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

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

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 (R) 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 SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. 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.

1027-5606
17-40
Beck, Hylke E.
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Pan, Ming
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Miralles, Diego G.
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Reichle, Rolf H.
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Dorigo, Wouter A.
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Hahn, Sebastian
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Sheffield, Justin
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Karthikeyan, Lanka
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Balsamo, Gianpaolo
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Parinussa, Robert M.
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van Dijk, Albert I.J.M.
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Du, Jinyang
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Kimball, John S.
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Vergopolan, Noemi
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Wood, Eric F.
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Beck, Hylke E.
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Pan, Ming
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Miralles, Diego G.
ce0c5485-e556-4c5b-a8c6-5241b090f307
Reichle, Rolf H.
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Dorigo, Wouter A.
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Hahn, Sebastian
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Sheffield, Justin
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Karthikeyan, Lanka
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Balsamo, Gianpaolo
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Parinussa, Robert M.
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van Dijk, Albert I.J.M.
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Du, Jinyang
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Kimball, John S.
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Vergopolan, Noemi
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Wood, Eric F.
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Beck, Hylke E., Pan, Ming, Miralles, Diego G., Reichle, Rolf H., Dorigo, Wouter A., Hahn, Sebastian, Sheffield, Justin, Karthikeyan, Lanka, Balsamo, Gianpaolo, Parinussa, Robert M., van Dijk, Albert I.J.M., Du, Jinyang, Kimball, John S., Vergopolan, Noemi and Wood, Eric F. (2021) Evaluation of 18 satellite- And model-based soil moisture products using in situ measurements from 826 sensors. Hydrology and Earth System Sciences, 25 (1), 17-40, [hess25172021]. (doi:10.5194/hess-25-17-2021).

Record type: Article

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 (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 (R) 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 SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. 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.

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Accepted/In Press date: 23 October 2020
Published date: 4 January 2021
Additional Information: Funding Information: Financial support. This research has been supported by the U.S. Publisher Copyright: © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.

Identifiers

Local EPrints ID: 471538
URI: http://eprints.soton.ac.uk/id/eprint/471538
ISSN: 1027-5606
PURE UUID: f2432ef9-ecc0-4da4-a26e-31a3afe0d3e2
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 10 Nov 2022 17:39
Last modified: 18 Mar 2024 03:33

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Contributors

Author: Hylke E. Beck
Author: Ming Pan
Author: Diego G. Miralles
Author: Rolf H. Reichle
Author: Wouter A. Dorigo
Author: Sebastian Hahn
Author: Lanka Karthikeyan
Author: Gianpaolo Balsamo
Author: Robert M. Parinussa
Author: Albert I.J.M. van Dijk
Author: Jinyang Du
Author: John S. Kimball
Author: Noemi Vergopolan
Author: Eric F. Wood

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