Temporal stability of electrical conductivity in a sandy soil

Abstract

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.

Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping

“…For predicting the clay contents at the Mehiläissaari field (barley in 2021, rapeseed in 2022), the model parameters ECa and moisture were significant. Similar results have been reported in several studies on agricultural fields (Pedrera‐Parrilla et al, 2016; Sudduth et al, 2005). Contrasting with this, at the grass ley sites, temperature and reflectance were significant predictors.…”

“…Correlations between ECa and texture (clay) were generally low but increased at higher water contents. Pedrera‐Parrilla et al (2016) used principal component analyses to derive information on the spatial stability of ECa data across measurement dates in a dryland area in Spain. They found the significant differences in clay and stone contents to be responsible for ECa spatial patterns and reported an exponential relation between soil water content and ECa.…”

Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil‐Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite‐Derived NDVI Time Series and Electromagnetic Induction Measurements

“…On the other hand, some simpler techniques with much less computational cost like time‐lapse differencing have been used to isolate underlying patterns and processes (Robinson et al, , ) to overcome the temporal effect of ECa. More recently, statistical techniques using principle component analysis and empirical orthogonal function have been used to isolate underlying patterns (Franz et al, ; Pedrera‐Parrilla et al, ) and remove temporal affects. These state‐of‐the‐art techniques can be employed to generate more features from EMI data, which can be integrated into the proposed unsupervised learning framework so that more interesting findings could be achieved.…”

Field-Scale Apparent Electrical Conductivity Mapping of Soil Properties in Precision Agriculture

“…Figure10shows that VWC rises together with the ECa and strong coefficient of correlation (0.972) was observed. A small change in moisture content leads to greater change in conductivity Pedrera- Parrilla et al (2016). andSamouëlian et al (2005) also made known that such fit exists between electrical conductivity and soil water content.…”