{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T22:50:40Z","timestamp":1774565440816,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan","award":["2017YFC1502901"],"award-info":[{"award-number":["2017YFC1502901"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771538"],"award-info":[{"award-number":["41771538"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution and continuous hydrological products have tremendous importance for the prediction of water-related trends and enhancing the capability for sustainable water resources management under climate change and human impacts. In this study, we used the random forest (RF) and extreme gradient boosting (XGBoost) methods to downscale groundwater storage (GWS) from 1\u00b0 (~110 km) to 1 km by downscaling Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data from 1\u00b0 (~110 km) and 0.25\u00b0 (~25 km) respectively, to 1 km for China. Three evaluation metrics were employed for the testing dataset for 2004\u22122016: The R2 ranged from 0.77\u22120.89 for XGBoost (0.74\u22120.86 for RF), the correlation coefficient (CC) ranged from 0.88\u22120.94 for XGBoost (0.88\u22120.93 for RF) and the root-mean-square error (RMSE) ranged from 0.37\u22122.3 for XGBoost (0.4\u22122.53 for RF). The R2 of the XGBoost models for GLDAS was 0.64\u22120.82 (0.63\u22120.82 for RF), the CC was 0.80\u22120.91 (0.80\u22120.90 for RF) and the RMSE was 0.63\u22121.75 (0.63\u22121.77 for RF). The downscaled GWS derived from GRACE and GLDAS were validated using in situ measurements by comparing the time series variations and the downscaled products maintained the accuracy of the original data. The interannual changes within 9 river basins between pre- and post-downscaling were consistent, emphasizing the reliability of the downscaled products. Ultimately, annual downscaled TWS, GLDAS and GWS products were provided from 2004 to 2016, providing a solid data foundation for studying local GWS changes, conducting finer-scale hydrological studies and adapting water resources management and policy formulation to local condition.<\/jats:p>","DOI":"10.3390\/rs13030523","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T13:01:12Z","timestamp":1612270872000},"page":"523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Downscaling Groundwater Storage Data in China to a 1-km Resolution Using Machine Learning Methods"],"prefix":"10.3390","volume":"13","author":[{"given":"Jianxin","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-7824","authenticated-orcid":false,"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Beijing 100875, China"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1002\/2014WR016869","article-title":"Water management: Current and future challenges and research directions","volume":"51","author":"Cosgrove","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"823","DOI":"10.5194\/hess-19-823-2015","article-title":"A high-resolution global-scale groundwater model","volume":"19","author":"Sutanudjaja","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5194\/esd-5-15-2014","article-title":"Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources","volume":"5","author":"Wada","year":"2014","journal-title":"Earth Syst. Dyn. Discuss."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"E1080","DOI":"10.1073\/pnas.1704665115","article-title":"Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data","volume":"115","author":"Scanlon","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s10712-015-9332-4","article-title":"Groundwater storage changes: Present status from GRACE observations","volume":"37","author":"Chen","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, J., Liu, K., and Wang, M. (2020). Seasonal and Interannual Variations in China\u2019s Groundwater Based on GRACE Data and Multisource Hydrological Models. Remote Sens., 12.","DOI":"10.3390\/rs12050845"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/ngeo1617","article-title":"Regional strategies for the accelerating global problem of groundwater depletion","volume":"5","author":"Gleeson","year":"2012","journal-title":"Nat. Geosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7564","DOI":"10.1029\/2018WR024618","article-title":"Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges","volume":"55","author":"Li","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"24398","DOI":"10.1038\/srep24398","article-title":"Have GRACE satellites overestimated groundwater depletion in the Northwest India Aquifer?","volume":"6","author":"Long","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1029\/2011WR011312","article-title":"Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA","volume":"48","author":"Scanlon","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The global land data assimilation system","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1029\/2005GL022964","article-title":"Low degree spherical harmonic influences on Gravity Recovery and Climate Experiment (GRACE) water storage estimates","volume":"32","author":"Chen","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sahour, H., Sultan, M., Vazifedan, M., Abdelmohsen, K., Karki, S., Yellich, J.A., Gebremichael, E., Alshehri, F., and Elbayoumi, T.M. (2020). Statistical applications to downscale GRACE-derived terrestrial water storage data and to fill temporal gaps. Remote Sens., 12.","DOI":"10.3390\/rs12030533"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Du, Z., Ge, L., Ng, A.H.