The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model
This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous Unite...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/437 |
| _version_ | 1797485159940882432 |
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| author | Azbina Rahman Viviana Maggioni Xinxuan Zhang Paul Houser Timothy Sauer David M. Mocko |
| author_facet | Azbina Rahman Viviana Maggioni Xinxuan Zhang Paul Houser Timothy Sauer David M. Mocko |
| author_sort | Azbina Rahman |
| collection | DOAJ |
| description | This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether. |
| first_indexed | 2024-03-09T23:14:49Z |
| format | Article |
| id | doaj.art-8a4786a15d054c738b234c0d4d40c4b0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:14:49Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8a4786a15d054c738b234c0d4d40c4b02023-11-23T17:37:37ZengMDPI AGRemote Sensing2072-42922022-01-0114343710.3390/rs14030437The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface ModelAzbina Rahman0Viviana Maggioni1Xinxuan Zhang2Paul Houser3Timothy Sauer4David M. Mocko5Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USADepartment of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USADepartment of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USADepartment of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USADepartment of Mathematics, George Mason University, Fairfax, VA 22030, USASAIC at NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAThis work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.https://www.mdpi.com/2072-4292/14/3/437data assimilationEnsemble Kalman FilterGLASSSMAPevapotranspirationnet ecosystem exchange |
| spellingShingle | Azbina Rahman Viviana Maggioni Xinxuan Zhang Paul Houser Timothy Sauer David M. Mocko The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model Remote Sensing data assimilation Ensemble Kalman Filter GLASS SMAP evapotranspiration net ecosystem exchange |
| title | The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model |
| title_full | The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model |
| title_fullStr | The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model |
| title_full_unstemmed | The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model |
| title_short | The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model |
| title_sort | joint assimilation of remotely sensed leaf area index and surface soil moisture into a land surface model |
| topic | data assimilation Ensemble Kalman Filter GLASS SMAP evapotranspiration net ecosystem exchange |
| url | https://www.mdpi.com/2072-4292/14/3/437 |
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