Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System
Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been wid...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/8/1772 |
| _version_ | 1797434277147705344 |
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| author | Yun Yang Martha Anderson Feng Gao Jie Xue Kyle Knipper Christopher Hain |
| author_facet | Yun Yang Martha Anderson Feng Gao Jie Xue Kyle Knipper Christopher Hain |
| author_sort | Yun Yang |
| collection | DOAJ |
| description | Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area. |
| first_indexed | 2024-03-09T10:29:54Z |
| format | Article |
| id | doaj.art-2990695ec9b74467918ef2a22691617d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:29:54Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2990695ec9b74467918ef2a22691617d2023-12-01T21:21:46ZengMDPI AGRemote Sensing2072-42922022-04-01148177210.3390/rs14081772Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion SystemYun Yang0Martha Anderson1Feng Gao2Jie Xue3Kyle Knipper4Christopher Hain5Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USAHydrology and Remote Sensing Laboratory, USDA ARS, Beltsville, MD 20705, USAHydrology and Remote Sensing Laboratory, USDA ARS, Beltsville, MD 20705, USAHydrology and Remote Sensing Laboratory, USDA ARS, Beltsville, MD 20705, USASustainable Agriculture Water Systems Research Unit, USDA ARS, Davis, CA 95616, USAEarth Science Branch, NASA Marshall Space Flight Center, Huntsville, AL 35805, USAEvapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area.https://www.mdpi.com/2072-4292/14/8/1772data fusionevapotranspirationLandsatremote sensingGoogle Earth Enginewater use |
| spellingShingle | Yun Yang Martha Anderson Feng Gao Jie Xue Kyle Knipper Christopher Hain Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System Remote Sensing data fusion evapotranspiration Landsat remote sensing Google Earth Engine water use |
| title | Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System |
| title_full | Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System |
| title_fullStr | Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System |
| title_full_unstemmed | Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System |
| title_short | Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System |
| title_sort | improved daily evapotranspiration estimation using remotely sensed data in a data fusion system |
| topic | data fusion evapotranspiration Landsat remote sensing Google Earth Engine water use |
| url | https://www.mdpi.com/2072-4292/14/8/1772 |
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