A geostatistical approach to upscale soil moisture with unequal precision observations
Upscaling ground-based moisture observations to satellite footprint-scale estimates is an important problem in remote sensing soil-moisture product validation. The reliability of validation is sensitive to the quality of input observation data and the upscaling strategy. This letter proposes a model...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/77048 |
| _version_ | 1848763807225085952 |
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| author | Wang, J. Ge, Y. Song, Yongze Li, X. |
| author_facet | Wang, J. Ge, Y. Song, Yongze Li, X. |
| author_sort | Wang, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Upscaling ground-based moisture observations to satellite footprint-scale estimates is an important problem in remote sensing soil-moisture product validation. The reliability of validation is sensitive to the quality of input observation data and the upscaling strategy. This letter proposes a model-based geostatistical approach to scale up soil moisture with observations of unequal precision. It incorporates unequal precision in the spatial covariance structure and uses Monte Carlo simulation in combination with a block kriging (BK) upscaling strategy. The approach is illustrated with a real-world application for upscaling soil moisture in the Heihe Watershed Allied Telemetry Experimental Research experiment. The results show that BK with unequal precision observations can consider both random ground-based measurement errors and upscaling model error to achieve more reliable estimates. We conclude that this approach is appropriate to quantify upscaling uncertainties and to investigate the error propagation process in soil-moisture upscaling. © 2004-2012 IEEE. |
| first_indexed | 2025-11-14T11:09:20Z |
| format | Journal Article |
| id | curtin-20.500.11937-77048 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:09:20Z |
| publishDate | 2014 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-770482019-12-03T01:20:35Z A geostatistical approach to upscale soil moisture with unequal precision observations Wang, J. Ge, Y. Song, Yongze Li, X. Science & Technology Physical Sciences Technology Geochemistry & Geophysics Engineering, Electrical & Electronic Remote Sensing Imaging Science & Photographic Technology Engineering Block kriging (BK) Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Monte Carlo simulation remote sensing product validation SENSOR NETWORK RADIOBRIGHTNESS DESIGN Upscaling ground-based moisture observations to satellite footprint-scale estimates is an important problem in remote sensing soil-moisture product validation. The reliability of validation is sensitive to the quality of input observation data and the upscaling strategy. This letter proposes a model-based geostatistical approach to scale up soil moisture with observations of unequal precision. It incorporates unequal precision in the spatial covariance structure and uses Monte Carlo simulation in combination with a block kriging (BK) upscaling strategy. The approach is illustrated with a real-world application for upscaling soil moisture in the Heihe Watershed Allied Telemetry Experimental Research experiment. The results show that BK with unequal precision observations can consider both random ground-based measurement errors and upscaling model error to achieve more reliable estimates. We conclude that this approach is appropriate to quantify upscaling uncertainties and to investigate the error propagation process in soil-moisture upscaling. © 2004-2012 IEEE. 2014 Journal Article http://hdl.handle.net/20.500.11937/77048 10.1109/LGRS.2014.2321429 English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC restricted |
| spellingShingle | Science & Technology Physical Sciences Technology Geochemistry & Geophysics Engineering, Electrical & Electronic Remote Sensing Imaging Science & Photographic Technology Engineering Block kriging (BK) Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Monte Carlo simulation remote sensing product validation SENSOR NETWORK RADIOBRIGHTNESS DESIGN Wang, J. Ge, Y. Song, Yongze Li, X. A geostatistical approach to upscale soil moisture with unequal precision observations |
| title | A geostatistical approach to upscale soil moisture with unequal precision observations |
| title_full | A geostatistical approach to upscale soil moisture with unequal precision observations |
| title_fullStr | A geostatistical approach to upscale soil moisture with unequal precision observations |
| title_full_unstemmed | A geostatistical approach to upscale soil moisture with unequal precision observations |
| title_short | A geostatistical approach to upscale soil moisture with unequal precision observations |
| title_sort | geostatistical approach to upscale soil moisture with unequal precision observations |
| topic | Science & Technology Physical Sciences Technology Geochemistry & Geophysics Engineering, Electrical & Electronic Remote Sensing Imaging Science & Photographic Technology Engineering Block kriging (BK) Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Monte Carlo simulation remote sensing product validation SENSOR NETWORK RADIOBRIGHTNESS DESIGN |
| url | http://hdl.handle.net/20.500.11937/77048 |