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

Jinlin Li,Chansheng He,Chen Zhao,Lanhui Zhang

doi:10.3390/su10082819

Jinlin Li, Chansheng He + Show 2 more

Open Access

https://doi.org/10.3390/su10082819

Copy DOI

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.

Highlights

Soil texture directly affects infiltration, retention, holding capacity of water, nutrients, and pollutants and long-term soil fertility [1,2]
The Performance of Both the Markov chain random field (MCRF) and ordinary kriging (OK) Methods in Calculating Different Soil Texture Types In this study, we evaluated the accuracy of the MCRF and OK methods in predicting different soil texture types
Using soil texture data obtained from 178 sampling points, both of the methods were applied at different search radiuses to calculate soil texture types by cross validation

Summary

Introduction

Soil texture directly affects infiltration, retention, holding capacity of water, nutrients, and pollutants and long-term soil fertility [1,2]. It influences the physical, chemical, biological, and hydrological processes of watersheds [3,4,5]. It is essential to accurately map the spatial distribution of different soil texture types at the watershed scale for eco-hydrological studies and water resource management [1,6,7,8,9,10]. It is difficult and expensive to map the spatial distribution of soil texture by field sampling at point scale [12]. Remote sensing has become an effective way to obtain soil texture data over large scales [13,14]

Methods

Results

Discussion

Conclusion