{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T19:52:07Z","timestamp":1781553127242,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901342"],"award-info":[{"award-number":["41901342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31671585"],"award-info":[{"award-number":["31671585"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Basic Research Project of Shandong Natural Science Foundation of China","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimates of evapotranspiration (ET) over croplands on a regional scale can provide useful information for agricultural management. The hybrid ET model that combines the physical framework, namely the Penman-Monteith equation and machine learning (ML) algorithms, have proven to be effective in ET estimates. However, few studies compared the performances in estimating ET between multiple hybrid model versions using different ML algorithms. In this study, we constructed six different hybrid ET models based on six classical ML algorithms, namely the K nearest neighbor algorithm, random forest, support vector machine, extreme gradient boosting algorithm, artificial neural network (ANN) and long short-term memory (LSTM), using observed data of 17 eddy covariance flux sites of cropland over the globe. Each hybrid model was assessed to estimate ET with ten different input data combinations. In each hybrid model, the ML algorithm was used to model the stomatal conductance (Gs), and then ET was estimated using the Penman-Monteith equation, along with the ML-based Gs. The results showed that all hybrid models can reasonably reproduce ET of cropland with the models using two or more remote sensing (RS) factors. The results also showed that although including RS factors can remarkably contribute to improving ET estimates, hybrid models except for LSTM using three or more RS factors were only marginally better than those using two RS factors. We also evidenced that the ANN-based model exhibits the optimal performance among all ML-based models in modeling daily ET, as indicated by the lower root-mean-square error (RMSE, 18.67\u201321.23 W m\u22122) and higher correlations coefficient (r, 0.90\u20130.94). ANN are more suitable for modeling Gs as compared to other ML algorithms under investigation, being able to provide methodological support for accurate estimation of cropland ET on a regional scale.<\/jats:p>","DOI":"10.3390\/rs13193838","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"3838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-2376","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sha","family":"Zhang","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Tang","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3477-7884","authenticated-orcid":false,"given":"Yun","family":"Bai","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"RG2005","DOI":"10.1029\/2011RG000373","article-title":"A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability","volume":"50","author":"Wang","year":"2012","journal-title":"Rev. Geophys."},{"key":"ref_2","first-page":"1355","article-title":"Spatio-temporal changes of agricultural irrigation water demand in the Lancang River Basin in the past 50 years","volume":"65","author":"Gu","year":"2010","journal-title":"Acta Geogr. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s00704-012-0734-7","article-title":"Spatial and temporal changes in aridity index in northwest China: 1960 to 2010","volume":"112","author":"Liu","year":"2012","journal-title":"Theor. Appl. Climatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.agrformet.2019.02.024","article-title":"Estimation of evapotranspiration using the crop canopy temperature at field to regional scales in large irrigation district","volume":"269","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4292","DOI":"10.1002\/hyp.10824","article-title":"Evaluation of evapotranspiration models over semi-arid and semi-humid areas of China","volume":"30","author":"Yang","year":"2016","journal-title":"Hydrol. Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s00704-018-2624-0","article-title":"Evaluation of the Penman-Monteith and other 34 reference evapotranspiration equations under limited data in a semiarid dry climate","volume":"137","author":"Djaman","year":"2018","journal-title":"Theor. Appl. Climatol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Muhammad, M.K.I., Nashwan, M., Shahid, S., Ismail, T., Song, Y., and Chung, E.-S. (2019). Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia. Sustainability, 11.","DOI":"10.3390\/su11164267"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.rse.2006.07.007","article-title":"Regional evaporation estimates from flux tower and MODIS satellite data","volume":"106","author":"Cleugh","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2007.04.015","article-title":"Development of a global evapotranspiration algorithm based on MODIS and global meteorology data","volume":"111","author":"Mu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1029\/2007WR006562","article-title":"A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation","volume":"44","author":"Leuning","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1061\/(ASCE)HE.1943-5584.0000366","article-title":"Local Calibration of the Hargreaves and Priestley-Taylor Equations for Estimating Reference Evapotranspiration in Arid and Cold Climates of Iran Based on the Penman-Monteith Model","volume":"16","author":"Tabari","year":"2011","journal-title":"J. Hydrol. