{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T18:51:38Z","timestamp":1781376698827,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T00:00:00Z","timestamp":1594944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Data Sharing Fundamental Program for the Construction of the National Science and Technology Infrastructure Platform","award":["Y719H71006"],"award-info":[{"award-number":["Y719H71006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Soil temperature (ST) plays a key role in the processes and functions of almost all ecosystems, and is also an essential parameter for various applications such as agricultural production, geothermal development, and their utilization. Although numerous machine learning models have been used in the prediction of ST, and good results have been obtained, most of the current studies have focused on daily or monthly ST predictions, while hourly ST predictions are scarce. This paper presents a novel scheme for forecasting the hourly ST using weather forecast data. The method considers the hourly ST prediction to be the superposition of two parts, namely, the daily average ST prediction and the ST amplitude (the difference between the hourly ST and the daily average ST) prediction. According to the results of correlation analysis, we selected nine meteorological parameters and combined two temporal parameters as the input vectors for predicting the daily average ST. For the task of predicting the ST amplitude, seven meteorological parameters and one temporal parameter were selected as the inputs. Two submodels were constructed using a deep bidirectional long short-term memory network (BiLSTM). For the task of hourly ST prediction at five different soil depths at 30 sites, which are located in 5 common climates in the United States, the results showed the method proposed in this paper performs best at all depths for 30 stations (100% of all) for the root mean square error (RMSE), 27 stations (90% of all) for the mean absolute error (MAE), and 30 stations (100% of all) for the coefficient of determination (R2), respectively. Moreover, the method adopted in this study displays a stronger ST prediction ability than the traditional methods under all climate types involved in the experiment, the hourly ST produced by it can be used as a driving parameter for high-resolution biogeochemical models, land surface models and hydrological models and can provide ideas for an analysis of other time series data.<\/jats:p>","DOI":"10.3390\/a13070173","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T06:08:17Z","timestamp":1595225297000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Cong","family":"Li","sequence":"first","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"College of Computer and Communication, LanZhou University of Technology, Lanzhou 730050, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8905-9006","authenticated-orcid":false,"given":"Yaonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xupeng","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer and Communication, LanZhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.agrformet.2018.07.011","article-title":"Grazing modulates soil temperature and moisture in a Eurasian steppe","volume":"262","author":"Yan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1139\/x93-313","article-title":"Predicting forest soil temperatures from monthly air temperature and precipitation records","volume":"23","author":"Yin","year":"1993","journal-title":"Can. J. For. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1111\/j.1365-2486.2004.00877.x","article-title":"Carbon limitation of soil respiration under winter snowpacks: Potential feedbacks between growing season and winter carbon fluxes","volume":"11","author":"Brooks","year":"2005","journal-title":"Glob. Chang. Boil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.soilbio.2003.09.008","article-title":"Increased snow depth affects microbial activity and nitrogen mineralization in two Arctic tundra communities","volume":"36","author":"Schimel","year":"2004","journal-title":"Soil Boil. Biochem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s00704-013-1065-z","article-title":"Modeling daily soil temperature using data-driven models and spatial distribution","volume":"118","author":"Kim","year":"2014","journal-title":"Theor. Appl. Clim."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1175\/1520-0450(2003)042<1139:ADSTDA>2.0.CO;2","article-title":"A Daily Soil Temperature Dataset and Soil Temperature Climatology of the Contiguous United States","volume":"42","author":"Hu","year":"2003","journal-title":"J. Appl. Meteorol."},{"key":"ref_7","first-page":"43","article-title":"Influence of upper layer properties on the ground temperature distribution","volume":"29","author":"Yilmaz","year":"2009","journal-title":"J. Therm. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1093\/treephys\/25.1.115","article-title":"Effects of soil temperature on shoot and root growth and nutrient uptake of 5-year-old Norway spruce seedlings","volume":"25","author":"Lahti","year":"2005","journal-title":"Tree Physiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.cj.2016.02.002","article-title":"A preliminary study of the effects of plastic film-mulched raised beds on soil temperature and crop performance of early-sown short-season spring maize (Zea mays L.) in the North China Plain","volume":"4","author":"Dang","year":"2016","journal-title":"Crop. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jvolgeores.2019.07.009","article-title":"Sealing capacity of clay-cap units above the Cerro Pabell\u00f3n hidden geothermal system (northern Chile) derived by soil CO2 flux and temperature measurements","volume":"384","author":"Taussi","year":"2019","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rser.2016.11.065","article-title":"Prediction of soil temperatures for shallow geothermal applications in Turkey","volume":"70","author":"Yener","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2019.11.123","article-title":"An experimental and numerical investigation on temperature profile of underground soil in the process of heat storage","volume":"148","author":"Zhang","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.energy.2019.02.160","article-title":"Parameter analysis of single U-tube GHE and dynamic simulation of underground temperature field round one year for GSHP","volume":"174","author":"Bi","year":"2019","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s40333-015-0058-4","article-title":"An analytical model for estimating soil temperature profiles on the Qinghai-Tibet Plateau of China","volume":"8","author":"Hu","year":"2015","journal-title":"J. Arid. Land"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.geoderma.2018.11.044","article-title":"Estimation of soil temperature from meteorological data using different machine learning models","volume":"338","author":"Feng","year":"2019","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.energy.2018.07.004","article-title":"Daily soil temperatures predictions for various climates in United States using data-driven model","volume":"160","author":"Xing","year":"2018","journal-title":"Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1002\/met.1489","article-title":"Short-term forecasting of soil temperature using artificial neural network: ANN-based soil temperature forecasting","volume":"22","author":"Tabari","year":"2015","journal-title":"Meteorol. Appl."