Improving time upscaling of instantaneous evapotranspiration based on machine learning models
Evapotranspiration (ET) plays a crucial role in the global water and energy cycle. Upscaling instantaneous ET ([Formula: see text]) to daily ET ([Formula: see text]) is vital for thermal-based ET estimation. Conventional methods – such as the constant evaporative fraction method (ConEF), radiation-b...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2024.2423431 |
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| author | Danni Yang Shanshan Yang Jiaojiao Huang Shuyu Zhang Sha Zhang Jiahua Zhang Yun Bai |
| author_facet | Danni Yang Shanshan Yang Jiaojiao Huang Shuyu Zhang Sha Zhang Jiahua Zhang Yun Bai |
| author_sort | Danni Yang |
| collection | DOAJ |
| description | Evapotranspiration (ET) plays a crucial role in the global water and energy cycle. Upscaling instantaneous ET ([Formula: see text]) to daily ET ([Formula: see text]) is vital for thermal-based ET estimation. Conventional methods – such as the constant evaporative fraction method (ConEF), radiation-based method, and evaporative ratio method – often overlook environmental factors, leading to biased estimates of [Formula: see text] from [Formula: see text]. To resolve this issue, this study aimed to assess four machine learning (ML) algorithms—XGBoost, LightGBM, AdaBoost, and Random Forest—to integrate meteorological and remote sensing data for upscaling [Formula: see text] across 88 global flux sites. Each ML model was tested with eight different variable combinations. Results indicated that XGBoost exhibited the best performance, with a root mean square error (RMSE) generally below 13 W [Formula: see text] in estimating [Formula: see text] from [Formula: see text]. The best variable combination simultaneously considers evaporative fraction, available energy, meteorology factors, remote sensing albedo, normalized vegetation index, and leaf area index. Using this combination, the XGBoost model achieved an [Formula: see text] = 0.88 and an RMSE = 12.33 W [Formula: see text], outperforming the ConEF method ([Formula: see text] = 0.71 and RMSE = 18.86 W [Formula: see text]) and its expansions. These findings support the application of ML models in ET upscaling, enabling ET estimation across large spatiotemporal scales. |
| format | Article |
| id | doaj-art-d1ed48bee52f451ca63ceb641215c831 |
| institution | DOAJ |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-d1ed48bee52f451ca63ceb641215c8312025-08-20T03:06:10ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-01-019112715410.1080/20964471.2024.2423431Improving time upscaling of instantaneous evapotranspiration based on machine learning modelsDanni Yang0Shanshan Yang1Jiaojiao Huang2Shuyu Zhang3Sha Zhang4Jiahua Zhang5Yun Bai6School of Computer Science and Technology, Qingdao University, Qingdao, ChinaSchool of Computer Science and Technology, Qingdao University, Qingdao, ChinaSchool of Computer Science and Technology, Qingdao University, Qingdao, ChinaSchool of Computer Science and Technology, Qingdao University, Qingdao, ChinaSchool of Geographic Sciences, Hebei Normal University, Shijiazhuang, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaHebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, ChinaEvapotranspiration (ET) plays a crucial role in the global water and energy cycle. Upscaling instantaneous ET ([Formula: see text]) to daily ET ([Formula: see text]) is vital for thermal-based ET estimation. Conventional methods – such as the constant evaporative fraction method (ConEF), radiation-based method, and evaporative ratio method – often overlook environmental factors, leading to biased estimates of [Formula: see text] from [Formula: see text]. To resolve this issue, this study aimed to assess four machine learning (ML) algorithms—XGBoost, LightGBM, AdaBoost, and Random Forest—to integrate meteorological and remote sensing data for upscaling [Formula: see text] across 88 global flux sites. Each ML model was tested with eight different variable combinations. Results indicated that XGBoost exhibited the best performance, with a root mean square error (RMSE) generally below 13 W [Formula: see text] in estimating [Formula: see text] from [Formula: see text]. The best variable combination simultaneously considers evaporative fraction, available energy, meteorology factors, remote sensing albedo, normalized vegetation index, and leaf area index. Using this combination, the XGBoost model achieved an [Formula: see text] = 0.88 and an RMSE = 12.33 W [Formula: see text], outperforming the ConEF method ([Formula: see text] = 0.71 and RMSE = 18.86 W [Formula: see text]) and its expansions. These findings support the application of ML models in ET upscaling, enabling ET estimation across large spatiotemporal scales.https://www.tandfonline.com/doi/10.1080/20964471.2024.2423431Constant evaporative fraction method (ConEF)evapotranspiration (ET)machine learning (ML)time upscaling |
| spellingShingle | Danni Yang Shanshan Yang Jiaojiao Huang Shuyu Zhang Sha Zhang Jiahua Zhang Yun Bai Improving time upscaling of instantaneous evapotranspiration based on machine learning models Big Earth Data Constant evaporative fraction method (ConEF) evapotranspiration (ET) machine learning (ML) time upscaling |
| title | Improving time upscaling of instantaneous evapotranspiration based on machine learning models |
| title_full | Improving time upscaling of instantaneous evapotranspiration based on machine learning models |
| title_fullStr | Improving time upscaling of instantaneous evapotranspiration based on machine learning models |
| title_full_unstemmed | Improving time upscaling of instantaneous evapotranspiration based on machine learning models |
| title_short | Improving time upscaling of instantaneous evapotranspiration based on machine learning models |
| title_sort | improving time upscaling of instantaneous evapotranspiration based on machine learning models |
| topic | Constant evaporative fraction method (ConEF) evapotranspiration (ET) machine learning (ML) time upscaling |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2024.2423431 |
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