{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T03:58:33Z","timestamp":1774583913514,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T00:00:00Z","timestamp":1696464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42201407"],"award-info":[{"award-number":["42201407"]}]},{"name":"National Natural Science Foundation of China","award":["42101382"],"award-info":[{"award-number":["42101382"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2022QD120"],"award-info":[{"award-number":["ZR2022QD120"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QD016"],"award-info":[{"award-number":["ZR2020QD016"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["42201407"],"award-info":[{"award-number":["42201407"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["42101382"],"award-info":[{"award-number":["42101382"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["ZR2022QD120"],"award-info":[{"award-number":["ZR2022QD120"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["ZR2020QD016"],"award-info":[{"award-number":["ZR2020QD016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W\/m2. The average annual T\/ET of the examined ten ecosystems was 0.50 \u00b1 0.08, with the highest value in deciduous broadleaf forests (0.68 \u00b1 0.11), followed by mixed forests (0.61 \u00b1 0.04), and the lowest in croplands (0.40 \u00b1 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T\/ET. Overall, at the interannual scale, LAI contributed 28% to the T\/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T\/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T\/ET estimation.<\/jats:p>","DOI":"10.3390\/rs15194831","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T09:14:22Z","timestamp":1696497262000},"page":"4831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Ecosystem Evapotranspiration Partitioning and Its Spatial\u2013Temporal Variation Based on Eddy Covariance Observation and Machine Learning Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Linjun","family":"Lu","sequence":"first","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Danwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Sha","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Yun","family":"Bai","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Shanshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1038\/nature09396","article-title":"Recent decline in the global land evapotranspiration trend due to limited moisture supply","volume":"467","author":"Jung","year":"2010","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E1897","DOI":"10.1175\/BAMS-D-19-0316.1","article-title":"Closing the water cycle from observations across scales: Where do we stand?","volume":"102","author":"Dorigo","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1175\/2008BAMS2634.1","article-title":"Earth\u2019s global energy budget","volume":"90","author":"Trenberth","year":"2009","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1038\/s41558-018-0207-9","article-title":"Partitioning global land evapotranspiration using CMIP5 models constrained by observations","volume":"8","author":"Lian","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_5","unstructured":"Baldocchi, D.D., and Ryu, Y. (2011). Forest Hydrology and Biogeochemistry, Springer. Ecological Studies."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.agrformet.2015.12.003","article-title":"Evapotranspiration partitioning through in-situ oxygen isotope measurements in an oasis cropland","volume":"230\u2013231","author":"Wen","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.agwat.2016.08.012","article-title":"Partitioning of evapotranspiration using a stable isotope technique in an arid and high temperature agricultural production system","volume":"179","author":"Lu","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.agwat.2012.10.003","article-title":"Combined use of eddy covariance and sap flow techniques for partition of ET fluxes and water stress assessment in an irrigated olive orchard","volume":"120","author":"Cammalleri","year":"2013","journal-title":"Agric. Water Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.agrformet.2009.09.005","article-title":"Partitioning carbon dioxide and water vapor fluxes using correlation analysis","volume":"150","author":"Scanlon","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1002\/2015WR017766","article-title":"Partitioning evapotranspiration based on the concept of underlying water use efficiency","volume":"52","author":"Zhou","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107701","DOI":"10.1016\/j.agrformet.2019.107701","article-title":"An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming","volume":"279","author":"Niu","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108100","DOI":"10.1016\/j.agrformet.2020.108100","article-title":"Shifts in ecosystem water use efficiency on china\u2019s loess plateau caused by the interaction of climatic and biotic factors over 1985\u20132015","volume":"291","author":"Cao","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2018.12.031","article-title":"Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002\u20132017","volume":"222","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108800","DOI":"10.1016\/j.agrformet.2021.108800","article-title":"Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements","volume":"314","author":"Zhou","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.agrformet.2018.11.017","article-title":"A simple and objective method to partition evapotranspiration into transpiration and evaporation at eddy-covariance sites","volume":"265","author":"Li","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3617","DOI":"10.1029\/2018JG004727","article-title":"Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm","volume":"123","author":"Nelson","year":"2018","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_17","first-page":"56","article-title":"Evapotranspiration