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Remote Sens. 2017, 9(8), 847; https://doi.org/10.3390/rs9080847

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

1
Agricultural and Life Science Research Institute, Seoul National University, Seoul 08826, Korea
2
CESBIO, 13 Avenue du Colonel Roche, UMR 5126, 31401 Toulouse, France
3
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Received: 19 May 2017 / Revised: 25 June 2017 / Accepted: 26 July 2017 / Published: 15 August 2017
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Abstract

To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with. View Full-Text
Keywords: satellite bias correction for short-range weather forecast; footprint scale satellite retrieval errors; SMOS/SMAP soil moisture; climatology stationary errors; stochastic retrievals; upscaling errors satellite bias correction for short-range weather forecast; footprint scale satellite retrieval errors; SMOS/SMAP soil moisture; climatology stationary errors; stochastic retrievals; upscaling errors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lee, J.H.; Zhao, C.; Kerr, Y. Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sens. 2017, 9, 847.

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