{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:10:07Z","timestamp":1787908207092,"version":"build-2784847793"},"reference-count":89,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2023ORP04"],"award-info":[{"award-number":["CBAS2023ORP04"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["22JR5RA070"],"award-info":[{"award-number":["22JR5RA070"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["22JR5RA060"],"award-info":[{"award-number":["22JR5RA060"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["21JR7RA068"],"award-info":[{"award-number":["21JR7RA068"]}]},{"name":"Gansu Provincial Natural Science Foundation, China","award":["CBAS2023ORP04"],"award-info":[{"award-number":["CBAS2023ORP04"]}]},{"name":"Gansu Provincial Natural Science Foundation, China","award":["22JR5RA070"],"award-info":[{"award-number":["22JR5RA070"]}]},{"name":"Gansu Provincial Natural Science Foundation, China","award":["22JR5RA060"],"award-info":[{"award-number":["22JR5RA060"]}]},{"name":"Gansu Provincial Natural Science Foundation, China","award":["21JR7RA068"],"award-info":[{"award-number":["21JR7RA068"]}]},{"name":"Basic Research Innovative Groups of Gansu province, China","award":["CBAS2023ORP04"],"award-info":[{"award-number":["CBAS2023ORP04"]}]},{"name":"Basic Research Innovative Groups of Gansu province, China","award":["22JR5RA070"],"award-info":[{"award-number":["22JR5RA070"]}]},{"name":"Basic Research Innovative Groups of Gansu province, China","award":["22JR5RA060"],"award-info":[{"award-number":["22JR5RA060"]}]},{"name":"Basic Research Innovative Groups of Gansu province, China","award":["21JR7RA068"],"award-info":[{"award-number":["21JR7RA068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3\/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3\/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET\u2019s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study\u2019s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.<\/jats:p>","DOI":"10.3390\/s24010035","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T11:24:33Z","timestamp":1703071473000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1654-4112","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinliang","family":"Hou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1366-5170","authenticated-orcid":false,"given":"Chunlin","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4908","DOI":"10.1038\/s41467-023-40641-y","article-title":"Soil moisture-atmosphere coupling accelerates global warming","volume":"14","author":"Qiao","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3545","DOI":"10.1038\/s41467-023-39318-3","article-title":"Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow","volume":"14","author":"Koster","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lu, H.S., Crow, W.T., Zhu, Y.H., Ouyang, F., and Su, J.B. (2016). Improving Streamflow Prediction Using Remotely-Sensed Soil Moisture and Snow Depth. Remote Sens., 8.","DOI":"10.3390\/rs8060503"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1109\/TGRS.2010.2089526","article-title":"An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval","volume":"49","author":"Das","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.06.010","article-title":"A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days","volume":"184","author":"Djamai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1002\/2015WR017782","article-title":"Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models","volume":"52","author":"Pau","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1109\/TGRS.2019.2947356","article-title":"Modeling of EM Wave Coherent Scattering From a Rough Multilayered Medium With the Scalar Kirchhoff Approximation for GPR Applications","volume":"58","author":"Pinel","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, H., and Xue, Z.H. (2020). Calibrated Integral Equation Model for Bare Soil Moisture Retrieval of Synthetic Aperture Radar: A Case Study in Linze County. Appl. Sci., 10.","DOI":"10.3390\/app10217921"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/LGRS.2017.2735421","article-title":"Microwave Thermal Emission Characteristics of a Two-Layer Medium With Rough Interfaces Using the Second-Order Small Perturbation Method","volume":"14","author":"Burkholder","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/17499518.2018.1440317","article-title":"Probabilistic calibration of a coupled hydro-mechanical slope stability model with integration of multiple observations","volume":"12","author":"Zhang","year":"2018","journal-title":"Georisk Assess. Manag. Risk Eng. Syst. Geohazards"},{"key":"ref_11","first-page":"559","article-title":"Soil moisture sensor for agricultural applications inspired from state of art study of surfaces scattering models & semi-empirical soil moisture models","volume":"20","author":"Shakya","year":"2021","journal-title":"J. Saudi Soc. Agric. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3481","DOI":"10.5194\/gmd-11-3481-2018","article-title":"The Variable Infiltration Capacity model version 5 (VIC-5): Infrastructure improvements for new applications and reproducibility","volume":"11","author":"Hamman","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.5194\/gmd-11-1343-2018","article-title":"LPJmL4-a dynamic global vegetation model with managed land\u2014Part 1: Model description","volume":"11","author":"Schaphoff","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.5194\/gmd-11-2429-2018","article-title":"PCR-GLOBWB 2: A 5 arcmin global hydrological and water resources model","volume":"11","author":"Sutanudjaja","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/j.1752-1688.1998.tb05961.x","article-title":"Large area hydrologic modeling and assessment\u2014Part 1: Model