基于多元地统计的土壤有机质含量空间格局反演

为了提高土壤有机质含量的空间预测精度,该文采用了一种多元地统计方法来构建遥感定量反演模型。考虑到回归误差在空间上具有一定程度的聚类,该文提出了基于局部变化均值的普通克里金方法,然后用其构建土壤有机质含量遥感定量反演模型。对四川省西南部土壤有机质含量进行空间预测试验,并与普通克里金、普通遥感定量反演、基于回归克里金的遥感定量反演等方法相比较。结果表明:该文提出方法的空间预测结果最优,其原因为该方法通过空间统计来建立采样数据与地表反射率间的联系,充分考虑了数据间的空间相关性,因此可以更精确地获得土壤有机质含量的遥感反演模型;相比基于回归克里金的遥感定量反演方法,基于局部变化均值的普通克里金假设回归误差在局部邻域内的均值也不一定为零,更符合实际情况。该方法为农田养分管理及区域农业的可持续发展提供科学依据。

Inversion for spatial distribution of soil organic matter content based on multivariate geostatistics

The classical statistical method is always used to construct quantitative remote sensing retrieval model. However, the method doesn't take into account the spatial relations between data, which will severely affect the retrieval accuracy. In order to improve the spatial predictive accuracy of soil organic matter, a multivariate geospatial method for making retrieval model was presented in this paper. Considering the spatial distribution characteristic of regression error, a multivariate geostatistical method called ordinary Kriging with varying local means (OKLM) was presented, which was used to construct remote sensing retrieval model. The method was illustrated using soil organic matter (SOM) content in Southwest Sichuan province, and was compared with other method, such as ordinary Kriging, ordinary remote sensing retrieval method, and remote sensing retrieval model based on regression Kriging. The results showedthe proposed method improved the predictive accuracy effectively among these methods, because the proposed method was based on relations between SOM sampling data and TM images using spatial statistics, taking fully into account the spatial relations among the data, and obtained more accurate retrieval model. Compared with regression Kriging, OKLM assumed that the means of regression errors cannot always be zero in local neighborhood, which was more in line with the actual situation. The proposed method provides a scientific basis for the farmland nutrient management and sustainable development of the regional agricultural.