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Review

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)
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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MDPI and ACS Style

Lee, J.H.; Zhao, C.; Kerr, Y. Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sens. 2017, 9, 847. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9080847

AMA Style

Lee JH, Zhao C, Kerr Y. Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sensing. 2017; 9(8):847. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9080847

Chicago/Turabian Style

Lee, Ju Hyoung, Chuanfeng Zhao, and Yann Kerr. 2017. "Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products" Remote Sensing 9, no. 8: 847. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9080847

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