC estimate show fair-to-good correlations: 0.67 for AMSR-E, 0.69 for SMOS, and 0.68 for ASCAT. However the SF yields lower estimates at the large-error limit of TC. On one hand, this is perhaps expected given rationale of the SF method to estimate only the stochastic/high-frequency components of the total errors. On the other hand, the simple error model and implementation of TC with non-coincident can over-estimate the errors. This work therefore presents an additional perspective of studying the errors (in the Fourier domain) that may complement other error estimation approaches (in the time domain) in better understanding the sources and types of errors in the satellite-retrieved soil moisture products.