An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data
The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/24/6253 |
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| author | Mojtaba Saboori Yousef Mousivand Jordi Cristóbal Reza Shah-Hosseini Ali Mokhtari |
| author_facet | Mojtaba Saboori Yousef Mousivand Jordi Cristóbal Reza Shah-Hosseini Ali Mokhtari |
| author_sort | Mojtaba Saboori |
| collection | DOAJ |
| description | The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day<sup>−1</sup>, coefficient of determination (R<sup>2</sup>) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET. |
| first_indexed | 2024-03-09T15:54:22Z |
| format | Article |
| id | doaj.art-49cb5f5683114818806f17f2dc4a5ec1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:22Z |
| publishDate | 2022-12-01 |
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| series | Remote Sensing |
| spelling | doaj.art-49cb5f5683114818806f17f2dc4a5ec12023-11-24T17:46:39ZengMDPI AGRemote Sensing2072-42922022-12-011424625310.3390/rs14246253An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis DataMojtaba Saboori0Yousef Mousivand1Jordi Cristóbal2Reza Shah-Hosseini3Ali Mokhtari4Department of Geography, Kharazmi University, Tehran 1491115719, IranDepartment of Geography, Kharazmi University, Tehran 1491115719, IranEfficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology, Fruitcentre, Parc Científic i Tecnològic Agroalimentari de Lleida 23, 25003 Lleida, SpainSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, IranSchool of Life Sciences, Technical University of Munich, 85354 Freising, GermanyThe large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day<sup>−1</sup>, coefficient of determination (R<sup>2</sup>) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET.https://www.mdpi.com/2072-4292/14/24/6253evapotranspirationSEBALendmember selectionLandsatGLDAStime series |
| spellingShingle | Mojtaba Saboori Yousef Mousivand Jordi Cristóbal Reza Shah-Hosseini Ali Mokhtari An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data Remote Sensing evapotranspiration SEBAL endmember selection Landsat GLDAS time series |
| title | An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data |
| title_full | An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data |
| title_fullStr | An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data |
| title_full_unstemmed | An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data |
| title_short | An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data |
| title_sort | automated and improved methodology to retrieve long time series of evapotranspiration based on remote sensing and reanalysis data |
| topic | evapotranspiration SEBAL endmember selection Landsat GLDAS time series |
| url | https://www.mdpi.com/2072-4292/14/24/6253 |
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