Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

Pedrera‐Parrilla, Aura; Vijver, Ellen Van De; Meirvenne, Marc Van; Espejo-Pérez, A. J.; Giráldez, Juan Vicente; Vanderlinden, Karl

doi:10.1007/s11119-016-9435-z

Cited by 48 publications

(29 citation statements)

References 29 publications

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“…Temporal stability in ECa data has been demonstrated using Spearman rank correlation (De Caires et al, 2015), correlation coefficient between ECa in dry and wet soil conditions (Farahani et al, 2004;Pedrera-Parrilla et al, 2016;Serrano et al, 2013), and map comparisons (Li et al, 2007). One effective method of studying temporal stability of spatially and temporally variable soil properties consists in using principal component analysis (PCA) .…”

Section: Introductionmentioning

confidence: 99%

Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Section: Introductionmentioning

confidence: 99%

Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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“…We suspect that those decisions are made in the absence of high-quality information about spatial soil properties in the near subsurface. In this work we have shown that EOF analysis of timelapse EMI provides a key and often missing soil covariate [Pedrera-Parrilla et al, 2016]. We found ECa EOF1 to be a good predictor of spatial soil inventories, reducing cross-validation RMSE by 30.5% in clay weight percent, and 29.5% in total soil carbon weight percent ( Figure 5 and Table S5) using regression kriging versus linear interpolation.…”

Section: Environmental Controls Of Soil Propertiesmentioning

confidence: 77%

“…As an alternative strategy, here we investigate the correlation between the EMI survey results with the 2011 soil core data set. While this strategy is not new [ Pedrera‐Parrilla et al , ], here we argue that the EOF/EC pairs from the time‐lapse imagery will be better environmental covariates with soil properties as individual EMI surveys are known to be affected by soil temperature, soil water content, pore water pH, etc. [ Friedman , ].…”

Section: Resultsmentioning

confidence: 99%

“…In order to better isolate soil properties and states from ECa, previous work has developed correction factors for changes in soil temperature [Friedman, 2005] and used time-lapse differencing to isolate time-varying components, such as subsurface water flow pathways [Robinson et al, 2009;Franz et al, 2011]. As an extension of time-lapse ECa differencing, here we will further deconstruct the variability in the images using the technique of empirical orthogonal functions (EOF) [Perry and Niemann, 2007] in order to better predict spatial soil properties [Pedrera-Parrilla et al, 2016]. We will then investigate whether the deconstructed hydrogeophysical maps and landscape position (specifically elevation and topographic wetness index (TWI), as defined by Sorensen et al [2006]) are good environmental covariates to predict soil properties and states across space and through time.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal predictions of soil properties and states in variably saturated landscapes

Franz

Loecke

Burgin

et al. 2017

J. Geophys. Res. Biogeosci.

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Understanding greenhouse gas (GHG) fluxes from landscapes with variably saturated soil conditions is challenging given the highly dynamic nature of GHG fluxes in both space and time, dubbed hot spots, and hot moments. On one hand, our ability to directly monitor these processes is limited by sparse in situ and surface chamber observational networks. On the other hand, remote sensing approaches provide spatial data sets but are limited by infrequent imaging over time. We use a robust statistical framework to merge sparse sensor network observations with reconnaissance style hydrogeophysical mapping at a well‐characterized site in Ohio. We find that combining time‐lapse electromagnetic induction surveys with empirical orthogonal functions provides additional environmental covariates related to soil properties and states at high spatial resolutions (~5 m). A cross‐validation experiment using eight different spatial interpolation methods versus 120 in situ soil cores indicated an ~30% reduction in root‐mean‐square error for soil properties (clay weight percent and total soil carbon weight percent) using hydrogeophysical derived environmental covariates with regression kriging. In addition, the hydrogeophysical derived environmental covariates were found to be good predictors of soil states (soil temperature, soil water content, and soil oxygen). The presented framework allows for temporal gap filling of individual sensor data sets as well as provides flexible geometric interpolation to complex areas/volumes. We anticipate that the framework, with its flexible temporal and spatial monitoring options, will be useful in designing future monitoring networks as well as support the next generation of hyper‐resolution hydrologic and biogeochemical models.

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“…ECa can be intensively recorded in a parcel, in an easy and inexpensive way, by means of different electrical conductivity sensors currently on the market (e.g. Pedrera-Parrilla et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Characterization of soil fertility using the Rasch model

Moral

Rebollo

2017

J. Soil Sci. Plant Nutr.

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A quantification of the overall soil fertililty potential should integrate the main soil physical and chemical properties, with different units. The formulation of the Rasch model is proposed as an instrument to measure soil fertility potential, integrating 11 soil properties (clay, silt and sand content, organic matter -OM-, pH, total nitrogen -TN-, available phosphorus -AP-and potassium -AK-, cation exchange capacity -CEC-, and deep -ECdand shallow -ECs-soil apparent electrical conductivity, 0-90 and 0-30 cm depth respectively) measured at 70 locations in a field. In the case study, the considered soil variables fit the model reasonably, having an important influence on soil fertility, except pH, probably due to its homogeneity in the field. Moreover, a ranking of all soil samples according to their fertility potential and the influence of each variable on soil fertility are provided, being ECd, ECs, and the textural fractions of soil the most influential properties on soil fertility and, on the other hand, AP and AK the less influential properties. Results are in accordance with a previous work in the same field considering only five soil properties, both ECd and ECs and texture, denoting the importance of these variables to estimate soil fertility potential. Rasch model resulted to be useful to rationally determine locations in a field where high soil fertility potential exists and establishing those soil samples or properties with anomalies. This information can be necessary to conduct site-specific treatments, leading to a more cost-effective and sustainable field management.

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Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

Cited by 48 publications

References 29 publications

Temporal stability of electrical conductivity in a sandy soil

Temporal stability of electrical conductivity in a sandy soil

Spatiotemporal predictions of soil properties and states in variably saturated landscapes

Characterization of soil fertility using the Rasch model

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