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Estimation of the temporal autocorrelation structure by the collocation technique with an emphasis on soil moisture studies
Authors:Simon Zwieback  Wouter Dorigo  Wolfgang Wagner
Institution:1. ETH Zürich, Institute of Environmental Engineering, Zürich, Switzerlandzwieback@ifu.baug.ethz.ch;3. Research Group Remote Sensing, Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
Abstract:Abstract

The collocation technique has become a popular tool in oceanography and hydrology for estimating the error variances of different data sources such as in situ sensors, models and remote sensing products. It is also possible to determine calibration constants, for example to account for an off-set between the data sources. So far, the temporal autocorrelation structure of the errors has not been studied, although it is known that it has detrimental effects on the results of the collocation technique, in particular when calibration constants are also determined. This paper shows how the (triple) collocation estimators can be adapted to retrieve the autocovariance functions; the statistical properties as well as the structural deficencies are described. The coupling between the autocorrelation of the error and the estimation of calibration constants is studied in detail, due to its importance for analysing temporal changes. In soil moisture applications, such time variations can be induced, for example, by seasonal changes in the vegetation cover, which affect both models and remote sensing products. The limitations of the proposed technique associated with these considerations are analysed using remote sensing and in situ soil moisture data. The variability of the inter-sensor calibration and the autocovariance are shown to be closely related to temporal patterns of the data.

Editor D. Koutsoyiannis

Citation Zwieback, S., Dorigo, W., and Wagner, W., 2013. Estimation of the temporal autocorrelation structure by the collocation technique with an emphasis on soil moisture studies. Hydrological Sciences Journal, 58 (8), 1729–1747.
Keywords:soil moisture  validation  time series analysis  remote sensing  error analysis
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