Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-3283-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3283-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
27 May 2026
Research article | Highlight paper |
| 27 May 2026
| 27 May 2026
A novel classifier-guided ensemble framework for global terrestrial evapotranspiration estimates
Le Ni
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
Weiguang Wang
CORRESPONDING AUTHOR
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
Jianyu Fu
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China
School of Civil Engineering, Sun Yat-sen University, Guangzhou 519082, China
Mingzhu Cao
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China
School of Civil Engineering, Sun Yat-sen University, Guangzhou 519082, China
Related authors
No articles found.
Xuejin Tan, Bingjun Liu, Xuezhi Tan, Zeqin Huang, and Jianyu Fu
Hydrol. Earth Syst. Sci., 29, 427–445, https://doi.org/10.5194/hess-29-427-2025, https://doi.org/10.5194/hess-29-427-2025, 2025
Short summary
Short summary
We assess changes in blue and green water scarcity in an anthropogenic highly impacted watershed and their association with climate change and land use change, using the multi-water-flux validated Soil and Water Assessment Tool. Observed streamflow, evapotranspiration, and soil moisture are integrated into model calibration and validation. Results show that both climate change and land use change decrease blue water, while land use change increases green water.
Cited articles
Abbott, P. F. and Tabony, R. C.: The estimation of humidity parameters, Meteorol. Mag., 114, 49–56, 1985.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Fao, Rome, 300, D05109, ISBN 92-5-104219-5, 1998.
Ayan, E., Erbay, H., and Varçın, F.: Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks, Comput. Electron. Agr., 179, 105809, https://doi.org/10.1016/j.compag.2020.105809, 2020.
Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., and Holtslag, A. A. M.: A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, J. Hydrol., 212–213, 198–212, https://doi.org/10.1016/S0022-1694(98)00253-4, 1998.
Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/SXAVCZFAQLNO, 2020.
Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25 degree V2.1, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brenowitz, N. D. and Bretherton, C. S.: Prognostic Validation of a Neural Network Unified Physics Parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018GL078510, 2018.
Cai, Y., Xu, Q., Bai, F., Cao, X., Wei, Z., Lu, X., Wei, N., Yuan, H., Zhang, S., Liu, S., Zhang, Y., Li, X., and Dai, Y.: Reconciling Global Terrestrial Evapotranspiration Estimates From Multi-Product Intercomparison and Evaluation, Water Resour. Res., 60, e2024WR037608, https://doi.org/10.1029/2024WR037608, 2024.
Cao, M., Wang, W., Xing, W., Wei, J., Chen, X., Li, J., and Shao, Q.: Multiple sources of uncertainties in satellite retrieval of terrestrial actual evapotranspiration, J. Hydrol., 601, 126642, https://doi.org/10.1016/j.jhydrol.2021.126642, 2021.
Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, 2023.
Chakraborty, D., Başağaoğlu, H., and Winterle, J.: Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling, Expert Syst. Appl., 170, 114498, https://doi.org/10.1016/j.eswa.2020.114498, 2021.
Chen, H., Good, S., Caylor, K., Fiorella, R. P., and Wang, L.: A hybrid Penman–Monteith and machine learning model for simulating evapotranspiration and its components, J. Hydrol., 134985, https://doi.org/10.1016/j.jhydrol.2026.134985, 2026.
Chen, H., Huang, J. J., Dash, S. S., Wei, Y., and Li, H.: A hybrid deep learning framework with physical process description for simulation of evapotranspiration, J. Hydrol., 606, 127422, https://doi.org/10.1016/j.jhydrol.2021.127422, 2022.
Chu, W., Zhang, C., Li, H., Zhang, L., Shen, D., and Li, R.: SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting, Int. J. Appl. Earth Obs., 131, 103972, https://doi.org/10.1016/j.jag.2024.103972, 2024.
Dorogush, A. V., Ershov, V., and Gulin, A.: CatBoost: gradient boosting with categorical features support, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.11363, 2018.
ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., and Winkler, A. J.: Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning, Environ. Res. Lett., 18, 034039, https://doi.org/10.1088/1748-9326/acbbe0, 2023.
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A.: AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data, arXiv [preprint], https://doi.org/10.48550/arXiv.2003.06505, 2020.
Eskandari, H., Saadatmand, H., Ramzan, M., and Mousapour, M.: Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning, Appl. Energ., 366, 123314, https://doi.org/10.1016/j.apenergy.2024.123314, 2024.
Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X., and Zeng, W.: Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data, Agr. Water Manage., 225, 105758, https://doi.org/10.1016/j.agwat.2019.105758, 2019.
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R., McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K., Miralles, D. D., Perret, J., Lagouarde, J.-P., Waliser, D., Purdy, A. J., French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.: The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, Water Resour. Res., 53, 2618–2626, https://doi.org/10.1002/2016WR020175, 2017.
Foken, T.: The Energy Balance Closure Problem: An Overview, Ecol. Appl., 18, 1351–1367, https://doi.org/10.1890/06-0922.1, 2008.
Friedl, M. and Sulla-Menashe, D.: MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12C1.006, 2015.
Fu, J., Wang, W., Shao, Q., Xing, W., Cao, M., Wei, J., Chen, Z., and Nie, W.: Improved global evapotranspiration estimates using proportionality hypothesis-based water balance constraints, Remote Sens. Environ., 279, 113140, https://doi.org/10.1016/j.rse.2022.113140, 2022.
Galindo, F. J. and Palacio, J.: Estimating the Instabilities of N Correlated Clocks, https://api.semanticscholar.org/CorpusID:92985051 (last access: 24 April 2026), 1999.
Galindo, F. J. and Palacio, J.: Post-processing ROA data clocks for optimal stability in the ensemble timescale, Metrologia, 40, S237, https://doi.org/10.1088/0026-1394/40/3/301, 2003.
Gan, R., Zhang, Y., Shi, H., Yang, Y., Eamus, D., Cheng, L., Chiew, F. H. S., and Yu, Q.: Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems, Ecohydrology, 11, e1974, https://doi.org/10.1002/eco.1974, 2018.
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., and Suganthan, P. N.: Ensemble deep learning: A review, Eng. Appl. Artif. Intel., 115, 105151, https://doi.org/10.1016/j.engappai.2022.105151, 2022.
Good, S. P., Noone, D., and Bowen, G.: Hydrologic connectivity constrains partitioning of global terrestrial water fluxes, Science, 349, 175–177, https://doi.org/10.1126/science.aaa5931, 2015.
Granata, F.: Evapotranspiration evaluation models based on machine learning algorithms – A comparative study, Agr. Water Manage., 217, 303–315, https://doi.org/10.1016/j.agwat.2019.03.015, 2019.
Greve, P. and Seneviratne, S. I.: Assessment of future changes in water availability and aridity, Geophys. Res. Lett., 42, 5493–5499, https://doi.org/10.1002/2015GL064127, 2015.
Guo, X., Yao, Y., Tang, Q., Liang, S., Shao, C., Fisher, J. B., Chen, J., Jia, K., Zhang, X., Shang, K., Yang, J., Yu, R., Xie, Z., Liu, L., Ning, J., and Zhang, L.: Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm, Agr. Forest Meteorol., 349, 109962, https://doi.org/10.1016/j.agrformet.2024.109962, 2024.
Huang, T. and Merwade, V.: Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling, Water Resour. Res., 59, e2023WR034947, https://doi.org/10.1029/2023WR034947, 2023.
Huntington, T. G.: Evidence for intensification of the global water cycle: Review and synthesis, J. Hydrol., 319, 83–95, https://doi.org/10.1016/j.jhydrol.2005.07.003, 2006.
Jiménez, C., Martens, B., Miralles, D. M., Fisher, J. B., Beck, H. E., and Fernández-Prieto, D.: Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements, Hydrol. Earth Syst. Sci., 22, 4513–4533, https://doi.org/10.5194/hess-22-4513-2018, 2018.
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, A. J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B. E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D., Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N., Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline in the global land evapotranspiration trend due to limited moisture supply, Nature, 467, 951–954, https://doi.org/10.1038/nature09396, 2010.
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M., Vaccari, F., and Williams, C.: Global patterns of land–atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations, J. Geophys. Res.-Biogeo., 116, https://doi.org/10.1029/2010JG001566, 2011.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land–atmosphere energy fluxes, Scientific Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Karbasi, M., Jamei, M., Ali, M., Malik, A., and Yaseen, Z. M.: Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer, Comput. Electron. Agr., 198, 107121, https://doi.org/10.1016/j.compag.2022.107121, 2022.
