Temporal persistence and stability of surface soil moisture in a semi-arid watershed

Cosh, Michael H.; Jackson, Thomas J.; Moran, Susan; Bindlish, Rajat

doi:10.1016/j.rse.2007.07.001

Cited by 215 publications

(134 citation statements)

References 19 publications

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“…At each watershed, observations collected from between 8 and 15 sites are averaged using the Thiessen polygon method to produce a coarse-scale near-surface soil moisture observation with spatial support similar to the AMSR-E observations. Intensive field experiments have shown these coarse-scale estimates to be very accurate, with errors on the order of 0.01 m 3 m −3 (Bosch et al, 2006;Cosh et al, 2006Cosh et al, , 2008. The soil moisture has also been observed below the nearsurface layer at Little Washita since 2007, and at Little River since 2004, with observations potentially made at every 5 cm from 5 to 60 cm.…”

Section: The Soil Moisture Data Setsmentioning

confidence: 97%

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Draper

Reichle

2015

Hydrol. Earth Syst. Sci.

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Abstract. A 9 year record of Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SM short ), mean seasonal (SM seas ), and inter-annual (SM long ) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10 −3 m 3 m −3 ) 2 , of which 19 % was in SM long , 26 % in SM seas , and 55 % in SM short . The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SM long , 24 % in SM seas , and 38 % in SM short . For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SM short , assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SM seas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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Section: The Soil Moisture Data Setsmentioning

confidence: 97%

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Draper

Reichle

2015

Hydrol. Earth Syst. Sci.

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“…Firstly, although direct measurement is the most accurate method for estimating soil moisture, this technique is expensive, time consuming and only provides point measurements which limited the study of spatio-temporal variations in soil moisture in arid environments (Gao et al, 2013). The development of modeling techniques to estimate soil moisture availability has been an area of extensive research during the past decade (Jacobs et al, 2004;Cosh et al, 2006Cosh et al, , 2008Starks et al, 2006;Vivoni et al, 2008). However, retrieved soil moisture products by remote sensing have focused on near surface soil moisture and very few reports refer to the whole soil profile or the relationship between surface and profile variability (Kachanoski and de Jong, 1988;Hupet and Vanclooster, 2002;Schmugge et al, 2002;Ceballos, 2003, 2005;Pachepsky et al, 2005;De Lannoy et al, 2006;Starks et al, 2006;Guber et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of root zone soil moisture in artificially revegetated and natural ecosystems at an arid desert area, NW China

Pan

Wang

Zhang

et al. 2015

Ecological Engineering

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“…Soil moisture spatial patterns, in general, have a strong temporal stability (Vachaud et al 1985) even for large areas that range in size from 100 to 1000 km 2 . Thus the soil moisture temporal pattern observed at one location closely follows the temporal pattern of the spatial average for the given area (Martínez-Fernández and Ceballos 2003;Cosh et al 2008;Wagner et al 2008;Brocca et al 2010), which enabled us to validate the triangle method results using ground soil moisture measurements. Given the resources necessary to monitor soil moisture across a broad region, we were only able to utilize seven measurement stations.…”

Section: Discussionmentioning

confidence: 68%

Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery

Przeździecki¹,

Zawadzki²,

Cieszewski³

et al. 2017

Silva Fenn.

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Highlights• Temperature vegetation dryness indices were calculated from MODIS satellite imagery to estimate subsurface soil moisture at different depths using the triangle method.• Observations were carried out over the vast areas of Georgia and South Carolina, USA, covered with diverse land uses that, included dense forests and agricultural areas.• The triangle method may be useful in forestry management applications where the productivity potential of a region and the hydrologic role of forests in that region are of concern. AbstractWe describe here a study based on analysis of vegetation indices and land surface temperatures, which provides relevant information for estimating soil moisture at regional scales. Through an analysis of MODIS satellite imagery and in situ moisture data, the triangle method was used to develop a conceptual land surface temperature−vegetation index model, and spatial temperaturevegetation dryness index (TVDI) values to describe soil moisture relationships for a broad landscape. This study was situated mainly within two states of the southern United States (Georgia and South Carolina). The total study area was about 30 million hectares. The analyses were conducted using information gathered from the 2009 growing season (from the end of March to September).The results of the study showed that soil moisture content was inversely proportional to TVDI, and that TVDI based on the normalized difference vegetation index (NDVI) had a slightly higher correlation with soil moisture than TVDI based on the enhanced vegetation index (EVI).

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Temporal persistence and stability of surface soil moisture in a semi-arid watershed

Cited by 215 publications

References 19 publications

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Spatio-temporal variability of root zone soil moisture in artificially revegetated and natural ecosystems at an arid desert area, NW China

Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery

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