Recent advances in (soil moisture) triple collocation analysis |
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| Institution: | 1. Department of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria;2. Earth and Climate Cluster, Faculty of Earth and Life Sciences, VU University Amsterdam, the Netherlands;3. Max-Planck-Institute for Meteorology, KlimaCampus, Hamburg, Germany;4. European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom;5. Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy;1. Department of Infrastructure Engineering, University of Melbourne, Victoria 3010, Australia;2. Hydrology and Remote Sensing Laboratory, US Department of Agriculture, Beltsville, MD, USA;1. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, United States;2. Hydrology and Remote Sensing Laboratory, United States Department of Agriculture, United States;3. Estellus, France;4. Laboratoire de l''Etude du Rayonnement et de la Matière en Astrophysique, CNRS, Observatoire de Paris, Paris, France;5. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, United States;1. School of Earth Ocean and the Environment, University of South Carolina, Columbia, SC 29208, USA;2. VanderSat, Wilhelminastraat 43a, 2011 VK Haarlem, The Netherlands;3. Department of Earth System Science, Stanford University, Stanford, CA, USA;4. Vienna University of Technology, Department of Geodesy and Geoinformation, Vienna, Austria;5. USDA-ARS-Hydrology and Remote Sensing Laboratory, Beltsville, MD 21032, USA;6. Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon, Republic of Korea |
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| 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. |
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| Keywords: | Soil moisture Error characterization Validation Triple collocation |
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