Selection of a semivariogram model in the study of spatial distribution of soil moisture

Abstract

The paper presents a selection of a semivariogram model in the study of spatial variability of soil moisture in a loess agricultural catchment. Soil moisture tests were carried out in the Moszenki village, 15 km northwest of Lublin. Soil moisture measurements were performed at two dates at 104 points, located on a rectangular surface measuring 700 × 1200 m. These points were laid out in the corners of a grid of squares with sides 100 m. In addition, 6 measurements were made at a distance of less than 100 m from the nearest points. Soil moisture was measured in the soil surface (0–5 cm). ArcGis software with Geostatistical Analyst extension was used for modelling semivariograms. In both terms, five models of semivariograms were used: stable, circular, spherical, exponential and Gaussian. Kriging was used for the estimation of soil moisture values. Among the semivariogram models analyzed in this study, the largest errors in the determined values of soil moisture relative to the empirical data were observed for the exponential model, and the smallest for the Gaussian model. However, it should be emphasized that the values of the analysed errors for the individual semivariogram models were similar. Application of the ordinary kriging method for interpolation of spatial distribution of soil moisture yields good results, but it has to be kept in mind that the final shape of the spatial distribution is influenced by the choice of the semivariance function model.

The practice of spatial variability modeling in geotechnical engineering

“…Several types of semivariogram models exist. For example, the exponential model is (9) and the Gaussian model is (10) The ordinary Kriging method obtains the weights (or influence) of the values, solving the Kriging equation shown in the equation (11) where n is the number of observations, m is the Lagrange multiplier used for the minimization of constraints, λ is the weight given to each of the observations and the sum of all λ is equal to one. The subscripts i and j denote the points sampled, the subscript 0 is the point in estimation and hij is the distance between Si and S0 from the semivariogram.…”

Performing Spatial Variabilityof Peat Depth by Using Geostatistics

“…The semivariogram and the variogram are the two basic tools for the analysis of spatial structure [12]. It is defined as a graphical display that shows the relationship (structure) between the variance of pairs of observations as function of the distance separating those observations (h) [13]. In the other word, it describes the variance within a group of distance (y-axis) against the distancebetweenpairsofpopulations(x-axis) (Figure 4).…”

Mapping variability of soybean leaf potassium concentrations to develop a sampling protocol

“…Spatial‐statistic methods are useful in the evaluation of the spatial distribution and variability of various factors within cropping systems (Webster, 1985; Xu‐dong et al., 2001). A primary method commonly used to describe the spatial dependencies of nutrient availability in a field as a fitted model is a semivariogram (Obroślak & Dorozhynskyy, 2017), which can be used to depict the spatial autocorrelation of a soil sample or tissue test (Xu‐dong et al., 2001). The development of a soybean trifoliolate leaf sampling protocol will allow stakeholders to properly monitor the K nutrition in their soybean crop and implement in‐season K fertilizer applications to maximize yield and profit.…”