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      Global soil moisture data derived through machine learning trained with in-situ measurements

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      Scientific Data

      Nature Publishing Group UK

      Hydrology, Hydrology

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          Abstract

          While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

          Abstract

          Measurement(s) wetness of soil
          Technology Type(s) machine learning
          Factor Type(s) soil layer • temporal interval • geographic location
          Sample Characteristic - Environment soil

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14790510

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          Long Short-Term Memory

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            FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities

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              The ERA5 Global Reanalysis

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                Author and article information

                Contributors
                sungmino@bgc-jena.mpg.de
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                12 July 2021
                12 July 2021
                2021
                : 8
                Affiliations
                GRID grid.419500.9, ISNI 0000 0004 0491 7318, Max Planck Institute for Biogeochemistry, ; Jena, D-07745 Germany
                Article
                964
                10.1038/s41597-021-00964-1
                8275613
                34253737
                ace98ce0-8c43-41f1-89b3-7397d48b4f98
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: Emmy Noether grant 391059971
                Award ID: Emmy Noether grant 391059971
                Award Recipient :
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                © The Author(s) 2021

                hydrology

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