Regionalizing root-zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climateRegionalizing root-zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climate Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2022-11-292021enThis research is cofinanced by Greece and the European Union (European Social Fund [ESF]) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ) under the grand agreement no. 2019-050-0503-16972. IND contributed to this research in the context of “DRip Irrigation Precise—DR.I.P.: Development of an Advanced Precision Drip Irrigation System for Tree Crops” (Project Code: T1EDK-03372) which is cofinanced by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE. A.G.K. acknowledges partial support by the COST Action CA19139: PROCLIAS, supported by COST (European Cooperation in Science and Technology).The European Space Agency (ESA), through the Climate Change Initiative (CCI), is currently providing nearly 4 decades of global satellite-observed, fully homogenized soil moisture data for the uppermost 2–5 cm of the soil layer. These data are valuable as they comprise one of the most complete remotely sensed soil moisture data sets available in time and space. One main limitation of the ESA CCI soil moisture data set is the limited soil depth at which the moisture content is represented. In order to address this critical gap, we (a) estimate and calibrate the Soil Water Index using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then (b) leverage machine learning techniques and physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration. We use this calibration to assess the root-zone soil moisture for the period 2001–2018. The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land, and the Famine Early Warning Systems Network Land Data Assimilation System reanalyses soil moisture data sets, showing a good agreement, mainly over mid latitudes. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large-scale soil moisture-related studies.http://creativecommons.org/licenses/by/4.0/Water Resources Research575 Gryllakis Emmanouil Γρυλλακης Εμμανουηλ Koutroulis Aristeidis Κουτρουλης Αριστειδης Alexakis Dimitrios D. Polykretis Christos Daliakopoulos Ioannis Δαλιακοπουλος Ιωαννης American Geophysical Union Machine learning Calibration of the Soil Water Index Soil, climate, and vegetation descriptors ESA CCI Soil Water Index European Space Agency (ESA) Climate Change Initiative (CCI)