U.S. flagAn official website of the United States government
                    Here's how you know **Official websites use .gov**
                            
                            A **.gov** website belongs to an official government organization in the United States.
 **Secure .gov websites use HTTPS**
                            
                            A **lock** (                            
                              A locked padlock                                                      
                            
                            ) or **https://** means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Menu Agricultural Research Service
						
                    U.S. DEPARTMENT OF AGRICULTURE
 Hydrology and Remote Sensing Laboratory: Beltsville, MD
 close



    Research

                    Research Projects
                                    Publications
                                    Collaborations
                                    News
                                    Software
                People

                    Staff Listing
                                    CareersSearch small
                    Search
            
            ARS Home
                                About ARS
                                Contact Us




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





- ARS National Programs
- Search for a scientific manuscript
- Search for a research project
- ARS Software
- Programs and Projects
- Collaborations
- Subjects of Investigation
- Publications
- People
- Research Sites
- Pesticide Volatilization
- Watershed Modeling
- Climate Change
- Soil Moisture
- Drought and ET
- HRSL Brochure

Title:   Author  item
                                            SU, CHUN-HSU - University Of Melbourneitem
                                            RYU, D - University Of Melbourneitem
                                            WESTERN, A - University Of Melbourneitem
                                            Crow, Wadeitem
                                            WAGNER, W - Vienna University Of Technology
  Submitted to: International Congress on Modeling and Simulation Proceedings
                                    Publication Type: Proceedings
                                    Publication Acceptance Date: 11/30/2013
                                    Publication  Date: N/A
                                    Citation: N/AInterpretive Summary:
                                Technical Abstract: Soil moisture is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of soil moisture over meso to global scales used 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 operate at sub-daily intervals at mesoscale resolutions. However to correctly interpret observed variations and assimilate them in hydrological and weather models, the error structures of the retrieved soil moisture data need 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 analyse 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 a 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 South, 112-154 East). In particular we consider the 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, the dry cooler desert areas of southern Australia also show higher errors, in contrast to lower errors over the hotter dry desert of central Australia. The reverse is the case for AMSR-E. These patterns are also reflected in the spatial error maps of TC analysis and the direct comparisons of SF and TC estimates show moderate-to-good correlations: 0.64 for AMSR-E, 0.68 for SMOS, and 0.68 for ASCAT. However the SF yields lower estimates than TC at the high end of the range. 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 overpass times can also over-estimate the errors. 

This work therefore presents an additional perspective on satellite soil moisture observation errors (in the Fourier domain) that may complement other error estimation approaches (in the time domain), thereby improving our understanding of the sources and types of errors.


Return to top


Connect with ARS Sign up


ARS Home
                            USDA.gov
                            Plain Writing
                            Policies & Links
                            Civil Rights Statements
                            FOIA
                            Accessibility Statement
                            Privacy Policy
                            Non-Discrimination Statement
                            Quality of Information
                            USA.gov
                            WhiteHouse.gov