مجله آب و خاک (Jul 2024)
Evaluating the Precision of the Conditioned Latin Hypercube Sampling Method for Selecting Soil Samples to Generate Digital Maps of Soil Properties
Abstract
Introduction Soil properties play a crucial role as they determine the soil's suitability for different types of plant growth, ecosystems, and biota functioning. They have a significant impact on nutrient cycling, carbon sequestration, and soil management. Digital Soil Mapping (DSM) is a process aimed at delineating soil properties. Soil sampling for DSM serves as a fundamental step in improving prediction accuracy and is crucial for incorporating variability in terms of environmental covariates. Conditioned Latin Hypercube (CLH) sampling is a technique utilized to generate a sample of points from a multivariate distribution conditioned on one or more covariates. Numerous researchers (Ramirez-Lopez et al., 2014; Adhikari et al., 2017; Zhang et al., 2022) have endorsed this approach in their studies, following its inception by Minasny and McBratney in 2006. However, there has been limited research to date on the impact of the Latin hypercube method's random sample selection process on the accuracy of resulting maps. Hence, the central question remains: Is the Latin hypercube sampling method, which is currently widely adopted, always a dependable approach in this field? Materials and Methods The study area covers longitudes 50°35'47'' to 51°29'' east and latitudes 31°36''31'' to 32°15'48'' north in Borujen city, Chaharmahal, and Bakhtiari Province. The region, with an average elevation of 2338 meters above sea level, receives an annual rainfall of 250 millimeters and maintains an average temperature of 11.5 degrees centigrade. In this investigation, inherited data from soil studies were utilized, consisting of 250 samples distributed across the study area. In this research, the studied characteristics included percentage of equivalent calcium carbonate, clay, and soil organic carbon at a depth of 0 to 30 cm. Land component variables were extracted using the Alus Palsar digital elevation model with a spatial resolution of 12.5 meters. In the initial stage, digital maps of equivalent calcium carbonate, clay, and soil organic carbon were generated using the support vector machine method. The modeling process proceeded until a highly accurate model was achieved, with the root mean square error percentage (RMSE%) being less than 40. The Latin hypercube approach was utilized for sample design, with 500 repetitions in this study. After selecting sampling points for each run using the Latin hypercube method, these points were mapped onto a detailed map, and the corresponding feature values were retrieved. The final map was created based on the extracted points. Subsequently, the latin hypercube approach was employed to generate soil property maps for each selected dataset. Validation was conducted using criteria such as the coefficient of explanation, root mean square error, and root mean square error in multiple iterations to ensure the accuracy of the generated maps. Results and Discussion The results distinctly illustrates the varied selection of sampling positions with each implementation of the Latin hypercube method. It is important to note that there may be some overlaps in different implementations. Consequently, the primary question arises: Is a one-time execution of the Latin hypercube sufficient for selecting study points? The findings indicate that the support vector machine model achieves satisfactory accuracy for all the examined characteristics. In the studied area, the environmental factors such as slope and elevation were identified as a significant predictors for estimating percentage of equivalent calcium carbonate. Conclusion In the present study, the accuracy of the latin hypercube method was assessed for selecting sampling location for digital soil mapping endeavors in Chaharmahal and Bakhtiari Province. Given the impracticality of collecting numerous field samples to evaluate the soil sampling method, this research aimed to employ simulation methods based on highly accurate maps for this purpose. The results indicate that the different outputs of the Latin hypercube method influence the accuracy of modeling, although this effect is also influenced by the specific feature under investigation and the extent of its variability within the study area. Considering that the Latin hypercube method is based on the principle that samples are randomly selected in each class of environmental parameters, it is suggested that future studies using this method should account for this principle. Adequate consideration should be given, and the selection of sampling locations should rely on multiple implementations of the Bhattacharya distance method to ensure robustness and reliability.
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