{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:20:38Z","timestamp":1776144038030,"version":"3.50.1"},"reference-count":114,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,15]],"date-time":"2017-08-15T00:00:00Z","timestamp":1502755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016400","name":"NRF","doi-asserted-by":"publisher","award":["NRF-2015R1C1A1A02037224"],"award-info":[{"award-number":["NRF-2015R1C1A1A02037224"]}],"id":[{"id":"10.13039\/100016400","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs9080847","type":"journal-article","created":{"date-parts":[[2017,8,15]],"date-time":"2017-08-15T14:35:00Z","timestamp":1502807700000},"page":"847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products"],"prefix":"10.3390","volume":"9","author":[{"given":"Ju Hyoung","family":"Lee","sequence":"first","affiliation":[{"name":"Agricultural and Life Science Research Institute, Seoul National University, Seoul 08826, Korea"},{"name":"CESBIO, 13 Avenue du Colonel Roche, UMR 5126, 31401 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6352-1717","authenticated-orcid":false,"given":"Chuanfeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5196-3996","authenticated-orcid":false,"given":"Yann","family":"Kerr","sequence":"additional","affiliation":[{"name":"CESBIO, 13 Avenue du Colonel Roche, UMR 5126, 31401 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Anderson, P.W. 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