摘要
有机质含量是表征土壤肥力质量的重要属性,其空间分布模式对于施肥等耕作管理措施的推荐具有重要的指导意义。本文以我国黑土区黑龙江省海伦市为研究区域,在土壤采样点数量较有限的情况下,分别采用普通克里格、反距离权重、遥感反演和基于土壤学专业知识四种方法对耕层土壤有机质含量进行了空间预测。结果表明:四种方法表征的海伦耕地土壤有机质含量空间分布特征具有相似性,即由东北向西南方向递减。空间预测精度从高到低依次为反距离权重、普通克里格、基于土壤学专业知识和遥感反演法;而在有机质的局部变异细节表达方面,从高到低为遥感反演、基于土壤学专业知识、反距离权重和普通克里格法。四种方法中仅遥感反演法预测结果的极差范围较宽,普通克里格法则存在明显的平滑效应,而综合比较结果则表明,最合适的方法是基于土壤学专业知识的方法。
Soil organic matter（SOM） is an extremely important soil property for assessing soil quality,and the spatial distribution patterns of SOM are critical for guiding cultivation,fertilization,and other management practices.Seventy two topsoil samples collected from Hailun City of Heilongjiang Province and four spatial predicting methods,ordinary kriging（OK）,inverse distance weighted（IDW）,retrieving of remote sensing data（RRS）,and Pedological professionnal knowledge based（PKB） methods were applied in this study to predict the spatial distribution of SOM content in cropland topsoil of Hailun.The results showed that the global distribution patterns of SOM derived from the four methods are similar;SOM contents in northeastern area of the city are higher that those located in southwestern areas.The mean absolute percentage error（MAPE） and root mean squared residual（RMSR） results suggested that the IDW method had the highest predicting performance,while the RRS method was lowest.Moreover,the range parameters obtained by different predicting methods indicated that the RSS method had best efficiency for representing SOM local details whereas the OK method had strong smoothing effect,suggesting that the PKB method is most suitable for mapping SOM spatial distributions when only limited soil data are available for use.
出处
《土壤通报》
CAS
CSCD
北大核心
2012年第3期662-667,共6页
Chinese Journal of Soil Science
基金
国家自然科学基金科学部主任基金(41040009)
中国科学院知识创新工程重要方向项目(KZCX2-EW-QN404)
973项目全球变化研究国家重大科学研究计划(2010CB950702)资助
关键词
海伦市
有机质
普通克里格
反距离权重
遥感反演
基于土壤学专业知识
Hailun
Soil organic matter（SOM）
Ordinary kriging（OK）
Inverse distance weighted（IDW）
Retrieving from remote sensing data（RRS）
Pedological professional knowledge based method（PKB）
