Data Assimilation to Extract Soil Moisture Information from SMAP Observations
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NAS...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/11/1179 |
| _version_ | 1818083160711233536 |
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| author | Jana Kolassa Rolf H. Reichle Qing Liu Michael Cosh David D. Bosch Todd G. Caldwell Andreas Colliander Chandra Holifield Collins Thomas J. Jackson Stan J. Livingston Mahta Moghaddam Patrick J. Starks |
| author_facet | Jana Kolassa Rolf H. Reichle Qing Liu Michael Cosh David D. Bosch Todd G. Caldwell Andreas Colliander Chandra Holifield Collins Thomas J. Jackson Stan J. Livingston Mahta Moghaddam Patrick J. Starks |
| author_sort | Jana Kolassa |
| collection | DOAJ |
| description | This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m − 3 and 0.001 m 3 m − 3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m − 3 , but increased the root zone bias by 0.014 m 3 m − 3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables. |
| first_indexed | 2024-12-10T19:33:35Z |
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| id | doaj.art-427cfa35035b41f8bfe9bcc4eb854967 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T19:33:35Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-427cfa35035b41f8bfe9bcc4eb8549672022-12-22T01:36:11ZengMDPI AGRemote Sensing2072-42922017-11-01911117910.3390/rs9111179rs9111179Data Assimilation to Extract Soil Moisture Information from SMAP ObservationsJana Kolassa0Rolf H. Reichle1Qing Liu2Michael Cosh3David D. Bosch4Todd G. Caldwell5Andreas Colliander6Chandra Holifield Collins7Thomas J. Jackson8Stan J. Livingston9Mahta Moghaddam10Patrick J. Starks11Universities Space Research Association, Columbia, MD 21046, USAGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAUSDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAUSDA ARS Southeast Watershed Research Center, Tifton, GA 31793, USABureau of Economic Geology, The University of Texas at Austin, Austin, TX 78712, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USAUSDA ARS Southwest Watershed Research Center, Tucson, AZ 85719, USAUSDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAUSDA ARS National Soil Erosion Research Laboratory, West Lafayette, IN 47907, USAViterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USAUSDA ARS Grazinglands Research Laboratory, El Reno, OK 73036, USAThis study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m − 3 and 0.001 m 3 m − 3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m − 3 , but increased the root zone bias by 0.014 m 3 m − 3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables.https://www.mdpi.com/2072-4292/9/11/1179data assimilationSMAP soil moistureneural networksbias correction |
| spellingShingle | Jana Kolassa Rolf H. Reichle Qing Liu Michael Cosh David D. Bosch Todd G. Caldwell Andreas Colliander Chandra Holifield Collins Thomas J. Jackson Stan J. Livingston Mahta Moghaddam Patrick J. Starks Data Assimilation to Extract Soil Moisture Information from SMAP Observations Remote Sensing data assimilation SMAP soil moisture neural networks bias correction |
| title | Data Assimilation to Extract Soil Moisture Information from SMAP Observations |
| title_full | Data Assimilation to Extract Soil Moisture Information from SMAP Observations |
| title_fullStr | Data Assimilation to Extract Soil Moisture Information from SMAP Observations |
| title_full_unstemmed | Data Assimilation to Extract Soil Moisture Information from SMAP Observations |
| title_short | Data Assimilation to Extract Soil Moisture Information from SMAP Observations |
| title_sort | data assimilation to extract soil moisture information from smap observations |
| topic | data assimilation SMAP soil moisture neural networks bias correction |
| url | https://www.mdpi.com/2072-4292/9/11/1179 |
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