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., and Kumar, V.: Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data, IEEE T. Knowl. Data En., 29, 2318–2331, https://doi.org/10.1109/TKDE.2017.2720168, 2017.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in: Neural Information Processing Systems, https://api.semanticscholar.org/CorpusID:3815895 (last access: 24 April 2026), 2017.
Koppa, A., Rains, D., Hulsman, P., Poyatos, R., and Miralles, D. G.: A deep learning-based hybrid model of global terrestrial evaporation, Nat. Commun., 13, 1912, https://doi.org/10.1038/s41467-022-29543-7, 2022.
Kustas, W. P. and Norman, J. M.: A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations, Water Resour. Res., 33, 1495–1508, https://doi.org/10.1029/97WR00704, 1997.
Leuning, R., Zhang, Y. Q., Rajaud, A., Cleugh, H., and Tu, K.: A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman–Monteith equation, Water Resour. Res., 44, https://doi.org/10.1029/2007WR006562, 2008.
Li, C., Yang, H., Yang, W., Liu, Z., Jia, Y., Li, S., and Yang, D.: Error characterization of global land evapotranspiration products: Collocation-based approach, J. Hydrol., 612, 128102, https://doi.org/10.1016/j.jhydrol.2022.128102, 2022.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, 2023.
Liu, G., Tang, Z., Qin, H., Liu, S., Shen, Q., Qu, Y., and Zhou, J.: Short-term runoff prediction using deep learning multi-dimensional ensemble method, J. Hydrol., 609, 127762, https://doi.org/10.1016/j.jhydrol.2022.127762, 2022.
Liu, J., Chai, L., Dong, J., Zheng, D., Wigneron, J.-P., Liu, S., Zhou, J., Xu, T., Yang, S., Song, Y., Qu, Y., and Lu, Z.: Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method, Remote Sens. Environ., 255, 112225, https://doi.org/10.1016/j.rse.2020.112225, 2021.
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, arXiv [preprint], https://doi.org/10.48550/arXiv.1705.07874, 2017.
Lyu, Y. and Yong, B.: A Novel Double Machine Learning Strategy for Producing High-Precision Multi-Source Merging Precipitation Estimates Over the Tibetan Plateau, Water Resour. Res., 60, e2023WR035643, https://doi.org/10.1029/2023WR035643, 2024.
Ma, N.: Dataset of the water-balance-based evapotranspiration of global typical large river basins (1983–2016), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.300493, 2024.
Ma, N., Zhang, Y., and Szilagyi, J.: Water-balance-based evapotranspiration for 56 large river basins: A benchmarking dataset for global terrestrial evapotranspiration modeling, J. Hydrol., 630, 130607, https://doi.org/10.1016/j.jhydrol.2024.130607, 2024.
Mallick, K., Boegh, E., Trebs, I., Alfieri, J. G., Kustas, W. P., Prueger, J. H., Niyogi, D., Das, N., Drewry, D. T., Hoffmann, L., and Jarvis, A. J.: Reintroducing radiometric surface temperature into the Penman–Monteith formulation, Water Resour. Res., 51, 6214–6243, https://doi.org/10.1002/2014WR016106, 2015.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C., Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.: Reconciling the optimal and empirical approaches to modelling stomatal conductance, Glob. Change Biol., 17, 2134–2144, https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011.
Milly, P. C. D., Dunne, K. A., and Vecchia, A. V.: Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438, 347–350, https://doi.org/10.1038/nature04312, 2005.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.: Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges, Ann. NY Acad. Sci., 1436, 19–35, https://doi.org/10.1111/nyas.13912, 2019.
Mizoguchi, Y., Miyata, A., Ohtani, Y., Hirata, R., and Yuta, S.: A review of tower flux observation sites in Asia, J. Forest Res.-Jpn., 14, 1–9, https://doi.org/10.1007/s10310-008-0101-9, 2009.
Mohammed, A. and Kora, R.: A comprehensive review on ensemble deep learning: Opportunities and challenges, Journal of King Saud University – Computer and Information Sciences, 35, 757–774, https://doi.org/10.1016/j.jksuci.2023.01.014, 2023.
Monteith, J. L.: Evaporation and environment, Sym. Soc. Exp. Biol., 19, 205–234, 1965.
