Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review
The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/770 |
| _version_ | 1797484939159011328 |
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| author | Reza Khandan Jean-Pierre Wigneron Stefania Bonafoni Arastoo Pour Biazar Mehdi Gholamnia |
| author_facet | Reza Khandan Jean-Pierre Wigneron Stefania Bonafoni Arastoo Pour Biazar Mehdi Gholamnia |
| author_sort | Reza Khandan |
| collection | DOAJ |
| description | The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented. |
| first_indexed | 2024-03-09T23:11:40Z |
| format | Article |
| id | doaj.art-53323aa621be4ccd97570f8cf7a91c66 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:11:40Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-53323aa621be4ccd97570f8cf7a91c662023-11-23T17:43:12ZengMDPI AGRemote Sensing2072-42922022-02-0114377010.3390/rs14030770Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A ReviewReza Khandan0Jean-Pierre Wigneron1Stefania Bonafoni2Arastoo Pour Biazar3Mehdi Gholamnia4Department of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Teheran 14155-6619, IranINRAE, nUMR1391 ISPA, F-33140 Villenave d’Ornon, FranceDepartment of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, ItalyAtmospheric Science Department, University of Alabama in Huntsville, Huntsville, AL 35805, USADepartment of Civil Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj P.O. Box 618, IranThe correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented.https://www.mdpi.com/2072-4292/14/3/770Soil Moisture (SM)assimilationLand Surface Model (LSM)Radiative Transfer Model (RTM)surfaceroot zone |
| spellingShingle | Reza Khandan Jean-Pierre Wigneron Stefania Bonafoni Arastoo Pour Biazar Mehdi Gholamnia Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review Remote Sensing Soil Moisture (SM) assimilation Land Surface Model (LSM) Radiative Transfer Model (RTM) surface root zone |
| title | Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review |
| title_full | Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review |
| title_fullStr | Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review |
| title_full_unstemmed | Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review |
| title_short | Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review |
| title_sort | assimilation of satellite derived soil moisture and brightness temperature in land surface models a review |
| topic | Soil Moisture (SM) assimilation Land Surface Model (LSM) Radiative Transfer Model (RTM) surface root zone |
| url | https://www.mdpi.com/2072-4292/14/3/770 |
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