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
                                            GRUBER, ALEX - Vienna University Of Technologyitem
                                            SU, CHUN-HSU - University Of Melbourneitem
                                            ZWIEBECK, A. - Vienna University Of Technologyitem
                                            Crow, Wadeitem
                                            DORIGO, W.A. - Vienna University Of Technologyitem
                                            WAGNER, W. - Vienna University Of Technology
  Submitted to: International Journal of Applied Earth Observation and Geoinformation
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 8/1/2015
                                    Publication  Date: 4/15/2016
                                    Citation: Gruber, A., Su, C., Zwiebeck, A., Crow, W.T., Dorigo, W., Wagner, W. 2016. Recent advances in (soil moisture) triple collocation analysis. International Journal of Applied Earth Observation and Geoinformation. 45(B):200-211. doi: 10.1016/j.jag.2015.09.002.Interpretive Summary: Knowledge of soil moisture availability is critical for a range of agricultural and water resource applications. Soil moisture estimates can be obtained from remote sensing observations. However, such observations are difficult to evaluate due to a severe lack of ground-based soil moisture observations. Recently, statistical techniques have been developed that allow us to estimate the accuracy of remotely-sensed soil moisture observations without the direct use of ground-based observations for comparison. This paper reviews the state-of-the art for these techniques and extends them to provide more useful information regarding the quality of surface soil moisture estimates provided by remote sensing. The techniques reviewed and developed by this paper will be used by developers of agricultural drought monitors as they attempt to fully integrate remotely-sensed soil moisture retrievals into their operational products. As such, it will eventual help us to better detect and mitigate the economic consequences of agricultural drought.
                                Technical Abstract: To date, triple collocation (TC) analysis is one of the most important methods for the global scale evaluation of remotely sensed soil moisture data sets. In this study we review existing implementations of soil moisture TC analysis as well as investigations of the assumptions underlying the method. Different notations that are used to formulate the TC problem are shown to be mathematically identical. While many studies have investigated issues related to possible violations of the underlying assumptions, only few TC modifications have been proposed to mitigate the impact of these violations. Moreover, assumptions, which are often understood as a limitation that is unique to TC analysis are shown to be common also to other conventional performance metrics. Noteworthy advances in TC analysis have been made in the way error estimates are being presented by moving
from the investigation of absolute error variance estimates to the investigation of signal-to-noise ratio (SNR) metrics. Here we review existing error presentations and propose the combined investigation of the SNR (expressed in logarithmic units), the unscaled error variances, and the soil moisture sensitivities of the data sets as an optimal strategy for the evaluation of remotely-sensed soil moisture data sets.


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