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Can the spatial prediction of soil organic matter be improved by incorporating multiple regression confidence intervals as soft data into BME method?

Zhang, Chu-tian, Yang, Yong
Catena 2019 v.178 pp. 322-334
Bayesian theory, autocorrelation, confidence interval, kriging, landscapes, models, prediction, regression analysis, soil management, soil organic matter, soil properties, soil surveys, uncertainty, vegetation index
A better understanding of the spatial pattern of soil organic matter (SOM) is important for scientific soil management. As multisource secondary data become increasingly cheap and readily available, numerous methods have been established to incorporate secondary information; however, these methods exhibit limitations under certain conditions due to their relatively strict requirements on secondary data. In this study, we tried to integrate sampled soil data and secondary data more effectively within the framework of Bayesian maximum entropy (BME). Specifically, multiple linear regression (MLR) and geographically weighted regression (GWR) were run 100 times based on environmental covariates such as terrain indices, vegetation indices and categorical variables obtained from soil maps. Then, the 95% confidence interval was derived from the multiple prediction values at each of the soft data points. For comparison, some conventional techniques, including ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK), were also applied. The results showed that BME exhibited a prediction accuracy comparable to that of OK and maintained the prediction uncertainty at a low level, while other studied methods (MLR, GWR, RK and GWRK) were all significantly inferior to BME and OK. The proposed methodology in this study represents a promising scenario for the digital soil mapping, especially when the relationships between the target soil attributes and various secondary information are not strong or residuals of trend models show insignificant spatial autocorrelation.