[Objective] The objective of this project was to evaluate and compare spa- tial estimation accuracy by ordinary kriging and regression kriging with MODIS data, predicting SOM contents using limited available data in Shimen County, Hunan Province, China. [Method] Terrain parameters （derived from DEM） and Normalized differential vegetation index （NDVI）, Land surface temperature （LST） （derived from MODIS data） were used as auxiliary data to predict the SOM spatial distribution. The mean error （ME） and mean square error （RMSE） were adopted to validate the SOM prediction accuracy. The descriptive statistics and data transformation were conducted by using computer technology. [Result] Regression kriging with terrain and remotely sensed data was superior to ordinary kriging in the case of limited available samples; even the linear relationship between environmental variables and SOM content was moderate. The accuracy assessment showed that the regression kriging method combining with environmental factors obtained a lower mean predication error and root mean square prediction error. The relative improvement was 6.03% compared with ordinary kriging. [Conclusion] Remotely sensed data such as MODIS im- age have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction in the hilly regions.
调查研究了湘南低山红壤区油茶林土壤肥力质量,基于研究区采集的99个土壤样品数据,利用主成分分析法(PrincipalComponent Analysis,PCA)及相关数理统计分析法,计算了PCA中各因子的综合因子载荷(Norm值),建立了低山红壤油茶林土壤肥力质量评价的最小数据集(Minimum Data Set,MDS),确定了MDS为土壤有机质、速效钾、碱解氮、全钾和有效磷。研究结果可为湘南低山红壤油茶林土壤肥力质量评价因子的选取提供依据,为油茶林土壤肥力的评价及油茶林复垦和施肥提供指导。
