High-resolution European daily soil moisture derived with machine learning (2003–2020)

Sungmin O; Rene Orth; Ulrich Weber; Seon Ki Park

doi:10.1038/s41597-022-01785-6

Scientific Data (Nov 2022)

High-resolution European daily soil moisture derived with machine learning (2003–2020)

Sungmin O,
Rene Orth,
Ulrich Weber,
Seon Ki Park

Affiliations

Sungmin O: Department of Climate & Energy System Engineering, Ewha Womans University
Rene Orth: Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry
Ulrich Weber: Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry
Seon Ki Park: Department of Climate & Energy System Engineering, Ewha Womans University

DOI: https://doi.org/10.1038/s41597-022-01785-6
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 13

Abstract

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Measurement(s) wetness of soil Technology Type(s) machine learning Sample Characteristic - Environment wetness of soil Sample Characteristic - Location Europe

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

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