| 【Objective】The study was to make full use of the multi-source data with uncertainty or prior distribution
in improving spatial prediction accuracy of soil organic carbon density（SOCD）.【Method】The typical agricultural
landscape unit of subtropical red soil hills were selected as the research area, and the environmental factors as auxiliary
variables. Three methods were used to calculate and compare the results of spatial prediction for SOCD, including
geographically weighted regression model（GWR）, Bayesian maximum entropy combined with geographically weighted
regression model（BME-GWR）, and Bayesian maximum entropy combined with geographically weighted regression
model estimated by land use type（BME-GWRL）.【Results】BME-GWR and BME-GWRL model had stronger ability
to explain the spatial heterogeneity of SOCD. The leave-one-out cross validation results of determination coefficient（R2） of BME-GWR and BME-GWRL models were 0.81 and 0.79, the root mean square errors（RMSE）of BME-GWR and BMEGWRL models were 0.35 and 0.33, and the mean absolute fitting errors（MAE）of BME-GWR and BME-GWRL models
were 0.19 and 0.21, respectively. These two methods had a higher fitting accuracy than GWR model, which could better reflect
the spatial local characteristics of SOCD with auxiliary variables as soft data. In particular, BME-GWRL model used the soft
data extracted from the prediction of SOCD under various land use types, and the prediction result was more accurate than that
of BME-GWR model, of which soft data was directly simulated in the whole study area without considering land use types.
【Conclusion】BME-GWRL considers the uncertainty of the estimation unit of soft data, which can provide an effective method
for improving the accuracy of spatial prediction with rational utilization of multi-source auxiliary data. |
