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Preprints
https://doi.org/10.5194/hess-2020-184
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-184
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 19 May 2020

Submitted as: research article | 19 May 2020

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This preprint is currently under review for the journal HESS.

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Hylke E. Beck1,2, Ming Pan1,2, Diego G. Miralles3, Rolf H. Reichle4, Wouter A. Dorigo5, Sebastian Hahn5, Justin Sheffield6,2, Lanka Karthikeyan7, Gianpaolo Balsamo8, Robert M. Parinussa9, Albert I. J. M. van Dijk10, Jinyang Du11, John S. Kimball11, Noemi Vergopolan1, and Eric F. Wood1,2 Hylke E. Beck et al.
  • 1Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
  • 2Princeton Climate Analytics, Inc., Princeton, NJ, USA
  • 3Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
  • 4Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 5Department of Geodesy and Geoinformation (GEO), Vienna University of Technology, Vienna, Austria
  • 6School of Geography and Environmental Science, University of Southampton, Southampton, UK
  • 7Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400 076, India
  • 8European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
  • 9School of Geographic Sciences, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, People’s Republicof China
  • 10Fenner School of Environment and Society, Australian National University, Canberra, Australian Capital Territory, Australia
  • 11Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59801, USA

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as open-loop models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo and six estimates from the HBV model with three precipitation inputs (ERA5, IMERG, and MSWEP) and with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5-cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. The median R ± interquartile range across all sites and products in each category was 0.66 ± 0.30 for the satellite products, 0.69 ± 0.25 for the open-loop models, and 0.72 ± 0.22 for the models with satellite data assimilation. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E, SMOS, AMSR2, and ASCAT, with the L-band-based SMAPL3E (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI), MeMo performed better on average (median R of 0.72 versus 0.67), mainly due to the inclusion of SMAPL3E. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Hylke E. Beck et al.

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Hylke E. Beck et al.

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Short summary
We evaluated the temporal dynamics of 18 state-of-the-art global soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation, and six based on models that assimilate satellite data. We found pronounced differences in performance among the products, even among those derived from the same data source. Our results provide guidance to choose the most suitable soil moisture product for a particular application.
We evaluated the temporal dynamics of 18 state-of-the-art global soil moisture products,...
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