-M., and Li, X. (2016, January 10\u201315). Time series interferometry integrated with groundwater depletion measurement from GRACE. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729295"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1007\/s10040-018-1768-4","article-title":"Long-term groundwater storage changes and land subsidence development in the North China Plain (1971\u20132015)","volume":"26","author":"Gong","year":"2018","journal-title":"Hydrogeol. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1080\/15481603.2016.1227297","article-title":"Land subsidence under different land use in the eastern Beijing plain, China 2005\u20132013 revealed by InSAR timeseries analysis","volume":"53","author":"Zhou","year":"2016","journal-title":"Gisci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100661","DOI":"10.1016\/j.ejrh.2019.100661","article-title":"Surface deformation observed by InSAR shows connections with water storage change in Southern Ontario","volume":"27","author":"Li","year":"2020","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5985","DOI":"10.1002\/2015WR018211","article-title":"Groundwater depletion in Central Mexico: Use of GRACE and InSAR to support water resources management","volume":"52","author":"Castellazzi","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zheng, M., Deng, K., Fan, H., and Du, S. (2018). Monitoring and analysis of surface deformation in mining area based on InSAR and GRACE. Remote Sens., 10.","DOI":"10.3390\/rs10091392"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, J., Zhou, L., Yao, C., and Hu, J. (2016). Surface subsidence analysis by multi-temporal insar and grace: A case study in Beijing. Sensors, 16.","DOI":"10.3390\/s16091495"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2017.11.025","article-title":"Quantitative mapping of groundwater depletion at the water management scale using a combined GRACE\/InSAR approach","volume":"205","author":"Castellazzi","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1175\/2007JHM951.1","article-title":"Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River basin","volume":"9","author":"Zaitchik","year":"2008","journal-title":"J. Hydrometeorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.advwatres.2012.08.007","article-title":"Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA","volume":"52","author":"Sahoo","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7622","DOI":"10.1029\/2018WR024670","article-title":"On the use of adaptive ensemble Kalman filtering to mitigate error misspecifications in GRACE data assimilation","volume":"55","author":"Shokri","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8931","DOI":"10.1029\/2018WR022785","article-title":"Performance of different ensemble Kalman filter structures to assimilate GRACE terrestrial water storage estimates into a high-resolution hydrological model: A synthetic study","volume":"54","author":"Shokri","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1111\/gec3.12036","article-title":"Statistical downscaling in climatology","volume":"7","author":"Schoof","year":"2013","journal-title":"Geogr. Compass"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2020WR028059","DOI":"10.1029\/2020WR028059","article-title":"Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning","volume":"56","author":"Majumdar","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, A., Scanlon, B., Save, H., and Rateb, A. (2020). Reconstruction of GRACE Total Water Storage through Automated Machine Learning. Water Resour. Res.","DOI":"10.5194\/gstm2020-53"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1029\/2018WR023044","article-title":"Water resources assessment of China\u2019s transboundary river basins using a machine learning approach","volume":"55","author":"Yan","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Michael, W.J., Minsker, B.S., Tcheng, D., Valocchi, A.J., and Quinn, J.J. (2005). Integrating data sources to improve hydraulic head predictions: A hierarchical machine learning approach. Water Resour. Res., 41.","DOI":"10.1029\/2003WR002802"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8028","DOI":"10.1029\/2019WR024892","article-title":"Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine","volume":"55","author":"Cho","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Seyoum, W.M., Kwon, D., and Milewski, A.M. (2019). Downscaling GRACE TWSA data into high-resolution groundwater level anomaly using machine learning-based models in a glacial aquifer system. Remote Sens., 11.","DOI":"10.3390\/rs11070824"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Milewski, A.M., Thomas, M.B., Seyoum, W.M., and Rasmussen, T.C. (2019). Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA. Remote Sens., 11.","DOI":"10.3390\/rs11232756"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, L., He, Q., Liu, K., Li, J., and Jing, C. (2019). Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model. Remote Sens., 11.","DOI":"10.3390\/rs11242979"},{"key":"ref_35","first-page":"I_133","article-title":"Statistical downscaling of GRACE-derived terrestrial water storage using satellite and GLDAS products","volume":"70","author":"Ning","year":"2014","journal-title":"Proc. Civ. Eng. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5973","DOI":"10.1029\/2017JD027468","article-title":"Statistical Downscaling of GRACE-Derived Groundwater Storage Using ET Data in the North China Plain","volume":"123","author":"Yin","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rahaman, M.M., Thakur, B., Kalra, A., Li, R., and Maheshwari, P. (2019). Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach. Environments, 6.","DOI":"10.3390\/environments6060063"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Miro, M.E., and Famiglietti, J.S. (2018). Downscaling GRACE remote sensing datasets to high-resolution groundwater storage change maps of California\u2019s Central Valley. Remote Sens., 10.","DOI":"10.3390\/rs10010143"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6485","DOI":"10.1002\/2015WR017311","article-title":"Water balance-based actual evapotranspiration reconstruction from ground and satellite observations over the conterminous United States","volume":"51","author":"Wan","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1111\/j.1936-704X.2019.03324.x","article-title":"Investigating Relationship Between Soil Moisture and Precipitation Globally Using Remote Sensing Observations","volume":"168","author":"Sehler","year":"2019","journal-title":"J. Contemp. Water Res. Educ."},{"key":"ref_41","unstructured":"Engineers, S.C., Borchers, J.W., Carpenter, M., Grabert, V.K., Dalgish, B., and Cannon, D. (2014). Land Subsidence from Groundwater Use in California, California Water Foundation."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qiu, L., Huang, J., and Niu, W. (2018). Decoupling and Driving Factors of Economic Growth and Groundwater Consumption in the Coastal Areas of the Yellow Sea and the Bohai Sea. Sustainability, 10.","DOI":"10.3390\/su10114158"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"S265","DOI":"10.1016\/j.landusepol.2009.09.005","article-title":"The relationship between land use and groundwater resources and quality","volume":"26","author":"Lerner","year":"2009","journal-title":"Land Use Policy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.habitatint.2015.10.003","article-title":"Strategic assessment of groundwater resource exploitation using DPSIR framework in Guwahati city, India","volume":"51","author":"Hazarika","year":"2016","journal-title":"Habitat Int."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.tourman.2005.02.007","article-title":"Developing regional tourism in China: The potential for activating business clusters in a socialist market economy","volume":"27","author":"Jackson","year":"2006","journal-title":"Tour. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.jafrearsci.2015.07.008","article-title":"Identification of potential artificial groundwater recharge zones in Northwestern Saudi Arabia using GIS and Boolean logic","volume":"111","author":"Zaidi","year":"2015","journal-title":"J. Afr. Earth Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1080\/02626667.2018.1481962","article-title":"Artificial recharge efficiency assessment by soil water balance and modelling approaches in a multi-layered vadose zone in a dry region","volume":"63","author":"Pakparvar","year":"2018","journal-title":"Hydrol. Sci. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"121","DOI":"10.2478\/jwld-2018-0031","article-title":"Using DEM and GIS for evaluation of groundwater resources in relation to landforms in the Maharlou-Bakhtegan watershed, Fars province, Iran","volume":"37","author":"Mokarram","year":"2018","journal-title":"J. Water Land Dev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"570","DOI":"10.2307\/1939317","article-title":"How tree cover influences the water balance of Mediterranean rangelands","volume":"74","author":"Joffre","year":"1993","journal-title":"Ecology"},{"key":"ref_50","first-page":"54","article-title":"Method of pixelizing GDP data based on the GIS","volume":"18","author":"Yi","year":"2006","journal-title":"J. Gansu Sci"},{"key":"ref_51","first-page":"26","article-title":"Spatialization approach to 1 km grid GDP supported by remote sensing","volume":"2","author":"Liu","year":"2005","journal-title":"Geo-Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gandhi, S.M., and Sarkar, B.C. (2016). Chapter 3\u2014Reconnaissance and Prospecting. Essentials of Mineral Exploration and Evaluation, Elsevier.","DOI":"10.1016\/B978-0-12-805329-4.00010-7"},{"key":"ref_53","unstructured":"Didan, K. (2015). MOD13A2 MODIS\/Terra Vegetation indices 16-Day L3 Global 1km SIN Grid V006 [Data set]. NASA Eosdis Lp DAAC, 6."},{"key":"ref_54","unstructured":"Wan, Z., Hook, S., and Hulley, G. (2015). MOD11A1 MODIS\/Terra Land Surface Temperature\/Emissivity Daily L3 Global 1 km SIN Grid V006 [Data Set], NASA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2013.786146","article-title":"Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error","volume":"6","author":"Sexton","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.5194\/essd-11-1931-2019","article-title":"1 km monthly temperature and precipitation dataset for China from 1901 to 2017","volume":"11","author":"Peng","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1029\/2011WR011453","article-title":"Accuracy of scaled GRACE terrestrial water storage estimates","volume":"48","author":"Landerer","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"7547","DOI":"10.1002\/2016JB013007","article-title":"High-resolution CSR GRACE RL05 mascons","volume":"121","author":"Save","year":"2016","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_59","unstructured":"China Institute of Geological Environment Monitoring (CIGEM) (2013). China Geological Environment Monitoring: Groundwater Yearbook, China Land Press."