Mu, Q., Heinsch, F. A., Zhao, M., and Running, S. W.: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111, 519–536, https://doi.org/10.1016/j.rse.2007.04.015, 2007.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Muñoz Sabater: ERA5-Land monthly averaged data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019.
Ni, L., Wang, W., Fu, J. and Cao, M.: ET Dataset for paper “A Novel Classifier-Guided Ensemble Framework for Global Terrestrial Evapotranspiration Estimates”, Zenodo [data set], https://doi.org/10.5281/zenodo.18543737, 2026.
Ochege, F. U., Yuan, X., Ezekwe, I. C., Ling, Q., Nzabarinda, V., Kayiranga, A., Xie, M., Shi, H., and Luo, G.: Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method, J. Hydrol., 634, 131115, https://doi.org/10.1016/j.jhydrol.2024.131115, 2024.
Oki, T. and Kanae, S.: Global Hydrological Cycles and World Water Resources, Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845, 2006.
de Oliveira e Lucas, P., Alves, M. A., de Lima e Silva, P. C., and Guimarães, F. G.: Reference evapotranspiration time series forecasting with ensemble of convolutional neural networks, Comput. Electron. Agr., 177, 105700, https://doi.org/10.1016/j.compag.2020.105700, 2020.
Pascolini-Campbell, M. A., Reager, J. T., and Fisher, J. B.: GRACE-based Mass Conservation as a Validation Target for Basin-Scale Evapotranspiration in the Contiguous United States, Water Resour. Res., 56, e2019WR026594, https://doi.org/10.1029/2019WR026594, 2020.
Pastorello, G., Trotta, C., Canfora, E., et al.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Penman, H. L.: Natural evaporation from open water, bare soil and grass, P. R. Soc. Lond. A-Conta., 193, 120–145, https://doi.org/10.1098/rspa.1948.0037, 1948.
Pérez-Rodríguez, J., Fernández-Navarro, F., and Ashley, T.: Estimating ensemble weights for bagging regressors based on the mean–variance portfolio framework, Expert Syst. Appl., 229, 120462, https://doi.org/10.1016/j.eswa.2023.120462, 2023.
Polhamus, A., Fisher, J. B., and Tu, K. P.: What controls the error structure in evapotranspiration models?, Agr. Forest Meteorol., 169, 12–24, https://doi.org/10.1016/j.agrformet.2012.10.002, 2013.
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large Scale Parameters, Mon. Weather Rev., 100, 81–92, 1972.
Purdy, A. J., Fisher, J. B., Goulden, M. L., Colliander, A., Halverson, G., Tu, K., and Famiglietti, J. S.: SMAP soil moisture improves global evapotranspiration, Remote Sens. Environ., 219, 1–14, https://doi.org/10.1016/j.rse.2018.09.023, 2018.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Reitz, M., Sanford, W. E., and Saxe, S.: Ensemble Estimation of Historical Evapotranspiration for the Conterminous U. S., Water Resour. Res., 59, e2022WR034012, https://doi.org/10.1029/2022WR034012, 2023.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., Silva, A. da, Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Schwalm, C. R., Anderegg, W. R. L., Michalak, A. M., Fisher, J. B., Biondi, F., Koch, G., Litvak, M., Ogle, K., Shaw, J. D., Wolf, A., Huntzinger, D. N., Schaefer, K., Cook, R., Wei, Y., Fang, Y., Hayes, D., Huang, M., Jain, A., and Tian, H.: Global patterns of drought recovery, Nature, 548, 202–205, https://doi.org/10.1038/nature23021, 2017.
Shang, K., Yao, Y., Di, Z., Jia, K., Zhang, X., Fisher, J. B., Chen, J., Guo, X., Yang, J., Yu, R., Xie, Z., Liu, L., Ning, J., and Zhang, L.: Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau, Remote Sens. Environ., 289, 113519, https://doi.org/10.1016/j.rse.2023.113519, 2023.
Shapley, L. S.: A Value for N-Person Games, RAND Corporation, Santa Monica, CA, https://doi.org/10.7249/P0295, 1952.
da Silva Júnior, J. C., Medeiros, V., Garrozi, C., Montenegro, A., and Gonçalves, G. E.: Random forest techniques for spatial interpolation of evapotranspiration data from Brazilian's Northeast, Comput. Electron. Agr., 166, 105017, https://doi.org/10.1016/j.compag.2019.105017, 2019.
