EOF analysis of surface soil moisture field variability

Yoo, Chulsang; Kim, Sangdan

doi:10.1016/j.advwatres.2004.04.003

Cited by 73 publications

(51 citation statements)

References 35 publications

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“…The physical characteristic most related to the EOF pattern seemed to be soil texture (percent sand and percent clay). In contrast to the findings of Yoo and Kim (2004), topographic characteristics were relatively unimportant and even less relevant for dry conditions. Jawson and Niemann (2007) attributed this to the fact that topographic characteristics may influence soil moisture patterns mainly through lateral flow.…”

Section: Spatial Analysiscontrasting

confidence: 51%

“…Due to the smaller size of our dataset, the spatial EOF2 (10% explained variance) is statistically degenerated, whereas the second EOF in the study done by Perry and Niemann (2007) explained 9% of the spatial variability and could be related to the exposition (or PSRI; Potential Solar Radiation Index). Yoo and Kim (2004) investigated the characteristics of spatial and temporal variability of soil moisture and the relative roles of various affecting factors with the data of the SGP97 Little Washita field site (Famiglietti et al, 1999). Their first EOF accounted for more than 70% of the variability for interstorm periods and more than 60% for the whole dataset.…”

Section: Spatial Analysismentioning

confidence: 99%

“…Since introduced to the analysis of geophysical fields by Lorenz (1956), EOF analysis has been widely applied for the analysis of the spatial and temporal variability of large multidimensional datasets and has been commonly used in meteorological studies. More recently it has also been used to analyse soil moisture patterns at a large variety of scales, from the field scale for agricultural sites (Yoo and Kim, 2004), to catchment scales (Perry and Niemann, 2007), and to regional scales (Jawson and Niemann, 2007;Kim and Barros, 2002). The result of this analysis is a small number of spatial structures (EOFs) that explain a high percentage of variation of the dataset and temporal varying coefficients (ECs), which modulate the influence of these spatial structures in time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions

Korres

Koyama

Fiener

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes that are affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatiotemporal patterns of surface soil moisture in a grassland and an arable test site that are located within the Rur catchment in Western Germany. Surface soil moisture (0-6 cm) was measured in an approx. 50×50 m grid during 14 and 17 measurement campaigns

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Section: Spatial Analysiscontrasting

confidence: 51%

Section: Spatial Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions

Korres

Koyama

Fiener

et al. 2010

Hydrol. Earth Syst. Sci.

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show abstract

“…Kim and Barros (2002) and Jawson and Niemann (2007) used EOF analysis to decompose remotely-sensed soil moisture images from the SGP97 field campaign. Yoo and Kim (2004) used EOF analysis to decompose space-time patterns of ground-based soil moisture measurements for agricultural fields at the same site. The patterns of covariation that they identified are related to both topographic and soil properties.…”

Section: Introductionmentioning

confidence: 99%

Generation of soil moisture patterns at the catchment scale by EOF interpolation

Perry¹,

Niemann

2008

Hydrol. Earth Syst. Sci.

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Abstract. Spatial patterns of soil moisture cannot be adequately characterized by direct measurement for most practical applications, so interpolation between observations is required. Interpolation of soil moisture is complicated because multiple hydrologic processes can affect soil moisture and these processes can introduce distinct modes of variation into the soil moisture patterns. In this paper, a new method to interpolate soil moisture data is presented. This method accepts a dataset of soil moisture at widely-spaced locations on multiple dates and produces fine-scale patterns of soil moisture on the same dates. The method first uses Empirical Orthogonal Function (EOF) analysis to decompose the dataset into a set of time-invariant patterns of covariation (EOFs) and a set of associated time series (called expansion coefficients or ECs) that indicate the importance of the patterns on each date. The method then uses a statistical test to retain only the most important EOFs, and these EOFs are interpolated to the desired resolution using a standard estimation or interpolation method. The interpolated EOFs are finally combined with the spatial averages and the ECs to construct the finescale soil moisture patterns. Using the Tarrawarra dataset, the EOF-based interpolation method is shown to outperform analogous direct interpolation methods, and this improved performance is observed when as few as two observation dates are available. The improved performance occurs because EOF analysis decomposes soil moisture roughly according to the controlling processes and the most important EOFs exhibit distinct but more consistent spatial structures than soil moisture itself. Less predictable variation is also separated into higher order EOFs, which are discarded by the method.

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“…EOF analysis is widely applied to water resources management field Sauquet et al, 2000;Benjamin, 2002;Hisdal and Tallaksen, 2003;Yoo and Kim, 2004;Viron et al, 2006;Zveryaev and Arkhipkin, 2008;Perry and Niemann, 2008). The number of EOF is as many as 58, which is the same number with the stations used.…”

Section: Establishment Of Sad Curvementioning

confidence: 94%

Drought Analysis Based on SPI and SAD Curve for the Korean Peninsula Considering Climate Change

Kyoung¹,

Kwak²,

Kim³

et al. 2011

Climate Change - Geophysical Foundations and Ecological Effects

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EOF analysis of surface soil moisture field variability

Cited by 73 publications

References 35 publications

Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions

Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions

Generation of soil moisture patterns at the catchment scale by EOF interpolation

Drought Analysis Based on SPI and SAD Curve for the Korean Peninsula Considering Climate Change

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