基于BME-GWR法的景观单元土壤有机碳密度空间预测

Spatial Prediction of Soil Organic Carbon Density in Landscape Unit Based on BME-GWR Method

【目的】在预测土壤有机碳密度(SOCD)空间分布时,充分利用相关具有不确定性的或先验分布的多源数据,以提高其预测精度。【方法】在亚热带红壤丘陵区选取具有代表性的农业生态景观单元为研究区,以环境因子作为辅助变量,利用地理加权回归模型(GWR)、贝叶斯最大熵结合地理加权回归模型(BME-GWR)、按土地利用类型估算的贝叶斯最大熵结合地理加权回归模型(BME-GWRL)三种方法,计算并比较其对SOCD的空间预测结果的影响。【结果】BME-GWR和BME-GWRL模型方法对SOCD的空间异质性有更强的解释能力。BME-GWR和BME-GWRL模型的交叉验证结果的决定系数(R^(2))分别为0.81、0.79,均方根误差(RMSE)分别为0.35、0.33,平均绝对拟合误差(MAE)分别为0.21、0.19,模拟精度高于GWR模型。结合辅助变量的软数据可以更好地体现SOCD的空间局部特征,尤其是BME-GWRL模型采用了各土地利用类型的空间范围内拟合的软数据,比不划分土地利用类型直接以整个研究范围模拟(BME-GWR模型)的结果更准确。【结论】BMEGWRL考虑了软数据的估算单元的不确定性,可为合理利用多源辅助数据、提高模拟精度提供有效方法。

【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(R^(2))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.