},{"key":"ref_18","first-page":"59","article-title":"Prediction of soil temperature using regression and artificial neural network models","volume":"110","author":"Bilgili","year":"2010","journal-title":"Theor. Appl. Clim."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s00704-012-0807-7","article-title":"Spatiotemporal modeling of monthly soil temperature using artificial neural networks","volume":"113","author":"Wu","year":"2012","journal-title":"Theor. Appl. Clim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s12665-017-6607-8","article-title":"Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data","volume":"76","author":"Mehdizadeh","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s00704-014-1232-x","article-title":"Modeling soil temperatures at different depths by using three different neural computing techniques","volume":"121","author":"Kisi","year":"2014","journal-title":"Theor. Appl. Clim."},{"key":"ref_22","first-page":"445","article-title":"Research Progress of Biogeochemistry Model DNDC in Carbon Dynamic Modeling","volume":"25","author":"Zhang","year":"2017","journal-title":"Acta Agrestia Sinica"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9-1","DOI":"10.1029\/2001GB001838","article-title":"An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems","volume":"16","author":"Zhang","year":"2002","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ecolmodel.2017.07.013","article-title":"Evaluation of the DNDC model for simulating soil temperature, moisture and respiration from monoculture and rotational corn, soybean and winter wheat in Canada","volume":"360","author":"Li","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1007\/s00376-010-0131-z","article-title":"Coupling a terrestrial biogeochemical model to the common land model","volume":"28","author":"Shi","year":"2011","journal-title":"Adv. Atmospheric Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.jhydrol.2016.05.003","article-title":"A new soil-temperature module for SWAT application in regions with seasonal snow cover","volume":"538","author":"Qi","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"124390","DOI":"10.1016\/j.jhydrol.2019.124390","article-title":"Integrated land-surface hydrological and biogeochemical processes in simulating water, energy and carbon fluxes over two different ecosystems","volume":"582","author":"Zeng","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.egypro.2019.01.034","article-title":"Application of DBN for estimating daily solar radiation on horizontal surfaces in Lhasa, China","volume":"158","author":"Wang","year":"2019","journal-title":"Energy Procedia"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","article-title":"Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM","volume":"148","author":"Qing","year":"2018","journal-title":"Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.solener.2019.01.096","article-title":"3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction","volume":"181","author":"Zhao","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.renene.2019.05.039","article-title":"Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression","volume":"143","author":"Liu","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.enconman.2019.02.045","article-title":"Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks","volume":"186","author":"Li","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.enconman.2018.04.021","article-title":"Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network","volume":"166","author":"Liu","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1109\/TGRS.2018.2872131","article-title":"The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning","volume":"57","author":"Fang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World Map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorologische Zeitschrift"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"MissForest--non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_39","first-page":"1","article-title":"Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network","volume":"2019","author":"Zhao","year":"2019","journal-title":"J. Comput. Netw. Commun."},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.chemosphere.2018.12.128","article-title":"Long short-term memory\u2014Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction","volume":"220","author":"Zhao","year":"2019","journal-title":"Chemosphere"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neucom.2018.12.016","article-title":"Traffic flow prediction using LSTM with feature enhancement","volume":"332","author":"Yang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.procs.2018.03.076","article-title":"Prediction for Tourism Flow based on LSTM Neural Network","volume":"129","author":"Li","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.est.2018.12.011","article-title":"Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks","volume":"21","author":"Li","year":"2019","journal-title":"J. Energy Storage"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.renene.2018.10.031","article-title":"Fault diagnosis of wind turbine based on Long Short-term memory networks","volume":"133","author":"Lei","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.comcom.2020.04.004","article-title":"A deep neural networks based model for uninterrupted marine environment monitoring","volume":"157","author":"Reddy","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"104320","DOI":"10.1016\/j.cageo.2019.104320","article-title":"Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs. support vector regression models","volume":"133","author":"Achieng","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.neucom.2016.08.131","article-title":"Hierarchical multi-class classification in multimodal spacecraft data using DNN and weighted support vector machine","volume":"259","author":"Li","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.mfglet.2019.12.006","article-title":"Advanced cover glass defect detection and classification based on multi-DNN model","volume":"23","author":"Park","year":"2020","journal-title":"Manuf. Lett."},{"key":"ref_51","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume":"15","author":"Glorot","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1002\/widm.1125","article-title":"Support vector machines in engineering: An overview","volume":"4","year":"2014","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.compag.2016.03.025","article-title":"Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature","volume":"124","author":"Nahvi","year":"2016","journal-title":"Comput. Electron. Agric."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/7\/173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:34Z","timestamp":1760176174000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/7\/173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,17]]},"references-count":53,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["a13070173"],"URL":"https:\/\/doi.org\/10.3390\/a13070173","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,17]]}}}