Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States
Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to signifi...
| 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 |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/9/12/1181 |
| _version_ | 1818959905899413504 |
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| author | Meredith Reitz Gabriel B. Senay Ward E. Sanford |
| author_facet | Meredith Reitz Gabriel B. Senay Ward E. Sanford |
| author_sort | Meredith Reitz |
| collection | DOAJ |
| description | Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints. |
| first_indexed | 2024-12-20T11:49:04Z |
| format | Article |
| id | doaj.art-e04a18ef7abc48308eb82304bb9acc34 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:49:04Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e04a18ef7abc48308eb82304bb9acc342022-12-21T19:41:49ZengMDPI AGRemote Sensing2072-42922017-11-01912118110.3390/rs9121181rs9121181Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United StatesMeredith Reitz0Gabriel B. Senay1Ward E. Sanford2Hydrologic Remote Sensing Branch, U.S. Geological Survey, Reston, VA 20192, USAEarth Resources Observation and Science (EROS) Center, North Central Climate Science Center, U.S. Geological Survey, Fort Collins, CO 80523, USAEarth Systems Modeling Branch, U.S. Geological Survey, Reston, VA 20192, USAEvapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.https://www.mdpi.com/2072-4292/9/12/1181evapotranspirationwater resourcesremote sensingwater balanceevaporationtranspiration |
| spellingShingle | Meredith Reitz Gabriel B. Senay Ward E. Sanford Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States Remote Sensing evapotranspiration water resources remote sensing water balance evaporation transpiration |
| title | Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States |
| title_full | Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States |
| title_fullStr | Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States |
| title_full_unstemmed | Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States |
| title_short | Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States |
| title_sort | combining remote sensing and water balance evapotranspiration estimates for the conterminous united states |
| topic | evapotranspiration water resources remote sensing water balance evaporation transpiration |
| url | https://www.mdpi.com/2072-4292/9/12/1181 |
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