Simulation of Moisture Deficits and Areal Interpolation by Universal Cokriging (original) (raw)
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The Romanian Soil Survey System does not imply, up to the present date, the use of digital methods in representing field campaign results or for mapping soil parameters. The presented study tests several geostatistical methods to model some soil parameters (soil pH and topsoil humus content), mainly in order to observe the differences induced by the scale of the approach and to test existing data. In this respect, three differently dimensioned analysis scales were chosen, all parts of the same larger region, located in Iaşi County. On the chosen areas the main three categories of methods used in pedometrics were tested: methods of the kriging family (ordinary kriging, cokriging), regression methods applied both globally and locally (Geographically Weighted Regression) and the combined approach of regression-kriging respectively. In order to test the results were used cross-validation and independent sample validation. The root mean square error (RMSE) was used as selection criteria for the choice of the optimum method. The study proves that among the various interpolation methods tested, the regression-kriging approach gives better results and that the local approach, using GWR, is superior to the global regression approach. Moreover, the pH proved to be more spatially predictable compared to the topsoil humus content.
IJERT-Soil Fertility Mapping: Comparison of Three Spatial Interpolation Techniques
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/soil-fertility-mapping-comparison-of-three-spatial-interpolation-techniques https://www.ijert.org/research/soil-fertility-mapping-comparison-of-three-spatial-interpolation-techniques-IJERTV3IS111043.pdf Soil fertility mapping is essential when planning land use and developing crop fertilization strategies. Interpolation techniques are widely used for the mapping processes in varied fields of soil sciences to estimate the soil property values at unsampled sites. The aim of this work was to evaluate and compare the performances of three spatial interpolation methods (inverse distance weighting IDW; ordinary kriging OK; and spline) using the statistical criterion of root mean square error (RMSE) for cross validation and then generate a set of accurate soil property maps (pH, organic matter, phosphorus, and potassium). The study covers an area of 45 000 ha in the Loukkous irrigated district, Northwest of Morocco and includes 934 soil samples. These samples were collected from irregular cross-line nodes grids and were analyzed in the laboratory. Exploratory data analyses were first adopted to identify and remove all spatial outliers and to validate the normal distribution required for geostatistical analyses. In all cases, the distributions were found strongly skewed and needed to be transformed. Box-Cox transformations were used; they performed well for all soil properties. Experimental variograms were fitted with the exponential, spherical and Gaussian models. With the use of the lowest RMSE approach, ordinary kriging model was selected as the best method compared to IDW and spline for interpolating with the exponential variogram for pH, organic matter and potassium, and the spherical model for phosphorus. A map was generated for each soil property. The maps indicated the low level of potassium and organic matter soil content throughout the study area. These maps could be used for optimizing crop fertilizing considering the different soil fertility levels.
Geostatistic Interpolation of Soil Moisture
Hydrology Research
The spatial distribution of soil moisture defines preferential flow paths in the unsaturated zone. Hence, three dimensional (3D) estimates of soil moisture are of great importance to understand transport of contaminants as well as remediation processes in the unsaturated zone. In this study 3D estimates conditioned on spatially frequent observations of soil moisture, have been obtained by kriging. The observations were divided into subdomains consistent with the local stratigraphy, and directional semivariogram analysis was applied. It was found difficult to clearly identify a 3D semivariogram function in this case, but from a georadar survey two semivariogram functions were derived, describing two different sedimentological units. By conditioning the estimates of soil moisture on the sedimentological architecture computed by indicator kriging, morc accurate estimates were achieved. These improvements were quantified by a 'jackknife' cross validation procedure. Besides the practical aspects of finding, the most important flow paths, estimates of soil moisture are valuable when validating unsaturated flow models.
A generic framework for spatial prediction of soil variables based on regression-kriging
Geoderma, 2004
A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets. D
Communications in Soil Science and Plant Analysis, 2019
Spatial interpolation methods are frequently used to characterize soil attributes' spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.
