Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
Volume 19, issue 1
Hydrol. Earth Syst. Sci., 19, 17–31, 2015
https://doi.org/10.5194/hess-19-17-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 17–31, 2015
https://doi.org/10.5194/hess-19-17-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Jan 2015

Research article | 06 Jan 2015

Multi-scale analysis of bias correction of soil moisture

C.-H. Su and D. Ryu C.-H. Su and D. Ryu
  • Department of Infrastructure Engineering, University of Melbourne, 3010 Victoria, Australia

Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatio-temporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that is increasingly being used to address this issue, but is only applicable to the linear regime, whereas the non-linear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatio-temporal scales, we propose a multi-timescale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data-adaptive, non-linear de-noising via wavelet thresholding.

Publications Copernicus
Download
Short summary
Global environmental monitoring requires geophysical measurements from a variety of sources and sensors to close the information gap. This paper proposes a novel approach for analysing temporal scale-by-scale differences (biases and errors) between geophysical estimates from disparate sources. This allows assessment of different bias correction schemes, and forms the basis for a multi-scale bias correction scheme and data-adaptive, non-linear de-noising.
Global environmental monitoring requires geophysical measurements from a variety of sources and...
Citation