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

• Main Authors: H. E. Beck, M. Pan, D. G. Miralles, R. H. Reichle, W. A. Dorigo, S. Hahn, J. Sheffield, L. Karthikeyan, G. Balsamo, R. M. Parinussa, A. I. J. M. van Dijk, J. Du, J. S. Kimball, N. Vergopolan, E. F. Wood
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-01-01
• Series: Hydrology and Earth System Sciences
• Online Access: https://hess.copernicus.org/articles/25/17/2021/hess-25-17-2021.pdf
• ISSN: 1027-5606; 1607-7938

<p>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 (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) 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 (<span class="inline-formula"><i>R</i></span>) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E<span class="inline-formula">_SWI</span>, SMOS<span class="inline-formula">_SWI</span>, AMSR2<span class="inline-formula">_SWI</span>, and ASCAT<span class="inline-formula">_SWI</span>, with the L-band-based SMAPL3E<span class="inline-formula">_SWI</span> (median <span class="inline-formula"><i>R</i></span> of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI<span class="inline-formula">_SWI</span>), MeMo performed better on average (median <span class="inline-formula"><i>R</i></span> of 0.72 versus 0.67), probably due to the inclusion of SMAPL3E<span class="inline-formula">_SWI</span>. 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 <span class="inline-formula"><i>R</i></span> of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median <span class="inline-formula"><i>R</i></span> by <span class="inline-formula">+0.12</span> 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.<span id="page18"/> The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median <span class="inline-formula"><i>R</i></span> by <span class="inline-formula">+0.06</span>, suggesting that data assimilation yields significant benefits at the global scale.</p>