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      A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015

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          Abstract

          High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000–2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 in-situ soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m 3m −3 and correlation values above 0.5 over 70% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales.

          Abstract

          Measurement(s) wetness of soil
          Technology Type(s) digital curation • computational modeling technique
          Factor Type(s) geographic location • day
          Sample Characteristic - Environment soil
          Sample Characteristic - Location Europe

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11993547

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          Most cited references52

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          The Ensemble Kalman Filter: theoretical formulation and practical implementation

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            GLEAM v3: satellite-based land evaporation and root-zone soil moisture

            The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980–2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003–2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011–2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land–atmosphere feedbacks.
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              Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission

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                Author and article information

                Contributors
                b.naz@fz-juelich.de
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                3 April 2020
                3 April 2020
                2020
                : 7
                : 111
                Affiliations
                [1 ]Jülich Research Center GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG 3), Jülich, 52425 Germany
                [2 ]Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, 52425 Germany
                [3 ]ISNI 0000 0001 0940 3517, GRID grid.423977.c, Leibniz Supercomputing Centre, ; Boltzmannstr. 1, 85748 Garching, Germany
                Author information
                http://orcid.org/0000-0001-9888-1384
                http://orcid.org/0000-0002-8547-4146
                Article
                450
                10.1038/s41597-020-0450-6
                7125156
                32245972
                19ac39c5-2bbc-42a2-948a-2cb1847be6c5
                © The Author(s) 2020

                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.

                History
                : 14 October 2019
                : 12 March 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003163, Forschungszentrum Jülich (Jülich Research Centre);
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2020

                hydrology,scientific data
                hydrology, scientific data

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