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  "Title": "Merging of Satellite Datasets with Ground Observations using\nRandom Forests",
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  "Authors@R": "c(person(\"Mauricio Zambrano-Bigiarini\", email = \"mzb.devel@gmail.com\", role=c(\"aut\", \"cre\", \"cph\"), comment=c(ORCID = \"0000-0002-9536-643X\")), person(\"Oscar M. Baez-Villanueva\", email = \"oscar.baezvillanueva@ugent.be\", role=c(\"aut\", \"cph\"), comment=c(ORCID = \"0000-0002-2262-1698\")), person(\"Juan Giraldo-Osorio\", email = \"j.giraldoo@javeriana.edu.co\", role=c(\"ctb\"), comment=c(ORCID = \"0000-0001-6205-3341\")) )",
  "Maintainer": "Mauricio Zambrano-Bigiarini <mzb.devel@gmail.com>",
  "Description": "S3 implementation of the Random Forest MErging Procedure\n(RF-MEP), which combines two or more satellite-based datasets\n(e.g., precipitation products, topography) with ground\nobservations to produce a new dataset with improved\nspatio-temporal distribution of the target field. In\nparticular, this package was developed to merge different\nSatellite-based Rainfall Estimates (SREs) with measurements\nfrom rain gauges, in order to obtain a new precipitation\ndataset where the time series in the rain gauges are used to\ncorrect different types of errors present in the SREs. However,\nthis package might be used to merge other\nhydrological/environmental gridded datasets with point\nobservations. For details, see Baez-Villanueva et al. (2020)\n<doi:10.1016/j.rse.2019.111606>. Bugs / comments / questions /\ncollaboration of any kind are very welcomed.",
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