Partitioning Based on Leaf and Ecosystem Water Use Efficiency","volume":"184","author":"Liuyang","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"G00J07","DOI":"10.1029\/2010JG001566","article-title":"Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations","volume":"116","author":"Jung","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108528","DOI":"10.1016\/j.agrformet.2021.108528","article-title":"Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands","volume":"308\u2013309","author":"Irvin","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1111\/gcb.14845","article-title":"Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis","volume":"26","author":"Kim","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14496","DOI":"10.1029\/2019GL085291","article-title":"Physics-Constrained Machine Learning of Evapotranspiration","volume":"46","author":"Zhao","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1046\/j.1365-2486.2003.00609.x","article-title":"A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization","volume":"9","author":"Papale","year":"2003","journal-title":"Glob. Chang. Biol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5235","DOI":"10.1111\/gcb.15203","article-title":"Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks","volume":"26","author":"Tramontana","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1111\/gcb.15974","article-title":"A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems","volume":"28","author":"Eichelmann","year":"2022","journal-title":"Glob. Chang. Biol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.jhydrol.2009.04.036","article-title":"Comparing the Penman\u2013Monteith equation and a modified Jarvis\u2013Stewart model with an artificial neural network to estimate stand-scale transpiration and canopy conductance","volume":"373","author":"Whitley","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.jhydrol.2017.05.027","article-title":"Comparing three models to estimate transpiration of desert shrubs","volume":"550","author":"Xu","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106547","DOI":"10.1016\/j.agwat.2020.106547","article-title":"Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models","volume":"245","author":"Fan","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/s41597-021-00851-9","article-title":"Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data","volume":"8","author":"Pastorello","year":"2021","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1111\/gcb.13893","article-title":"Towards physiologically meaningful water-use efficiency estimates from eddy covariance data","volume":"24","author":"Knauer","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1111\/nph.14626","article-title":"How do leaf and ecosystem measures of water-use efficiency compare?","volume":"216","author":"Medlyn","year":"2017","journal-title":"New Phytol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"110212","DOI":"10.1016\/j.ecolmodel.2022.110212","article-title":"Simulating canopy carbonyl sulfide uptake of two forest stands through an improved ecosystem model and parameter optimization using an ensemble Kalman filter","volume":"475","author":"Chen","year":"2023","journal-title":"Ecol. Model."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: A state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Dutra","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107959","DOI":"10.1016\/j.agrformet.2020.107959","article-title":"The potential of remote sensing-based models on global water-use efficiency estimation: An evaluation and intercomparison of an ecosystem model (BESS) and algorithm (MODIS) using site level and upscaled eddy covariance data","volume":"287","author":"Yang","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3354","DOI":"10.1109\/JBHI.2022.3151091","article-title":"XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging","volume":"26","author":"Shin","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_36","unstructured":"Liashchynskyi, P., and Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: A big comparison for NAS. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agrformet.2018.08.019","article-title":"Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China","volume":"263","author":"Fan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5194\/adgeo-5-89-2005","article-title":"Comparison of different efficiency criteria for hydrological model assessment","volume":"5","author":"Krause","year":"2005","journal-title":"Adv. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105653","DOI":"10.1016\/j.compag.2020.105653","article-title":"Uncertainty analysis of artificial intelligence modeling daily reference evapotranspiration in the northwest end of China","volume":"176","author":"Yu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.5194\/bg-16-3747-2019","article-title":"Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities","volume":"16","author":"Stoy","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108384","DOI":"10.1016\/j.agrformet.2021.108384","article-title":"Regional contributions to interannual variability of net primary production and climatic attributions","volume":"303","author":"Li","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106996","DOI":"10.1016\/j.agwat.2021.106996","article-title":"Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China","volume":"255","author":"Ding","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1111\/gcb.13830","article-title":"Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years","volume":"24","author":"Yao","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1038\/nclimate2177","article-title":"Nutrient availability as the key regulator of global forest carbon balance","volume":"4","author":"Vicca","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.2166\/nh.2016.099","article-title":"Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau","volume":"48","author":"Cui","year":"2017","journal-title":"Hydrol. