{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:36:08Z","timestamp":1773347768926,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901371"],"award-info":[{"award-number":["41901371"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science and Technology Planning Project of Guangdong Province","award":["2018B020207012"],"award-info":[{"award-number":["2018B020207012"]}]},{"name":"the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["GML2019ZD0301"],"award-info":[{"award-number":["GML2019ZD0301"]}]},{"name":"the Guangdong Innovative and Entrepreneurial Research Team Program","award":["2016ZT06D336"],"award-info":[{"award-number":["2016ZT06D336"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast.<\/jats:p>","DOI":"10.3390\/rs14071750","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T05:11:34Z","timestamp":1649221894000},"page":"1750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Multi-Source Precipitation Data Merging for Heavy Rainfall Events Based on Cokriging and Machine Learning Methods"],"prefix":"10.3390","volume":"14","author":[{"given":"Junmin","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6330-7948","authenticated-orcid":false,"given":"Jianhui","family":"Xu","sequence":"additional","affiliation":[{"name":"Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1342-6417","authenticated-orcid":false,"given":"Xiaoai","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Huihua","family":"Ruan","sequence":"additional","affiliation":[{"name":"Guangdong Meteorological Observation Data Center, Guangzhou 510080, China"}]},{"given":"Xulong","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-3943","authenticated-orcid":false,"given":"Wenlong","family":"Jing","sequence":"additional","affiliation":[{"name":"Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1175\/2008JHM1052.1","article-title":"An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data","volume":"10","author":"Sapiano","year":"2009","journal-title":"J. Hydrometeorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/nature11377","article-title":"Afternoon rain more likely over drier soils","volume":"489","author":"Taylor","year":"2012","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.jhydrol.2019.04.061","article-title":"Short time precipitation estimation using weather radar and surface observations: With rainfall displacement information integrated in a stochastic manner","volume":"574","author":"Jieru","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.atmosres.2011.10.021","article-title":"Global precipitation measurement: Methods, datasets and applications","volume":"104","author":"Tapiador","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-14-00283.1","article-title":"So, how much of the Earth\u2019s surface is covered by rain gauges?","volume":"98","author":"Kidd","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1175\/JHM-D-14-0106.1","article-title":"Precipitation seasonality over the Indian subcontinent: An evaluation of gauge, reanalyses, and satellite retrievals","volume":"16","author":"Rana","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1175\/1520-0442(1996)009<0840:AOGMPU>2.0.CO;2","article-title":"Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions","volume":"9","author":"Xie","year":"1996","journal-title":"J. Clim."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3763","DOI":"10.1080\/01431161.2010.483489","article-title":"Evaluation of a satellite-based global flood monitoring system","volume":"31","author":"Yilmaz","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1175\/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2","article-title":"The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982-84","volume":"115","author":"Arkin","year":"1987","journal-title":"Mon. Weather. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1175\/1520-0426(1992)009<0129:DOMRFT>2.0.CO;2","article-title":"Determination of mean rainfall from the Special Sensor Microwave\/Imager (SSM\/I) using a mixed lognormal distribution","volume":"9","author":"Berg","year":"1992","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1175\/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2","article-title":"Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs","volume":"78","author":"Xie","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1175\/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2","article-title":"The global precipitation climatology project (GPCP) combined precipitation dataset","volume":"78","author":"Huffman","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"124378","DOI":"10.1016\/j.jhydrol.2019.124378","article-title":"An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China","volume":"581","author":"Ziqiang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_14","first-page":"1121","article-title":"Evaluation of the GSMaP Estimates on Monitoring Extreme Precipitation Events. Remote sensing Technology and Application","volume":"34","author":"Gao","year":"2019","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.atmosres.2009.08.017","article-title":"Precipitation: Measurement, remote sensing, climatology and modeling","volume":"94","author":"Michaelides","year":"2009","journal-title":"Atmos. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/BAMS-D-14-00174.1","article-title":"Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities","volume":"97","author":"Zhang","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3063","DOI":"10.1002\/2013JD020686","article-title":"A high spatiotemporal gauge-satellite merged precipitation analysis over China","volume":"119","author":"Shen","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-018-3860-4","article-title":"Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia","volume":"11","author":"Alharbi","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, G., Wang, Z., and Xia, T. (2019). Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region. Applied Sciences., 9.","DOI":"10.3390\/app9112294"},{"key":"ref_20","first-page":"219","article-title":"Parameter Improvements of Hourly Automatic Weather Stations Precipitation Analysis by Optimal Interpolation over China","volume":"27","author":"Shen","year":"2012","journal-title":"J. Chengdu Univ. Technol."