Regression-Kriging of Soil Organic Matter Using the Environmental Variables Derived from MODIS and DEM  

基于MODIS和DEM的土壤有机质空间预测研究(英文)

作　　者：王彬武[1] 周卫军[1] 马苏[2] 刘少坤[1] 于良艺[1] 郑超[1] 王金国[1] 

机构地区：[1]湖南农业大学资源环境学院,湖南长沙410128 [2]湖南师范大学GIS研究中心,湖南长沙410081

出　　处：《Agricultural Science & Technology》2012年第4期838-842,共5页农业科学与技术（英文版）

基　　金：Supported by National Natural Science Foundation of China(41071204);Hunan Provincial Innovation Foundation for Postgraduate(CX2011B310)~~

摘　　要：[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.[目的]本研究以湖南省石门县为例,采用普通克里格和基于MODIS和DEM数据的回归克里格方法,结合有限个采样数据对该区有机质进行空间预测,并进行对比分析。[方法]运用由地形参数(由DEM派生得到)、归一化植被指数(NDVI)以及由MODIS派生得到的地表温度(LST)等指标进行空间模拟,然后通过平均误差(ME)和均方根误差(RMSE)验证精度,数据的描述性统计及转换均通过软件实现。[结果]结果表明在有限个采样数据下,结合多元遥感数据的回归克里格方法优于普通克里格法,回归克里格法的平均误差和均方根误差均低于普通克里格法,相对提高值为6.03%。[结论]在低山丘陵区,运用MODIS数据及其他遥感数据对土壤有机质进行空间预测具有较好的效果。

关 键 词：Regression-kriging MODIS Soil organic matter Spatial prediction 

分 类 号：S153.62[农业科学—土壤学]