Present status of soil moisture estimation over the African continent

Myeni, Lindumusa; Moeletsi, Mokhele Edmond; Clulow, Alistair D.

doi:10.1016/j.ejrh.2018.11.004

Cited by 33 publications

(27 citation statements)

References 56 publications

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“…In tropical forest regions passive products often do not provide soil moisture retrievals, and when they do, the retrievals are typically less reliable than those from active products due to the dense vegetation cover (Al-Yaari et al, 2014;Chen et al, 2018;Miyaoka et al, 2017;Kim et al, 2018). Shedding more light on the strengths and weaknesses of soil moisture products in regions without dense measurement networks -for example using independent soil moisture products (Chen et al, 2018;Dong et al, 2019) or by expanding measurement networks (Kang et al, 2016;Singh et al, 2019) -should be a key priority for future research (Ochsner et al, 2013;Myeni et al, 2019).…”

Section: To What Extent Are Our Results Generalizable To Other Regions?mentioning

confidence: 99%

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

Beck¹,

Pan²,

Miralles

et al. 2021

Hydrol. Earth Syst. Sci.

216

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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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Section: To What Extent Are Our Results Generalizable To Other Regions?mentioning

confidence: 99%

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

Beck¹,

Pan²,

Miralles

et al. 2021

Hydrol. Earth Syst. Sci.

216

View full text Add to dashboard Cite

show abstract

Section: 9mentioning

confidence: 99%

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

Beck¹,

Pan²,

Miralles³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

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“…The lack of site-specific calibration equations as the result of financial constraints limits the use of collected data for verification of remote-sensing products and hydrological models in this region (Myeni et al, 2019). The proposed field calibration methodology can be reliably used to correct datasets that have been collected over years by soil moisture sensors that have been deployed in monitoring networks without prior site-specific calibrations in this region.…”

Section: Validation Of Dfm Capacitance Probesmentioning

confidence: 99%

Field calibration of DFM capacitance probes for continuous soil moisture monitoring

Myeni

Moeletsi

Clulow

2021

WSA

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This study was undertaken to derive textural and lumped site-specific calibration equations for Dirk Friedhelm Mercker (DFM) capacitance probes and evaluate the accuracy levels of the developed calibration equations for continuous soil moisture monitoring in three selected soil types. At each site, 9 probes (3 per plot) were installed in 2 m2 plots, for continuous soil moisture measurements at 5 different depths (viz. 10, 20, 30, 40 and 60 cm) under dry, moist and wet field conditions. Textural site-specific calibration equations were derived by grouping the same soil textural classes of each site regardless of soil depth, while lumped site-specific calibration equations were derived by grouping all datasets from each site, regardless of soil depth and textural classes. Sensor readings were plotted against gravimetrically measured volumetric soil moisture (θv) for different textural classes as a reference. The coefficient of determination (r2) was used to select the best fit of the regression function. The developed calibration equations were evaluated using an independent dataset. The results indicated that all developed textural and lumped site-specific calibration equations were linear functions, withr2 values ranging from 0.96 to 0.99. Relationships between the measured and estimated θv from calibration equations were reasonable at all sites, with r2 values greater than 0.91 and root mean square error (RMSE) values ranging from 0.010 to 0.020 m3∙m-3. The results also indicated that textural site-specific calibration equations (RMSE < 0.018 m3∙m-3) should be given preference over lumped site-specific calibrations (RMSE < 0.020 m3∙m-3) to attain more accurate θv measurements. The findings of this study suggest that once DFM capacitance probes are calibrated per site, they can be reliably used for accurate in-situ soil moisture measurements. The developed calibration equations can be applied with caution in other sites with similar soil types to attained reliable in-situ soil moisture measurements.

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Present status of soil moisture estimation over the African continent

Cited by 33 publications

References 56 publications

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

Field calibration of DFM capacitance probes for continuous soil moisture monitoring

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