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.advwatres.2013.08.011","article-title":"Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area","volume":"61","author":"Fang","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ecss.2018.05.031","article-title":"Spatial downscaling of MODIS Chlorophyll-a using Landsat 8 images for complex coastal water monitoring","volume":"209","author":"Fu","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_62","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_63","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jfoodeng.2014.01.007","article-title":"Modelling the relationship between peel colour and the quality of fresh mango fruit using Random Forests","volume":"131","author":"Fukuda","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of biomass in wheat using random forest regression algorithm and remote sensing data","volume":"4","author":"Zhou","year":"2016","journal-title":"Crop J."},{"key":"ref_67","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. In Proceedings of 22nd ACM SIGKDD International Conference on knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.tra.2019.08.009","article-title":"Cycling comfort evaluation with instrumented probe bicycle","volume":"129","author":"Zhu","year":"2019","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_70","first-page":"1","article-title":"Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data","volume":"2020","author":"Chen","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"111059","DOI":"10.1016\/j.ecoenv.2020.111059","article-title":"Manganese (Mn) removal prediction using extreme gradient model","volume":"204","author":"Bhagat","year":"2020","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_72","unstructured":"Garavaglia, S., and Sharma, A. (1998, January 4\u20136). A smart guide to dummy variables: Four applications and a macro. Proceedings of the Northeast SAS Users Group Conference, Pittsburgh, Pennsylvania."},{"key":"ref_73","unstructured":"Chen, T., He, T., Benesty, M., and Khotilovich, V. (2021, January 18). Package \u2018xgboost\u2019. R version 2020, Volume 90 . Available online: https:\/\/cran.r-project.org\/web\/packages\/xgboost\/index.html."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1002\/wrcr.20078","article-title":"Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region","volume":"49","author":"Voss","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"7490","DOI":"10.1002\/2016WR019344","article-title":"Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution","volume":"52","author":"Wiese","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/34.400568","article-title":"Mean shift, mode seeking, and clustering","volume":"17","author":"Cheng","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1175\/JHM-D-18-0253.1","article-title":"Differences in response of terrestrial water storage components to precipitation over 168 global river basins","volume":"20","author":"Zhang","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_78","unstructured":"Brydsten, L. (2006). Modelling Groundwater Discharge Areas Using Only Digital Elevation Models as Input Data, Swedish Nuclear Fuel and Waste Management, Co."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.compenvurbsys.2009.11.002","article-title":"Effects of DEM sources on hydrologic applications","volume":"34","author":"Li","year":"2010","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s11273-017-9535-1","article-title":"Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems","volume":"25","author":"Kiczko","year":"2017","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1029\/2009WR007733","article-title":"On the role of groundwater and soil texture in the regional water balance: An investigation of the Nebraska Sand Hills, USA","volume":"45","author":"Wang","year":"2009","journal-title":"Water Resour. Res."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Theil, H. (1992). A rank-invariant method of linear and polynomial regression analysis. Henri Theil\u2019s Contributions to Economics and Econometrics, Springer.","DOI":"10.1007\/978-94-011-2546-8_20"},{"key":"ref_83","unstructured":"Hoaglin, D.C., Mosteller, F., and Tukey, J.W. (1983). Understanding Robust and Exploratory Data Analysis, Wiley."},{"key":"ref_84","unstructured":"College of Urban and Environmental Science, P.U. (2019). Geographic Data Sharing Infrastructure, Panjab University."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/523\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:18:46Z","timestamp":1760159926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":84,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030523"],"URL":"https:\/\/doi.org\/10.3390\/rs13030523","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,2]]}}}