Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

Tao Liu,Tiezhu Shi,Huan Zhang

doi:10.3390/su12229330

Abstract

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.

Highlights

Soil organic carbon (SOC) plays an important role in soil productivity and the global carbon cycle [1]
The spatial distribution of soil organic carbon density (SOCD) had strong spatial heterogeneity and spatial dependence, and the development and evolution of SOCD were influenced by many different environmental factors, so it is important and hard work to accurately map the spatial distribution of SOCD
Wang et al [10] compared geographically weighted regression (GWR) and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables, and the results showed that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties

Summary

Introduction

Soil organic carbon (SOC) plays an important role in soil productivity and the global carbon cycle [1]. The change in SOC directly impacts CO2 concentration, which influences solar heat absorption and release, affecting global climate change [5]. This interrelated mechanism and balanced relationship plays a vital role in maintaining normal human habits and building a suitable spatial environment. Exploring the spatial distribution of SOC and the effects of different environmental variables in small-scale areas is important. Overcoming the defect of regional differences is useful in estimating the global SOC pool before a large-scale application in a complex environment

Methods

Results

Discussion

Conclusion