Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #317570

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

Location: Hydrology and Remote Sensing Laboratory

Title: Recent advances in (soil moisture) triple collocation analysis

item Gruber, Alex - Vienna University Of Technology
item Su, Chun-hsu - University Of Melbourne
item Zwiebeck, A. - Vienna University Of Technology
item Crow, Wade
item Dorigo, W.a. - Vienna University Of Technology
item 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.