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.jhydrol.2016.04.002","article-title":"Modification of evapotranspiration model based on effective resistance to estimate evapotranspiration of maize for seed production in an arid region of northwest China","volume":"538","author":"Jiang","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.rse.2012.06.004","article-title":"Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model","volume":"124","author":"Yan","year":"2012","journal-title":"Remote. Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105430","DOI":"10.1016\/j.compag.2020.105430","article-title":"Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data","volume":"173","author":"Zhu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s12517-019-4781-6","article-title":"Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs","volume":"12","author":"Adnan","year":"2019","journal-title":"Arab. J. Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104937","DOI":"10.1016\/j.compag.2019.104937","article-title":"Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data","volume":"165","author":"Reis","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compag.2016.11.011","article-title":"Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables","volume":"132","author":"Antonopoulos","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.agwat.2017.08.003","article-title":"Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling","volume":"193","author":"Feng","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"105875","DOI":"10.1016\/j.agwat.2019.105875","article-title":"Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data","volume":"228","author":"Todorovic","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"125286","DOI":"10.1016\/j.jhydrol.2020.125286","article-title":"Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods","volume":"591","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106113","DOI":"10.1016\/j.agwat.2020.106113","article-title":"New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning","volume":"234","author":"Ferreira","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_24","first-page":"100409","article-title":"Comparison of winter wheat NDVI data derived from Landsat 8 and active optical sensor at field scale","volume":"20","author":"Gozdowski","year":"2020","journal-title":"Remote. Sens. Appl. Soc. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3731","DOI":"10.1111\/gcb.14729","article-title":"Terrestrial gross primary production: Using NIR V to scale from site to globe","volume":"25","author":"Badgley","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.compgeo.2013.08.010","article-title":"Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns","volume":"55","author":"Tinoco","year":"2014","journal-title":"Comput. Geotech."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"491","DOI":"10.13031\/2013.2730","article-title":"Estimating daily pan evaporation with artificial neural networks","volume":"43","author":"Bruton","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/S0378-4371(00)00479-9","article-title":"Using genetic algorithms to select architecture of a feedforward artificial neural network","volume":"289","author":"Arifovic","year":"2001","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.biosystemseng.2017.09.015","article-title":"Modified Penman\u2013Monteith equation for monitoring evapotranspiration of wheat crop: Relationship between the surface resistance and remotely sensed stress index","volume":"164","author":"Amazirh","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.agwat.2015.09.009","article-title":"Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate","volume":"163","author":"Yassin","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105728","DOI":"10.1016\/j.compag.2020.105728","article-title":"Multi-step ahead forecasting of daily reference evapotranspiration using deep learning","volume":"178","author":"Ferreira","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106386","DOI":"10.1016\/j.agwat.2020.106386","article-title":"Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)","volume":"242","author":"Yin","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107040","DOI":"10.1016\/j.agwat.2021.107040","article-title":"Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks","volume":"255","author":"Granata","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105206","DOI":"10.1016\/j.compag.2019.105206","article-title":"Temporal convolution-network-based models for modeling maize evapotranspiration under mulched drip irrigation","volume":"169","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"126579","DOI":"10.1016\/j.jhydrol.2021.126579","article-title":"Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the Midwest U.S","volume":"600","author":"Talib","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s00484-011-0485-7","article-title":"Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions","volume":"56","author":"Laaboudi","year":"2012","journal-title":"Int. J. Biometeorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.jhydrol.2019.03.028","article-title":"Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM\u2014A new approach","volume":"572","author":"Ferreira","year":"2019","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:05:04Z","timestamp":1760166304000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":43,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193838"],"URL":"https:\/\/doi.org\/10.3390\/rs13193838","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,25]]}}}