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Bias reduction in short records of satellite soil moisture
Author(s) -
Reichle R. H.,
Koster R. D.
Publication year - 2004
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2004gl020938
Subject(s) - satellite , environmental science , data assimilation , cumulative distribution function , remote sensing , anomaly (physics) , sampling (signal processing) , forcing (mathematics) , water content , meteorology , climatology , geology , statistics , computer science , probability density function , geography , mathematics , physics , geotechnical engineering , filter (signal processing) , condensed matter physics , aerospace engineering , engineering , computer vision
Although surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals, they typically exhibit very different mean values and variability. These biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals through data assimilation. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by using spatial sampling with a 2 degree moving window we can obtain local statistics based on a one‐year satellite record that are a good approximation to those that would be derived from a much longer time series. This result should increase the usefulness of relatively short satellite data records.