Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Abstract

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC e 4-8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys

“…This relationship ( r 2 = 0.943) was then employed to convert EC 1:5 to EC e for the soil samples which EC e was not measured. This equation shows agreement with the relationships reported by Slavich and Petterson [ 33 ] for silt loam soil and by Yao et al [ 34 ] in the similar region. In this study, root zone EC e (i.e., the average EC e value of 0–1.0 m soil solum) was determined and used for mapping soil salinity at different time stamps.…”

Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

“…Theoretical models were then employed to interpret the experimental semivariograms and the model with the best fitting was chosen [22]. P Ni and P Cr were fitted with the exponential model, and the other four heavy metal single pollution indexes were all best fitted with the spherical model.…”

“…The methods of geostatistics use the stochastic theory of spatial correlation for both interpolation and for apportioning uncertainty [21]. Geostatistics is based on the theory of a regionalized variable which uses the technique of semivariogram to measure the spatial variability of a regionalized variable and provides the input parameters for the spatial interpolation of kriging [22, 23]. Geostatistics can also be used to assess the risk of exceeding critical values (regulatory thresholds, soil quality criterion) at unsampled locations and to simulate the spatial distribution of attribute values [21].…”

A New Method for Spatial Health Risk Assessment of Pollutants

“…There have been multiple applications of sequential Gaussian co-simulation for the study of spatial pattern of soil attributes including soil organic matter [5], bioavailable parts of phosphorus and potassium [6], and soil salinity [7]. However, even though, there is few case studies about its application for the assessment of contaminated sites.…”