Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products
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 cla...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/8/847 |
| _version_ | 1818997816420204544 |
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| author | Ju Hyoung Lee Chuanfeng Zhao Yann Kerr |
| author_facet | Ju Hyoung Lee Chuanfeng Zhao Yann Kerr |
| author_sort | Ju Hyoung Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T21:51:39Z |
| format | Article |
| id | doaj.art-f4fd8d12f7bd42f99753ec5271bff9ed |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T21:51:39Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f4fd8d12f7bd42f99753ec5271bff9ed2022-12-21T19:25:32ZengMDPI AGRemote Sensing2072-42922017-08-019884710.3390/rs9080847rs9080847Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture ProductsJu Hyoung Lee0Chuanfeng Zhao1Yann Kerr2Agricultural and Life Science Research Institute, Seoul National University, Seoul 08826, KoreaCollege of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, ChinaCESBIO, 13 Avenue du Colonel Roche, UMR 5126, 31401 Toulouse, FranceTo 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.https://www.mdpi.com/2072-4292/9/8/847satellite bias correction for short-range weather forecastfootprint scale satellite retrieval errorsSMOS/SMAP soil moistureclimatology stationary errorsstochastic retrievalsupscaling errors |
| spellingShingle | Ju Hyoung Lee Chuanfeng Zhao Yann Kerr Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products Remote Sensing satellite bias correction for short-range weather forecast footprint scale satellite retrieval errors SMOS/SMAP soil moisture climatology stationary errors stochastic retrievals upscaling errors |
| title | Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products |
| title_full | Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products |
| title_fullStr | Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products |
| title_full_unstemmed | Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products |
| title_short | Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products |
| title_sort | stochastic bias correction and uncertainty estimation of satellite retrieved soil moisture products |
| topic | satellite bias correction for short-range weather forecast footprint scale satellite retrieval errors SMOS/SMAP soil moisture climatology stationary errors stochastic retrievals upscaling errors |
| url | https://www.mdpi.com/2072-4292/9/8/847 |
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