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.
Highlights
In addition to Root Mean Square Error (RMSE) retrieval goal, more diverse perspective for defining errors directly retrievals, and its application to a short-range weather prediction, we review a stochastic approach at footprint scale isthe needed
This review the limitation of RMSE retrieval goal based upon different governing factors, a limited spatial discusses the limitation of RMSE retrieval goal based upon different governing factors, a limited coverage of high density validation site at a global scale, field measurement errors, and variable spatial coverage of high density validation site at a global scale, field measurement errors, and variable penetration depth
It is stated that relative approach such as Triple Collocation (TC) or cumulative cumulative distribution function (CDF) matching that rely on a relative comparison with other distribution function (CDF) matching that rely on a relative comparison with other datasets are datasets are designed for climatology stationary errors at a time-scale of years [45] rather than resolving sub-pixel heterogeneity [33]
Summary
He previously stated that the behavior of aggregated elements is not understood by a simple linear extrapolation of each element so that a different understanding of the new behavior is required at a higher level of complexity and scale. The footprint scale behavior of satellite soil moisture products is not captured by an extrapolation or other statistical synthesis of several point measurements at local scale [2,3]. Space borne sensors at low resolution do not very delicately detect the point-scale details from land surface. The satellite retrievals usually deal with a mixed pixel as a single uniform entity (e.g., SMOS retrieval algorithms read sub-pixel land cover information at 4 km by 4 km but make the pixels uniform by aggregation)
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