The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Dorigo, Wouter; Wagner, Wolfgang; Hohensinn, Roland; Hahn, Sebastian; Paulik, Christoph; Xaver, Angelika; Gruber, A.; Drusch, Matthias; Mecklenburg, Susanne; Oevelen, P.J. van; Robock, Alan; Jackson, Thomas J.

doi:10.5194/hess-15-1675-2011

Cited by 951 publications

(474 citation statements)

References 109 publications

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“…5 Soil moisture plays a critical role in land-atmosphere interactions, both as a store of water and as a control on both evaporation and run-off. While there are a few regions with substantial in situ soil water observations [19], the coverage of these observations is patchy, and there is no consistency over methodology or depth. Global coverage is possible with microwave satellite sensors (e.g.…”

Section: Rstaroyalsocietypublishingorg Philmentioning

confidence: 99%

Water security, global change and land–atmosphere feedbacks

Dadson

Acreman

Harding

2013

Phil. Trans. R. Soc. A.

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Section: Rstaroyalsocietypublishingorg Philmentioning

confidence: 99%

Water security, global change and land–atmosphere feedbacks

Dadson

Acreman

Harding

2013

Phil. Trans. R. Soc. A.

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“…Direct in situ comparisons were made between RZSM estimates with in situ data from the International Soil Moisture Network (ISMN; Dorigo et al, 2011). The ISMN provides access to a host of meteorological and soil moisture data (at many depths).…”

Section: In Situ Soil Moisturementioning

confidence: 99%

“…Air temperature and precipitation data were obtained from the Parameter elevation Regression on Independent Slopes Model (PRISM; Daly et al, 1994) from grid cells (4 km spatial resolution) co-located with examined in situ sites (PRISM Climate Group 2015). These data were used to screen dates below freezing and with significant precipitation data, as suggested by Dorigo et al (2011), to enhance quality control.…”

Section: Other Datasetsmentioning

confidence: 99%

Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

Tobin

Torres

Crow

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25 • and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T ). We examined five different eras (1997-2002; 2002-2005; 2005-2008; 2008-2011; 2011-2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0.5).The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash-Sutcliffe coefficients (NSs) for ARM ranged from −0.1 to 0.3 and were similar to the results obtained from the USCRN network (0.2-0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1-0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02-0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05-0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

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“…These and other soil moisture products from other projects have been collected in the International Soil Moisture Network (ISMN) database 1 (e.g. Dorigo et al, 2011). The data set presented in this paragraph was downloaded from the ISMN database.…”

Section: The Amma Databasementioning

confidence: 99%

A physically based approach for the estimation of root-zone soil moisture from surface measurements

Manfreda

Brocca

Moramarco

et al. 2014

Hydrol. Earth Syst. Sci.

101

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Abstract. In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. It was derived from a simplified soil water balance equation for semiarid environments that provides a closed form of the relationship between the root zone and the surface soil moisture with a limited number of physically consistent parameters. The method sheds lights on the mentioned relationship with possible applications in the use of satellite remote sensing retrievals of soil moisture. The proposed approach was used on soil moisture measurements taken from the African Monsoon Multidisciplinary Analysis (AMMA) and the Soil Climate Analysis Network (SCAN) databases. The AMMA network was designed with the aim to monitor three so-called mesoscale sites (super sites) located in Benin, Mali, and Niger using point measurements at different locations. Thereafter the new formulation was tested on three additional stations of SCAN in the state of New Mexico (US). Both databases are ideal for the application of such method, because they provide a good description of the soil moisture dynamics at the surface and the root zone using probes installed at different depths. The model was first applied with parameters assigned based on the physical characteristics of several sites. These results highlighted the potential of the methodology, providing a good description of the root-zone soil moisture. In the second part of the paper, the model performances were compared with those of the well-known exponential filter. Results show that this new approach provides good performances after calibration with a set of parameters consistent with the physical characteristics of the investigated areas. The limited number of parameters and their physical interpretation makes the procedure appealing for further applications to other regions.

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The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Cited by 951 publications

References 109 publications

Water security, global change and land–atmosphere feedbacks

Water security, global change and land–atmosphere feedbacks

Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

A physically based approach for the estimation of root-zone soil moisture from surface measurements

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