جستجوی مقالات مرتبط با کلیدواژه « interpolation techniques » در نشریات گروه « آب و خاک »

The study of spatial distribution of soil properties for optimal soil management and proper utilization of non-renewable soil resources is of particular importance. The copula function is one of the new interpolation techniques that are widely used in various sciences such as hydrology. Thus, the aim of this study was to evaluate the spatial variation of some soil properties using the copula function and to compare with geostatistics techniques. Sampling by regular networking was done in a 484 ha area in the west of Baft city, Kerman province, and 121 surface soil samples were collected. After air drying and passing through a 2 mm sieve, the percentage of organic matter and clay were determined in soil samples. To interpolate, four functions of the Archimedean copula including the Clayton, Frank, Gumbel and Joe functions, and geostatistics techniques including simple kriging, ordinary kriging, universal kriging and disjunctive Kriging and the Inverse Distance Weighting (IDW) method were used. The results were analyzed using Root Mean Square Error (RMSE), determination coefficient (R2), mean absolute error (MAE), and Mean Bias error (MBE). In order to fit the copula function on the data, the distribution function of the studied variables was determined. The results showed that the distribution of each of the studied variables is different and is explained by different distribution functions. Also, with increasing distance, the value of correlation for all studied variables decreased so that after a distance of 2000 meters, they do not show any spatial correlation. Comparison of the Copula function and geostatistical techniques based on evaluation criteria showed that the Copula function had a better performance in estimating the studied variables and the estimation error for the Copula function were calculated less. In general, the results of this study showed that due to the skewed nature of soil data, Copula function have the ability to fully express the probabilistic dependence and can be considered in spatial studies.

Spatial and temporal variations of soil characteristics occur in large and small scales. Study of these variations is very time-consuming and costly especially in large scales. In order to the fast and reliable determination of soil properties, various interpolation techniques have been developed and applied. The most widely used interpolation techniques in various sciences is the Kriging types. The copula function is one of the new interpolation techniques that are widely used in sciences such as hydrology. Thus, the aim of this study was to evaluate the spatial variations of some soil physical properties using copula function and to compare with geostatistics techniques.

Sampling by regular networking was done in an area of 484 ha located in 10 km from the west of Baft city, Kerman province and finally, 121 surface soil samples were collected. After air drying, the apparent bulk density was determined using the Hunk, then the soil samples were passed through a 2 mm sieve to determine the percentage of sand. To interpolate, four functions of the Archimedean copula including the Clayton, Frank, Gumbel and Joe functions, and geostatistics techniques including simple, ordinary, universal and disjunctive Kriging and the Inverse Distance Weighting (IDW) method were used. The results were analyzed using Root Mean Square Error (RMSE), determination coefficient (R2), Mean Absolute Error (MAE), and Mean Bias Error (MBE).

Based on the descriptive statistics, soil bulk density and soil sand followed a normal and skewed distribution, respectively. In order to fit the copula function, the distribution functions of the studied variables were firstly determined. The results showed that the sand and bulk density followed the Frechet (3P) and Wakeby distribution functions, respectively. Also, based on the Pearson correlation coefficient, the correlation between pairs of points was determined in distances less than 2000 m and distances more than 2000 m were known as an independent distance. The estimation efficiency based on the determination coefficient (R2) showed that value of determination coefficient for copula function for the sand variable, 6% and for bulk density 8%, more than conventional geostatistics techniques were obtained. Also, the estimation error of copula function was minimum that indicate good performance of copula function to estimate the spatial variation of soil physical properties.

The results of study showed that copula function, especially the median copula, have the better performance for estimation the studied soil properties. One of the most important reasons for this superiority is the ability to fit the marginal distribution function on the data in copula, while it is not possible in geostatistics techniques. Other reasons include the ability to express the correlation between the data at different intervals and the lack of sensitivity to outlier data in copula relative to conventional geostatistics techniques. Due to the skewness nature of soil data, as well as the need for more accurate analysis and interpretation of actual soil data, copula functions can be widely used to estimate of soil properties.

For design and implementation of soil conservation programs, accurate and optimized mapping of soil properties is required. The type of interpolation method is one of the most important issue involved in mapping soil properties. Six ordinary methods of spatial interpolation were compared to determine their suitability for estimating some quality properties of topsoil including clay content, electrical conductivity, equivalent calcium carbonate and organic carbon in the Naqade region farmlands located in west Azerbaijan. The area of study region was about 8300 ha. For this purpose, 282 topsoil samples were collected randomly and tested for laboratory analysis. Deterministic methods (radial basis function and global polynomial interpolation) as well as geostatistical estimators (ordinary Kriging) were utilized to map related soil properties. The highest and lowest CV was obtained for EC and clay content variables, respectively. For clay and electrical conductivity, the exponential model, for equivalent calcium carbonate the Gaussian model, and for organic carbon the spherical were the best fitting models to calculate the experimental variogram. According to the results, the ordinary Kriging approach characterized with the minimum error indicator of bias-variance trade-off (BVTO) for evaluating clay, electrical conductivity, equivalent calcium carbonate and organic carbon. The thin plat spline model of radial basis function showed the maximum error in assessment of studied attributes. Eventually, the interpolation maps were generated regard to the most appropriate interpolators.