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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
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| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2024.2423431 |
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| Summary: | 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. |
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| ISSN: | 2096-4471 2574-5417 |