Abstract
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.
Highlights
Global environmental monitoring requires geophysical measurements from a variety of sources and sensors to close the information gap
Given the possiblescale dependency in biases and errors, we propose an extension to Triple collocation (TC) analyses to include wavelet-based multi-resolution analysis (MRA) (Mallat, 1989) as a framework to (1) provide a fuller description of the temporal scale-by-scale relationships between coincident data sets; (2) study the influence of various prospective bias correction schemes; and (3) achieve multi-scale bias correction
At j = 1–2, as the scaling coefficients cannot be estimated by TC, cumulative distribution function (CDF) matching was applied to these scales such that the biases are still present on these scales
Summary
Global environmental monitoring requires geophysical measurements from a variety of sources and sensors to close the information gap. Based on an affine signal model and additive orthogonal error model, it assumes that representativity differences are manifested as additive and multiplicative biases These assumptions may have limited validity, as the temporal behaviour of SM may vary across different spatial scales, driven by a continuum of localised and mesoscale influences Given the possible (time)scale dependency in biases and errors, we propose an extension to TC analyses to include wavelet-based multi-resolution analysis (MRA) (Mallat, 1989) as a framework to (1) provide a fuller description of the temporal scale-by-scale relationships between coincident data sets; (2) study the influence of various prospective bias correction schemes; and (3) achieve multi-scale bias correction.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
