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Keywords = climatology stationary errors

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23 pages, 2342 KB  
Review
Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products
by Ju Hyoung Lee, Chuanfeng Zhao and Yann Kerr
Remote Sens. 2017, 9(8), 847; https://doi.org/10.3390/rs9080847 - 15 Aug 2017
Cited by 15 | Viewed by 5758
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 [...] Read more.
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. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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