Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Tung Gia Pham,Martin Kappas,Linh Hoang Khanh Nguyen,Chuong Van Huynh

doi:10.3390/ijgi8030147

Tung Gia Pham, Martin Kappas + Show 2 more

Open Access

https://doi.org/10.3390/ijgi8030147

Copy DOI

Abstract

Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km × 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method.

Highlights

Soil quality information plays a vital role in land use planning, resource management and site investigation [1]
Our results are consistent with the findings of Liu et al [72], who stated that land use type (LUT) has the most influence on soil organic carbon (SOC) when compared to other environmental variables
For soil pH and total nitrogen (TN) mapping, a single regression of LUT and predicted variables were established for interpolation, whereas multiple regressions of LUT and topographic wetness index (TWI) variables were used for the SOC mapping model

Summary

Introduction

Soil quality information plays a vital role in land use planning, resource management and site investigation [1]. Researchers have suggested a combination of regression and spatial interpolation, called regression kriging (RK), to determine the spatial distribution of soil characteristics [14,15,16,17,18,19,20,21]. For this method, the selection of auxiliary variables is essential and remotely sensed images are typically the first choice [22]. The accuracy of the RK method is not clear in all of the case studies because it depends on actual soil and environmental variable relationships [26]

Methods

Discussion

Conclusion