A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China

Abstract

Accurate mapping the spatial distribution of different soil textures is important for eco-hydrological studies and water resource management. However, it is quite a challenge to map the soil texture in data scarce, hard to access mountainous watersheds. This paper compares a nonlinear method, the Markov chain random field (MCRF) with a classical linear method, ordinary kriging (OK) for calculating the soil texture at different search radiuses in the upstream region of the Heihe River Watershed. Results show that soil texture values that were calculated by the OK method tends to predict soil texture values within a certain range (sand (12.098~40.317), silt (47.847~71.231), and clay (12.781~19.420)) because of the smoothing effect, thus leading to greater accuracy in predicting the major soil texture type (silt loam). Nonetheless, the MCRF method considers the interclass relationships between sampling points, leading to greater accuracy in predicting minor types (loam and sandy loam). Meanwhile, the OK method performed best for all the types at the radius of 65 km influenced by the densities of all the sampling points, while the best performance of the MCRF method differs with radiuses as the largest densities varying for different soil types. For loam and sandy loam, the OK method ignored them, thus the MCRF method is more suitable in mountainous areas with high soil heterogeneity.

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

“…tainous areas [41]. Badreddine Dahmoune et al (2019) used the Markov chain model to study the earthquake disaster in northwestern Algeria; and used the transition probability matrix to simulate the probability of earthquake occurrence in the next few decades [42].…”

“…Jinlin Li et al (2018) used the Markov chain and kriging interpolation methods to study the soil texture in the mountainous area of Northwest China. The results showed that the kriging interpolation method has a certain smoothing effect, which reduces the accuracy of the simulation; the Markov stochastic simulation method realizes the stochastic simulation of spatial variables, which is more suitable for the study of soil texture in mountainous areas [41]. Badreddine Dahmoune et al (2019) used the Markov chain model to study the earthquake disaster in northwestern Algeria; and used the transition probability matrix to simulate the probability of earthquake occurrence in the next few decades [42].…”

Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area

“…The CV values were lowest in period 3, followed by periods 1, 4, and 2 for the 10 cm layer, while the corresponding order for the 20 cm layer was 2, 1, 3, and 4 (Table 2). This is important because, as previous authors have noted, increases in the variability in soil moisture will inevitably impair the performance of calibration methods [101,102]. Second, there are clear mechanistic reasons for the differences in the methods' performance.…”

“…The CV was the most commonly used index to describe the variability of geographical elements because it is dimensionless. As it increases, the variability of soil moisture rises, and the performance of calibration methods tends to decline [101,102], thus explaining the better performance of the four extrapolation methods for SMC in the 10 cm layer than in the 20 cm layer.…”

An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau

“…Recently, there has been considerable progress towards creating large-scale SHP datasets for the mountainous areas of the QTP. These new datasets include the soil organic carbon (Ding et al, 2019;Song et al, 2016;Wang et al, 2021;Yang et al, 2016), soil thickness (Yang et al, 2016;Zhang et al, 2016), and soil texture (Li et al, 2018c;Lu et al, 2017), which have improved our understanding of the spatial distribution and estimation accuracy of SHP datasets over the QTP (Li et al, 2020). Field sampling and laboratory measurements are more difficult and time consuming for some key SHPs, such as the soil water retention curve and saturated hydraulic conductivity, than the equivalent investigations for more basic soil parameters, such as texture (He et al, 2021;Tian et al, 2017).…”