Su, Z.: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sci., 6, 85–100, https://doi.org/10.5194/hess-6-85-2002, 2002.
Tavella, P. and Premoli, A.: Estimating the Instabilities of N Clocks by Measuring Differences of their Readings, Metrologia, 30, 479, https://doi.org/10.1088/0026-1394/30/5/003, 1994.
Teuling, A. J., Hirschi, M., Ohmura, A., Wild, M., Reichstein, M., Ciais, P., Buchmann, N., Ammann, C., Montagnani, L., Richardson, A. D., Wohlfahrt, G., and Seneviratne, S. I.: A regional perspective on trends in continental evaporation, Geophys. Res. Lett., 36, https://doi.org/10.1029/2008GL036584, 2009.
Torcaso, F., Ekstrom, C. R., Burt, E., and Matsaki, D.: Estimating Frequency Stability and Cross-Correlations, in: Proceedings of the 30th Annual Precise Time and Time Interval Systems and Applications Meeting, Reston, Virginia, December 1998, 69–82, https://www.ion.org/publications/abstract.cfm?articleID=14124 (last access: 24 April 2026), 1998.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, B. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1, 2009.
Tseng, M.-H.: GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers, Mach. Learn.: Sci. Technol., 4, 045045, https://doi.org/10.1088/2632-2153/ad10cf, 2023.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P. R., Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L.: Correcting eddy-covariance flux underestimates over a grassland, Agr. Forest Meteorol., 103, 279–300, https://doi.org/10.1016/S0168-1923(00)00123-4, 2000.
Wang, D. and Alimohammadi, N.: Responses of annual runoff, evaporation, and storage change to climate variability at the watershed scale, Water Resour. Res., 48, W05546, https://doi.org/10.1029/2011WR011444, 2012.
Wang, K. and Dickinson, R. E.: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability, Rev. Geophys., 50, https://doi.org/10.1029/2011RG000373, 2012.
Wang, K., Dickinson, R. E., Wild, M., and Liang, S.: Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD013671, 2010a.
Wang, K., Dickinson, R. E., Wild, M., and Liang, S.: Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 2. Results, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2010JD013847, 2010b.
Willard, J., Jia, X., Xu, S., Steinbach, M., and Kumar, V.: Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems, ACM Comput. Surv., 55, 66:1–66:37, https://doi.org/10.1145/3514228, 2022.
Williams, D. G., Cable, W., Hultine, K., Hoedjes, J. C. B., Yepez, E. A., Simonneaux, V., Er-Raki, S., Boulet, G., Bruin, H. A. R. de, Chehbouni, A., Hartogensis, O. K., and Timouk, F.: Evapotranspiration components determined by stable isotope, sap flow and eddy covariance techniques, Agr. Forest Meteorol., 125, 241–258, https://doi.org/10.1016/j.agrformet.2004.04.008, 2004.
Wilson, K. B., Hanson, P. J., Mulholland, P. J., Baldocchi, D. D., and Wullschleger, S. D.: A comparison of methods for determining forest evapotranspiration and its components: sap-flow, soil water budget, eddy covariance and catchment water balance, Agr. Forest Meteorol., 106, 153–168, https://doi.org/10.1016/S0168-1923(00)00199-4, 2001.
Xiao, J., Zhuang, Q., Baldocchi, D. D., Law, B. E., Richardson, A. D., Chen, J., Oren, R., Starr, G., Noormets, A., Ma, S., Verma, S. B., Wharton, S., Wofsy, S. C., Bolstad, P. V., Burns, S. P., Cook, D. R., Curtis, P. S., Drake, B. G., Falk, M., Fischer, M. L., Foster, D. R., Gu, L., Hadley, J. L., Hollinger, D. Y., Katul, G. G., Litvak, M., Martin, T. A., Matamala, R., McNulty, S., Meyers, T. P., Monson, R. K., Munger, J. W., Oechel, W. C., Paw U, K. T., Schmid, H. P., Scott, R. L., Sun, G., Suyker, A. E., and Torn, M. S.: Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data, Agr. Forest Meteorol., 148, 1827–1847, https://doi.org/10.1016/j.agrformet.2008.06.015, 2008.
Xie, X., Liang, S., Yao, Y., Jia, K., Meng, S., and Li, J.: Detection and attribution of changes in hydrological cycle over the Three-North region of China: Climate change versus afforestation effect, Agr. Forest Meteorol. 203, 74–87, https://doi.org/10.1016/j.agrformet.2015.01.003, 2015.
