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

Abstract

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.

Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

“…Here, the goal of the calibration is to obtain a separate calibration curve for each soil texture type, where soil type-specific calibration curves are expected to have fewer inversion errors (i.e., from sensor measured signals into soil moisture) than using above given default Equation (1).…”

“…These quality control assessments are performed for all stations separately between 2008 and 2016 using daily values, while the sensitivity analyses (i.e., validation of remote sensing-and model-based soil moisture datasets given under Section 2.3) are performed between 2008 and 2011. Firstly, the raw 10-minute period observations are converted to 10-minute soil moisture estimates using the default calibration Equation (1). Then, the obtained 10-minute soil moisture values are converted to daily values using the arithmetic average method.…”

“…Clay Loam (G1) 1 11 Silt Loam (G5) 7 Loam (G2) 23 Silty Clay Loam (G6) 3 Loamy Sand (G3) 1 Clay 1 Sandy Loam (G4) 21 Missing Samples 1 1 The soil texture types that the laboratory calibrations made are given within the parenthesis (e.g., G1, G2, etc. ).…”

“…As an example, the actual and the estimated (via CS616 sensor) soil moisture values for Group 4 are given in Table 4. 1 The soil texture types that the laboratory calibrations made are given within the parenthesis (e.g., G1, G2, etc.). For each group, eight measurements representing different soil wetness are made.…”

“…Soil moisture plays a crucial role in the understanding of the water and energy cycle between the land and the atmosphere. Accurate and reliable acquisition of soil moisture estimates ensures its usability in many areas, such as weather forecast [1], drought analysis [2], flood forecast [3], forest fires [4], etc. Moreover, soil moisture, due to its high temporal memory, has significant effects on the Earth's climate properly [5].…”