},{"key":"ref_21","first-page":"1232","article-title":"Multi-source Precipitation Data Fusion Method Based on Filtersim","volume":"31","author":"Kunwei","year":"2019","journal-title":"J. Syst. Simul."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124664","DOI":"10.1016\/j.jhydrol.2020.124664","article-title":"A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China","volume":"584","author":"Wu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"125156","DOI":"10.1016\/j.jhydrol.2020.125156","article-title":"Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method","volume":"589","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.advwatres.2014.06.005","article-title":"Geostatistical radar\u2013raingauge merging: A novel method for the quantification of rain estimation accuracy","volume":"71","author":"Delrieu","year":"2014","journal-title":"Adv. Water Resour."},{"key":"ref_25","unstructured":"Sideris, I.V., Gabella, M., Sassi, M., and Germann, U. (2012, January 6\u20139). Real-Time Spatiotemporal Merging of Radar and Raingauge Precipitation Measurements in Switzerland. Proceedings of the 9th International Workshop on Precipitation in Urban Areas, St. Moritz, Switzerland."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/BF01543427","article-title":"Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data","volume":"3","author":"Krajewski","year":"1989","journal-title":"Stoch. Hydrol. Hydraul."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126612","DOI":"10.1016\/j.jhydrol.2021.126612","article-title":"Merging radar and rain gauge data by using spatial\u2013temporal local weighted linear regression kriging for quantitative precipitation estimation","volume":"601","author":"Zhang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TGRS.2019.2942280","article-title":"A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_29","unstructured":"S\u00f8nderby, C.K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., and Kalchbrenner, N. (2020). Metnet: A neural weather model for precipitation forecasting. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.jhydrol.2018.12.039","article-title":"A Monte Carlo-based multi-objective optimization approach to merge different precipitation estimates for land surface modeling","volume":"570","author":"Hazra","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_31","first-page":"177","article-title":"An experiment of high-resolution gauge-radar-satellite combined precipitation retrieval based on the Bayesian merging method","volume":"73","author":"Pang","year":"2015","journal-title":"Acta Meteorol. Sin."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wehbe, Y., Temimi, M., and Adler, R.F. (2020). Enhancing precipitation estimates through the fusion of weather radar, satellite retrievals, and surface parameters. Remote Sens.-Basel, 12.","DOI":"10.3390\/rs12081342"},{"key":"ref_33","first-page":"652","article-title":"Duration and seasonality of the hourly extreme rainfall in the central-eastern part of China","volume":"71","author":"Li","year":"2013","journal-title":"Acta Meteorol. Sin."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1175\/BAMS-84-9-1205","article-title":"The changing character of precipitation","volume":"84","author":"Trenberth","year":"2003","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_35","first-page":"8","article-title":"Climatic characteristics and forecast focus of heavy rain in Qingyuan","volume":"2","author":"Li","year":"1999","journal-title":"Guangdong Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1146\/annurev.earth.33.092203.122541","article-title":"Orographic precipitation","volume":"33","author":"Roe","year":"2005","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"ref_37","first-page":"26","article-title":"NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG)","volume":"4","author":"Huffman","year":"2015","journal-title":"Algorithm Theor. Basis Doc. ATBD Version"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3084","DOI":"10.1109\/TGRS.2009.2019954","article-title":"The GSMaP precipitation retrieval algorithm for microwave sounders\u2014Part I: Over-ocean algorithm","volume":"47","author":"Shige","year":"2009","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The global precipitation measurement mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"137","DOI":"10.2151\/jmsj.87A.137","article-title":"A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data","volume":"87","author":"Ushio","year":"2009","journal-title":"J. Meteorol. Soc. Jpn. Ser. II."},{"key":"ref_41","first-page":"259","article-title":"A geostatistical framework for area-to-point spatial interpolation","volume":"36","author":"Kyriakidis","year":"2004","journal-title":"Geogr. Anal."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_43","first-page":"3146","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_45","unstructured":"Zhang, R. (2005). Spatial Variation Theory and Applications, Science Press."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s11069-021-04585-0","article-title":"Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China","volume":"107","author":"Huang","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jhydrol.2018.01.042","article-title":"Geographically weighted regression based methods for merging satellite and gauge precipitation","volume":"558","author":"Chao","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_48","first-page":"276","article-title":"Analysis of fusion test results on hourly precipitation from meteorological and hydrological stations and radar","volume":"39","author":"Li","year":"2020","journal-title":"Torrential Rain Disasters"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1750\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:52Z","timestamp":1760136532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,6]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071750"],"URL":"https:\/\/doi.org\/10.3390\/rs14071750","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,6]]}}}