development","volume":"34","author":"Arnold","year":"1998","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"124367","DOI":"10.1016\/j.jhydrol.2019.124367","article-title":"Assimilation of Sentinel 1 and SMAP\u2014based satellite soil moisture retrievals into SWAT hydrological model: The impact of satellite revisit time and product spatial resolution on flood simulations in small basins","volume":"581","author":"Azimi","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.5194\/hess-23-1113-2019","article-title":"Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria","volume":"23","author":"Odusanya","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"128012","DOI":"10.1016\/j.jhydrol.2022.128012","article-title":"Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT","volume":"610","author":"Igder","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.13031\/2013.34903","article-title":"Swat Ungauged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin","volume":"53","author":"Srinivasan","year":"2010","journal-title":"Trans. Asabe"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.1002\/2017MS001144","article-title":"SWAT-Based Hydrological Data Assimilation System (SWAT-HDAS): Description and Case Application to River Basin-Scale Hydrological Predictions","volume":"9","author":"Zhang","year":"2017","journal-title":"J. Adv. Model Earth Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104082","DOI":"10.1016\/j.catena.2019.104082","article-title":"Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin","volume":"181","author":"Bhatta","year":"2019","journal-title":"Catena"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1007\/s11269-021-02867-7","article-title":"Quantifying Surface Water and Ground Water Interactions using a Coupled SWAT_FEM Model: Implications of Management Practices on Hydrological Processes in Irrigated River Basins","volume":"35","author":"Preetha","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.jhydrol.2007.12.025","article-title":"Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model","volume":"352","author":"Schuol","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.jhydrol.2006.09.014","article-title":"Modelling hydrology and water quality in the pre-alpine\/alpine Thur watershed using SWAT","volume":"333","author":"Abbaspour","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.jhydrol.2016.01.034","article-title":"Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review","volume":"535","author":"Francesconi","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.13031\/2013.23637","article-title":"The soil and water assessment tool: Historical development, applications, and future research directions","volume":"50","author":"Gassman","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_27","unstructured":"Abbaspour, K.C., Vejdani, M., and Haghighat, S. (2007). MODSIM 2007 International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, Swiss Federal Institute of Aquatic Science and Technology."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Balivada, S., Grant, G., Zhang, X., Ghosh, M., Guha, S., and Matamala, R. (2022). A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation. Sensors, 22.","DOI":"10.3390\/s22103913"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bertocco, M., Parrino, S., Peruzzi, G., and Pozzebon, A. (2023). Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning. Sensors, 23.","DOI":"10.3390\/s23042033"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bogena, H.R., Weuthen, A., and Huisman, J.A. (2022). Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management. Sensors, 22.","DOI":"10.3390\/s22249792"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, H., and Wang, J. (2023). Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network. Sensors, 23.","DOI":"10.3390\/s23052365"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Garcia, L., Parra, L., Jimenez, J.M., Parra, M., Lloret, J., Mauri, P.V., and Lorenz, P. (2021). Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas. Sensors, 21.","DOI":"10.3390\/s21051693"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liang, M.-C., Chen, H.-E., Tfwala, S.S., Lin, Y.-F., and Chen, S.-C. (2023). The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam. Sensors, 23.","DOI":"10.3390\/s23083795"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lloret, J., Sendra, S., Garcia, L., and Jimenez, J.M. (2021). A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture. Sens. Basel, 21.","DOI":"10.3390\/s21217243"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lopez, E., Vionnet, C., Ferrer-Cid, P., Barcelo-Ordinas, J.M., Garcia-Vidal, J., Contini, G., Prodolliet, J., and Maiztegui, J. (2022). A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields. Sensors, 22.","DOI":"10.3390\/s22186840"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lozoya, C., Favela-Contreras, A., Aguilar-Gonzalez, A., Felix-Herran, L.C., and Orona, L. (2021). Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control. Sensors, 21.","DOI":"10.3390\/s21165541"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Placidi, P., Morbidelli, R., Fortunati, D., Papini, N., Gobbi, F., and Scorzoni, A. (2021). Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors. Sensors, 21.","DOI":"10.3390\/s21155110"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rivera Guzman, E.F., Manay Chochos, E.D., Chiliquinga Malliquinga, M.D., Baldeon Egas, P.F., and Toasa Guachi, R.M. (2022). LoRa Network-Based System for Monitoring the Agricultural Sector in Andean Areas: Case Study Ecuador. Sensors, 22.","DOI":"10.3390\/s22186743"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Saeidi, T., Alhawari, A.R.H., Almawgani, A.H.M., Alsuwian, T., Imran, M.A., and Abbasi, Q. (2022). High Gain Compact UWB Antenna for Ground Penetrating Radar Detection and Soil Inspection. Sensors, 22.","DOI":"10.3390\/s22145183"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"138","DOI":"10.4236\/wsn.2012.45020","article-title":"An Eco-Hydrology Wireless Sensor Demonstration Network in High-Altitude and Alpine Environment in the Heihe River Basin of China","volume":"4","author":"Zhang","year":"2012","journal-title":"Wirel. Sens. Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1080\/13658816.2014.948446","article-title":"Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China","volume":"29","author":"Ge","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.jhydrol.2016.02.037","article-title":"Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed\/in-situ soil moisture","volume":"536","author":"Rajib","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"vzj2014-08","DOI":"10.2136\/vzj2014.08.0114","article-title":"Calibration and Evaluation of a Frequency Domain Reflectometry Sensor for Real-Time Soil Moisture Monitoring","volume":"14","author":"Ojo","year":"2015","journal-title":"Vadose Zone J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.5194\/gmd-9-1341-2016","article-title":"TerrSysMP-PDAF version 1.0): A modular high-performance data assimilation framework for an integrated land surface-subsurface model","volume":"9","author":"Kurtz","year":"2016","journal-title":"Geosci. Model Dev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1175\/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2","article-title":"Hydrologic data assimilation with the ensemble Kalman filter","volume":"130","author":"Reichle","year":"2002","journal-title":"Mon. Weather Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"715","DOI":"10.3402\/tellusa.v57i5.14732","article-title":"A comparison of error subspace Kalman filters","volume":"57","author":"Nerger","year":"2005","journal-title":"Tellus Ser. A Dyn. Meteorol. Oceanogr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8429","DOI":"10.1175\/JCLI-D-17-0093.1","article-title":"Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System","volume":"30","author":"Chen","year":"2017","journal-title":"J. Clim."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s13131-021-1768-4","article-title":"Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system","volume":"40","author":"Shu","year":"2021","journal-title":"Acta Oceanol. Sin."},{"key":"ref_50","first-page":"42","article-title":"The choice of the optimal parameters in a Local Error Subspace Transform Kalman Filter","volume":"37","author":"Wang","year":"2020","journal-title":"Mar. Forecast."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1175\/MWR-D-12-00203.1","article-title":"The impact of covariance localization for radar data on EnKF analyses of a developing MCS: Observing system simulation experiments","volume":"141","author":"Sobash","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1175\/WAF-D-19-0161.1","article-title":"Parameter Sensitivity of the WRF\u2013LETKF System for Assimilation of Radar Observations: Imperfect-Model Observing System Simulation Experiments","volume":"35","author":"Maldonado","year":"2020","journal-title":"Weather Forecast."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0924-2716(02)00124-7","article-title":"The shuttle radar topography mission\u2014A new class of digital elevation models acquired by spaceborne radar","volume":"57","author":"Rabus","year":"2003","journal-title":"Isprs J. Photogramm."},{"key":"ref_54","unstructured":"Nachtergaele, F.O., van Velthuizen, H., Verelst, L., Wiberg, D., Batjes, N.H., Dijkshoorn, J.A., van Engelen, V.W.P., Fischer, G., Jones, A., and Montanarella, L. (2012). Harmonized World Soil Database (Version 1.2), FAO."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/LGRS.2014.2319085","article-title":"A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China","volume":"11","author":"Jin","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","unstructured":"Jian, K., Xin, L., and Mingguo, M. (2015). HiWATER: WATERNET observation dataset in the upper reaches of the Heihe River Basin (2014). Heihe Plan Sci. Data Cent., 10."},{"key":"ref_58","first-page":"993","article-title":"Introduction of Eco-hydrological Wireless Sensor Network in the Heihe River Basin","volume":"27","author":"Jin","year":"2012","journal-title":"Adv. Earth Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.cageo.2012.03.026","article-title":"Software for ensemble-based data assimilation systems-Implementation strategies and scalability","volume":"55","author":"Nerger","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_60","unstructured":"Zhang, Y., Hou, J.L., Cao, Y.P., and Huang, C.L. (2016). Development and Evaluation of a HRU-based Gridded Approach in SWAT Model for Watershed-scale Hydrological Modelling. Environ. Model Softw., Submitted."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"9955","DOI":"10.1002\/2015JD023305","article-title":"Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data","volume":"120","author":"Hirschi","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1175\/MWR-D-14-00182.1","article-title":"On Serial Observation Processing in Localized Ensemble Kalman Filters","volume":"143","author":"Nerger","year":"2015","journal-title":"Mon. Weather Rev."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1002\/qj.945","article-title":"A regulated localization scheme for ensemble-based Kalman filters","volume":"138","author":"Nerger","year":"2012","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4341","DOI":"10.5194\/hess-20-4341-2016","article-title":"Multivariate hydrological data assimilation of soil moisture and groundwater head","volume":"20","author":"Zhang","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Han, X., Li, X., Rigon, R., Jin, R., and Endrizzi, S. (2015). Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0116435"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Li, Y., Cong, Z., and Yang, D. (2023). Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter. Remote Sens., 15.","DOI":"10.3390\/rs15071852"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"103813","DOI":"10.1016\/j.advwatres.2020.103813","article-title":"Improving parameter and state estimation of a hydrological model with the ensemble square root filter","volume":"147","author":"Li","year":"2021","journal-title":"Adv. Water Resour."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1002\/wrcr.20169","article-title":"Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags","volume":"49","author":"Li","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"D09110","DOI":"10.1029\/2010JD014673","article-title":"\u201cVariable localization\u201d in an ensemble Kalman filter: Application to the carbon cycle data assimilation","volume":"116","author":"Kang","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e2021GL094941","DOI":"10.1029\/2021GL094941","article-title":"Strongly Coupled Data Assimilation of Ocean Observations Into an Ocean-Atmosphere Model","volume":"48","author":"Tang","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e2019MS001938","DOI":"10.1029\/2019MS001938","article-title":"Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations","volume":"12","author":"Yang","year":"2020","journal-title":"J. Adv. Model Earth Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.5194\/hess-19-2999-2015","article-title":"Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: Evaluating the effect of ensemble size and localization on filter performance","volume":"19","author":"Rasmussen","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.advwatres.2013.07.011","article-title":"Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering","volume":"60","author":"Gharamti","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5519","DOI":"10.1007\/s00500-018-3210-1","article-title":"A Tabu Search implementation for adaptive localization in ensemble-based methods","volume":"23","year":"2019","journal-title":"Soft Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.3233\/IDA-205471","article-title":"Differential evolution algorithm-based multiple-factor optimization methods for data assimilation","volume":"25","author":"Bai","year":"2021","journal-title":"Intell. Data Anal."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.agrformet.2016.03.013","article-title":"Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields","volume":"230","author":"Huang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"112222","DOI":"10.1016\/j.rse.2020.112222","article-title":"Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter","volume":"253","author":"Seo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1175\/BAMS-D-15-00200.1","article-title":"Future Observing System Simulation Experiments","volume":"97","author":"Hoffman","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"3983","DOI":"10.5194\/hess-22-3983-2018","article-title":"Technical note: Assessment of observation quality for data assimilation in flood models","volume":"22","author":"Waller","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4921","DOI":"10.5194\/hess-22-4921-2018","article-title":"Inflation method for ensemble Kalman filter in soil hydrology","volume":"22","author":"Bauser","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3638","DOI":"10.1002\/qj.3864","article-title":"Analysis and design of covariance inflation methods using inflation functions. Part 1: Theoretical framework","volume":"146","author":"Duc","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1327766","DOI":"10.1080\/16000870.2017.1327766","article-title":"The smoother extension of the nonlinear ensemble transform filter","volume":"69","author":"Kirchgessner","year":"2017","journal-title":"Tellus Ser. A Dyn. Meteorol. Oceanogr."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chen, W., Shen, H., Huang, C., and Li, X. (2017). Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST. Remote Sens., 9.","DOI":"10.3390\/rs9030273"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.cageo.2012.03.011","article-title":"Ensemble smoother with multiple data assimilation","volume":"55","author":"Emerick","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1012165","DOI":"10.3389\/feart.2022.1012165","article-title":"A hybrid data assimilation system based on machine learning","volume":"10","author":"Dong","year":"2023","journal-title":"Front. Earth Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.5194\/hess-27-1583-2023","article-title":"Improving regional climate simulations based on a hybrid data assimilation and machine learning method","volume":"27","author":"He","year":"2023","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4280","DOI":"10.1002\/2015WR018425","article-title":"Determining soil moisture and soil properties in vegetated areas by assimilating soil temperatures","volume":"52","author":"Dong","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10712-015-9343-1","article-title":"Modelling Freshwater Resources at the Global Scale: Challenges and Prospects","volume":"37","author":"Doll","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_89","first-page":"2751","article-title":"Retrieving Accurate Soil Moisture over the Tibetan Plateau Using Multisource Remote Sensing Data Assimilation with Simultaneous State and Parameter Estimations","volume":"22","author":"Chen","year":"2021","journal-title":"J. Hydrometeorol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:42:18Z","timestamp":1760132538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,20]]},"references-count":89,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010035"],"URL":"https:\/\/doi.org\/10.3390\/s24010035","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,20]]}}}