【摘要】 通过两种不同的尺度进行了土壤有机质含量的预测,在全县范围(大尺度)内运用地统计方法进行最优无偏内插估计,得到全县土壤有机质含量的空间分布格局。在小尺度高光谱Hyperion影像范围内,确定623.6 nm处反射率倒数之对数的一阶微分与564.4nm处反射率倒数之对数的一阶微分的比值为土壤有机质的敏感变量,运用多元统计分析方法,确立各土壤有机质高光谱定量最佳反演模型,并把该模型应用于高光谱影像进行有机质含量定量填图,取得了很好的预测效果(R2=0.8684)。同时为了进行客观比较,基于同一尺度,利用30个样点进行地统计空间插值定量预测,比较两种预测结果,通过分析得出由于地统计学受到样点的数目、分布和间距情况以及内蕴假设的影响,其预测效果不如高光谱遥感反演模型。更多还原

【Abstract】 Soil organic matter(SOM)content was predicted at two different scales.At a large scale,the geo-statistical method was applied to interpolate the spatial distribution of SOM throughout Hengshan County of China.Additionally, at a small scale in Hyperion image,by analyzing the correlation between spectrally reflective data and SOM concentrate, the ratio of the reflectivity reciprocal-logarithm’s first derivative of 623.6 nm against the reflectivity reciprocal-logarithm’s first derivative of 564.4 nm was selected as the sensitive regression variable,and the best multivariate retrieval model was developed.Then the retrieval model was utilized to the hyper-spectral data for SOM quantitative mapping,and the adjusted R square coefficient of 0.8684 revealed a precise result.For objective comparison, 30 soil samples were used for spatial interpolation in geo-statistical way at the same scale in Hyperion imagery.After comparing and analyzing the two methods,it indicates that the predicted result of geo-statistics is not so good as that by hyper-pectral retrieval way due to the influences of sample quantities,sample distributions,sample intervals together with the inner-inclusion hypothesis.更多还原