Xie, Z., Yao, Y., Tang, Q., Liu, M., Fisher, J. B., Chen, J., Zhang, X., Jia, K., Li, Y., Shang, K., Jiang, B., Yang, J., Yu, R., Zhang, X., Guo, X., Liu, L., Ning, J., Fan, J., and Zhang, L.: Evaluation of seven satellite-based and two reanalysis global terrestrial evapotranspiration products, J. Hydrol., 630, 130649, https://doi.org/10.1016/j.jhydrol.2024.130649, 2024.
Xu, T., Guo, Z., Liu, S., He, X., Meng, Y., Xu, Z., Xia, Y., Xiao, J., Zhang, Y., Ma, Y., and Song, L.: Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale, J. Geophys. Res.-Atmos., 123, 8674–8690, https://doi.org/10.1029/2018JD028447, 2018.
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., and Zhang, L.: Deep learning in environmental remote sensing: Achievements and challenges, Remote Sens. Environ., 241, 111716, https://doi.org/10.1016/j.rse.2020.111716, 2020.
Zhang, C., Brodylo, D., Rahman, M., Rahman, M. A., Douglas, T. A., and Comas, X.: Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland, Sci. Total Environ., 831, 154969, https://doi.org/10.1016/j.scitotenv.2022.154969, 2022.
Zhang, C., Zhou, C., Luo, G., Ye, S., and Shi, Z.: Physics-constrained machine learning for satellite-derived evapotranspiration in China, J. Hydrol., 660, 133512, https://doi.org/10.1016/j.jhydrol.2025.133512, 2025.
Zhang, K., Kimball, J. S., Nemani, R. R., Running, S. W., Hong, Y., Gourley, J. J., and Yu, Z.: Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration, Sci. Rep.-UK, 5, 15956, https://doi.org/10.1038/srep15956, 2015.
Zhang, K., Zhu, G., Ma, J., Yang, Y., Shang, S., and Gu, C.: Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification, Water Resour. Res., 55, 2211–2231, https://doi.org/10.1029/2018WR023485, 2019.
Zhang, Z., Qin, H., Li, J., Liu, Y., Yao, L., Wang, Y., Wang, C., Pei, S., Li, P., and Zhou, J.: Operation rule extraction based on deep learning model with attention mechanism for wind-solar-hydro hybrid system under multiple uncertainties, Renewable Energy, 170, 92–106, https://doi.org/10.1016/j.renene.2021.01.115, 2021.
Zhangzhong, L., Gao, H., Zheng, W., Wu, J., Li, J., and Wang, D.: Development of an evapotranspiration estimation method for lettuce via mobile phones using machine vision: Proof of concept, Agr. Water Manage., 275, 108003, https://doi.org/10.1016/j.agwat.2022.108003, 2023.
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., and Qiu, G. Y.: Physics-Constrained Machine Learning of Evapotranspiration, Geophys. Res. Lett., 46, 14496–14507, https://doi.org/10.1029/2019GL085291, 2019.
Zhu, W., Tian, S., Wei, J., Jia, S., and Song, Z.: Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: Accuracy and uncertainty, J. Hydrol., 611, 127982, https://doi.org/10.1016/j.jhydrol.2022.127982, 2022.
Editorial statement
Evapotranspiration estimates often contain substantial uncertainties, while direct observations remain sparse. This study offers a practical and innovative framework for selecting the most appropriate model for Global Terrestrial Evapotranspiration Estimates for specific locations. The authors present a Classifier-Guided Ensemble approach that integrates process-based algorithms, machine learning models, and hybrid methods to identify the most suitable model for a given setting and subsequently estimate evapotranspiration (ET).
Evapotranspiration estimates often contain substantial uncertainties, while direct observations...
Short summary
Existing global evapotranspiration algorithms rely on sparse local measurements and each comes with its own strengths and weaknesses. Here, we proposed an ensemble framework that employed a machine learning system to dynamically select the most appropriate algorithm to be used across spatial and temporal scales, thus fully utilizing the distinct strengths of each method. In multi-scale validations, our framework exhibited enhanced extrapolation performance, stability, and interpretability.
Existing global evapotranspiration algorithms rely on sparse local measurements and each comes...
