[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124713-en":3,"doc-seo-124713-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124713,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale","Accurate surface soil moisture information at global scale under diverse climatic conditions supports hydrological and climatological applications. An ML-driven workflow integrates in situ hydrological measurements with complex environmental, climate, and satellite observations to generate reliable data products for monitoring water, energy, and carbon exchange in the Earth system. The study estimates daily surface soil moisture using eight optimised ML algorithms and ten ensemble models built via bootstrap model aggregation and five-fold cross-validation on International Soil Moisture Network data from 1722 stations.","Geosci. Model Dev., 16, 5825–5845, 2023 [https://doi.org/10.5194/gmd-16-5825-2023](https://doi.org/10.5194/gmd-16-5825-2023)[ ](https://doi.org/10.5194/gmd-16-5825-2023)© Author(s) 2023 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nEnsemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale  \nQianqian Han 1 , Yijian Zeng 1 , Lijie Zhang2 , Calimanut-Ionut Cira3 , Egor Prikaziuk 1 , Ting Duan 1 , Chao Wang4 , Brigitta Szabó5 , Salvatore Manfreda6 , Ruodan Zhuang6 , and Bob Su 1,7  \n1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, the Netherlands  \n2Institute of Bio and Geosciences: Agrosphere (IBG-3), Research Center Jülich, 52428 Jülich, Germany  \n3Departamento de Ingeniería Topográfica y Cartográfica, E.T.S.I. en Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain  \n4Department of Earth, Marine and Environmental Sciences, University of North Carolina, Chapel Hill, NC 27514, USA  \n5Institute for Soil Sciences, Centre for Agricultural Research, 1022 Budapest, Hungary  \n6Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy  \n7 Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of the Ministry of Education, School of Water and Environment, Chang'an University, Xi'an 710054, China  \nCorrespondence: Bob Su ([z.su@utwente.nl](z.su@utwente.nl))  \nReceived: 25 April 202 – Discussion started: 8 June 2023  \nRevised: 30 August 2023 – Accepted: 6 September 2023 – Published: 19 October 2023  \nAbstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation) . The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest rootmean-square error (0 .0379 cm3 cm􀀀3) on the “test_random”set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0 .0599 cm3 cm􀀀3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0 .0786 cm3 cm􀀀3) on the “test_independent-stations” set  \n(for testing the performance on the stations that were not used in training) . Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0 . 1 cm3 cm􀀀3 . GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm􀀀3 for all climate zones. All voting regressors achieved r scores of above 0 .6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve m","cbCaimrxRjH6Azsc","https://ap.wps.com/l/cbCaimrxRjH6Azsc","pdf",2297955,1,21,"English","en",105,"# Abstract\n# Introduction\n## Surface soil moisture importance\n## Observations and remote sensing\n# Model evaluation and results\n## Test set comparisons\n## Ensemble performance across climate zones\n# Conclusions","[{\"question\":\"What data and methods are used to predict daily surface soil moisture at global scale?\",\"answer\":\"The approach trains and tests eight optimised ML algorithms and ten ensemble models using International Soil Moisture Network (ISMN) observations from 1722 stations, combined with environmental, climate, and satellite information. Ensembles are built using bootstrap aggregation and five-fold cross-validation.\"},{\"question\":\"How does performance differ across the different evaluation test sets?\",\"answer\":\"Results vary by test set: K-neighbours Regressor performs best on randomly split data, Random Forest Regressor on temporally withheld periods, and AdaBoost on independent-stations not used for training.\"},{\"question\":\"Why do ensemble models outperform single algorithms?\",\"answer\":\"Model evaluation shows ensembles improve accuracy and stability versus base ML models, with lower median RMSE across climate zones. Voting regressors achieve strong correlations, while exceptions occur where training station coverage is sparse.\"}]","Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale | PDF",1785894053,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ensemble-of-optimised-machine-learning-algorithms-for-predicting-surface-soil-moisture-content-at-a-global-scale","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ensemble-of-optimised-machine-learning-algorithms-for-predicting-surface-soil-moisture-content-at-a-global-scale/124713/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and methods are used to predict daily surface soil moisture at global scale?","Question",{"text":75,"@type":76},"The approach trains and tests eight optimised ML algorithms and ten ensemble models using International Soil Moisture Network (ISMN) observations from 1722 stations, combined with environmental, climate, and satellite information. Ensembles are built using bootstrap aggregation and five-fold cross-validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does performance differ across the different evaluation test sets?",{"text":80,"@type":76},"Results vary by test set: K-neighbours Regressor performs best on randomly split data, Random Forest Regressor on temporally withheld periods, and AdaBoost on independent-stations not used for training.",{"name":82,"@type":73,"acceptedAnswer":83},"Why do ensemble models outperform single algorithms?",{"text":84,"@type":76},"Model evaluation shows ensembles improve accuracy and stability versus base ML models, with lower median RMSE across climate zones. Voting regressors achieve strong correlations, while exceptions occur where training station coverage is sparse.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]