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.compag.2018.07.029","article-title":"Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands","volume":"152","author":"Tang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107608","DOI":"10.1016\/j.agrformet.2019.06.007","article-title":"Improvement of sap flow estimation by including phenological index and time-lag effect in back-propagation neural network models","volume":"276\u2013277","author":"Tu","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Montesinos L\u00f3pez, O.A., Montesinos L\u00f3pez, A., and Crossa, J. (2022). Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer.","DOI":"10.1007\/978-3-030-89010-0"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2022.04.083","article-title":"A comprehensive survey on recent metaheuristics for feature selection","volume":"494","author":"Dokeroglu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.agrformet.2018.05.023","article-title":"Partitioning evapotranspiration using an optimized satellite-based ET model across biomes","volume":"259","author":"Gu","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.agrformet.2014.01.011","article-title":"Transpiration in the global water cycle","volume":"189\u2013190","author":"Schlesinger","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6753","DOI":"10.1002\/2014GL061439","article-title":"Global synthesis of vegetation control on evapotranspiration partitioning","volume":"41","author":"Wang","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1126\/science.aaa5931","article-title":"Hydrologic connectivity constrains partitioning of global terrestrial water fluxes","volume":"349","author":"Good","year":"2015","journal-title":"Science"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1126\/science.aaf7891","article-title":"Connections between groundwater flow and transpiration partitioning","volume":"353","author":"Maxwell","year":"2016","journal-title":"Science"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6795","DOI":"10.1002\/2017GL074041","article-title":"Constrained variability of modeled T: ET ratio across biomes","volume":"44","author":"Fatichi","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105923","DOI":"10.1016\/j.agwat.2019.105923","article-title":"Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland","volume":"228","author":"Chen","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.agrformet.2008.07.004","article-title":"Partitioning evapotranspiration in semiarid grassland and shrubland ecosystems using time series of soil surface temperature","volume":"149","author":"Moran","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.agrformet.2012.01.015","article-title":"Dynamics of evapotranspiration partitioning in a semi-arid forest as affected by temporal rainfall patterns","volume":"157","author":"Yakir","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1111\/j.1365-2486.2008.01582.x","article-title":"Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China","volume":"14","author":"Hu","year":"2008","journal-title":"Glob. Chang. Biol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jhydrol.2019.06.022","article-title":"Evapotranspiration partitioning in dryland ecosystems: A global meta-analysis of in situ studies","volume":"576","author":"Sun","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.agrformet.2018.01.019","article-title":"Evapotranspiration partitioning for three agro-ecosystems with contrasting moisture conditions: A comparison of an isotope method and a two-source model calculation","volume":"252","author":"Wei","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"108984","DOI":"10.1016\/j.agrformet.2022.108984","article-title":"Spatiotemporal variations in the ratio of transpiration to evapotranspiration and its controlling factors across terrestrial biomes","volume":"321","author":"Cao","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1111\/gcb.14875","article-title":"How afforestation affects the water cycle in drylands: A process-based comparative analysis","volume":"26","author":"Zhang","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Beer, C., Ciais, P., Reichstein, M., Baldocchi, D., Law, B.E., Papale, D., Soussana, J.F., Ammann, C., Buchmann, N., and Frank, D. (2009). Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles, 23.","DOI":"10.1029\/2008GB003233"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1111\/gcb.15314","article-title":"Ecosystem transpiration and evaporation: Insights from three water flux partitioning methods across FLUXNET sites","volume":"26","author":"Nelson","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6833","DOI":"10.1002\/2017GL074324","article-title":"Partitioning evapotranspiration using long-term carbon dioxide and water vapor fluxes","volume":"44","author":"Scott","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agrformet.2012.11.004","article-title":"A data-driven analysis of energy balance closure across FLUXNET research sites: The role of landscape scale heterogeneity","volume":"171","author":"Stoy","year":"2013","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4831\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:01:34Z","timestamp":1760130094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,5]]},"references-count":67,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194831"],"URL":"https:\/\/doi.org\/10.3390\/rs15194831","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,5]]}}}