Evaluating the Soil Moisture Content Through Different Interpolation Methods
2016
Water is vital for the plant growth. An adequate amount of soil moisture content is required in order to increase plant growth and yield. The spatial distribution can be determined using different methods for different depths of soil moisture content. In this study the spatial distribution is created at four different soil depths (30, 60, 90 and 120 cm) using deterministic and stochastic methods. In order to determine the most appropriate methods, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values were compared between the methods. The lowest RMSE (11.296) and MAE (7.821) values were obtained for 0-30 cm depth of soil moisture content by Ordinary Kriging method. As for the depth of 30-60 cm, the lowest RMSE (13.682) and MAE (8.444) values were obtained through Inverse Distance Weight (IDW). As for the depth of 60-90 cm, the lowest RMSE (17.767) and MAE (11.473) values were obtained through the Radial Basis Function (RBF). As for the depth of 90-120 cm, the lowest RMS...
Moisture content is a fundamental property of soils. Spatial variability of soil moisture has a major influence on many soil properties and processes contributing primarily in the variation in storage, transport and availability of nutrients thus affecting directly to the growth and development of plants. Geostatistics has been extensively used for quantifying the spatial variability of many environmental variables including soil moisture content. We aim to analyse and characterize the spatial variability pattern of topsoil gravimetric soil moisture content of an area in western Hungary using geostatistical interpolation methods. Ordinary kriging technique is applied for the estimation of soil moisture values at unsampled location and the results provided in terms of prediction maps and its associated variance suggest that ordinary kriging can be effectively used to analyze and describe soil moisture spatial variability. The results can be used as a source of information for the dev...
A Comparative Study of Interpolation Methods for Mapping Soil Properties
Agronomy Journal, 1999
and Salas (1985) compared kriging with several other interpolation techniques, including inverse distance, for The choice of an optimal interpolation technique for estimating annual precipitation distributions and found kriging to soil properties at unsampled locations is an important issue in sitebe superior to inverse distance weighting. Warrick et specific management. The objective of this study was to evaluate inverse distance (InvD) weighting, ordinary kriging (KO), and lognor-al. (1988) also reported kriging to be better than inverse mal ordinary kriging (KO log ) to determine the optimal interpolation distance weighting for mapping potato (Solanum tumethod for mapping soil properties. Relationships between statistical berosum L.) yield and soil properties, such as percent properties of the data and performance of the methods were analyzed of sand, Ca content, and infiltration rate. Laslett et al. using soil test P and K data from 30 agricultural fields. For InvD (1987) obtained more accurate pH predictions by using weighting, we used powers of 1, 2, 3, and 4. The numbers of the closest kriging than by using inverse distance weighting. Leeneighboring points ranged from 5 to 30 for the three methods. The naers et al. (1990) found kriging to be superior to inverse results suggest that KO log can improve estimation precision compared distance weighting for the majority of their soil Zn conwith KO for lognormally distributed data. The criteria helpful in tent data sets. Criteria for comparing the methods were deciding whether KO log is applicable for the given data set were the mean squared error (Warrick et al., 1988), sum of Kolmogorov-Smirnov goodness-of-fit statistic, coefficient of variation, skewness, kurtosis, and the size of the data set. Careful choice Dep. of Crop Sciences, 1102 S. Goodwin Ave., Univ. of Illinois, Abbreviations: D, Kolmogorov-Smirnov goodness-of-fit statistic; G, Urbana, IL 61801. Received 30 Dec. 1997. *Corresponding author goodness-of-prediction statistic; InvD, inverse distance; KO, ordinary (dbullock@uiuc.edu).
Conditional Gaussian co-simulation of regionalized components of soil variation
Geoderma, 2006
Stochastic simulations are increasingly used to represent and characterize the spatial structure and uncertainty of soil properties. Due to the potential presence of scale dependencies, simulations of the total variables can represent a mixture of spatial components operating at different scales, which may be better interpreted separately. While coregionalization analysis and factorial kriging provide means to characterize and estimate scale-specific components of variation, no methods are available that allow a proper representation of their spatial structure and an assessment of their spatial uncertainty. In this paper, the formulation of cokriging of regionalized components and regionalized factors is first reviewed, after which a method for the conditional Gaussian co-simulation of regionalized components and regionalized factors is presented. We highlight the need for performing conditional simulations for all structures jointly to reduce the correlation between components for different structures and avoid any bias on the sum of simulated components. Simulations obtained with this method adequately represent both the specific features of, and the uncertainty associated with, each scale of variation, as modeled in a coregionalization analysis. The method is applied to an agronomic dataset to characterize the spatial uncertainty of regionalized components of plant available phosphorous and potassium in the soil and illustrate advantages of this new simulation approach. D