基于土壤变异解释力的几种土壤制图方法的对比研究——以南阳市1m土体土壤有机碳密度制图为例  

Comparison between Soil Mapping Approaches Based on Their Ability Explaining Soil Variability——A Case of Mapping Soil Organic Carbon Density of Soil (0~1m) in Nanyang District

作　　者：赵彦锋[1] 李豪杰 陈杰[1] 孙志英[2] 梁思源[3] 

机构地区：[1]郑州大学水利与环境学院地理信息科学系,郑州450001 [2]河南省国土资源调查规划院,郑州450016 [3]郑州大学公共管理学院,郑州450001

出　　处：《土壤学报》2018年第1期43-53,共11页Acta Pedologica Sinica

基　　金：国家自然科学基金项目(40801080;41601210;40971128);科技部科技支撑计划项目(2012BAD05B02-7)资助~~

摘　　要：为克服方法的复杂性和数据的详细性解释土壤制图效果的不足,基于土壤变异解释力对多种方法进行对比研究。收集南阳1∶5万土壤类型图、30 m分辨率数字高程模型和TM影像,计算出高程、坡度、坡向、归一化植被指数(NDVI)、穗帽变换的湿度(TCW)参数等,以439个土壤剖面为训练数据,分别按土壤类型连接法(SCLM)、加权最小二乘法(WLS)回归、地理权重(GWR)回归、随机森林(RF)、普通克里格(OK)、回归克里格(RK)进行1m土体土壤有机碳密度(SOCD)制图,其余49个土壤剖面作为验证集。结果表明:(1)对SOCD变异的解释力是影响制图效果的本质因素。土壤类型、土壤表层有机质(OM)是主要预测变量,SCLM、WLS和GWR均只能利用其中一种主要变量,土壤图的详细化和回归模型的复杂化均不能明显改善SOCD制图效果。基于土属和OM变量,RF对SOCD变异的解释力最强,预测效果最优;地统计学空间变异函数对SOCD变异的解释力大于回归模型,小于RF,而与土壤类型相当,其相对制图效果亦如此。(2)预测变量建模和空间相关是两类不同的土壤变异解释机制,RK未必能使它们产生最佳组合:只有WLS回归、GWR回归和缺乏土壤类型信息的RF(OM+TCW)适合RK算法,在原始模型中它们对训练数据的拟合效果依次升高,但其RK结果的优劣排序则相反;所有RK的结果均未达到土属和OM参与下RF制图的精度。【Objective】Before the digital soil mapping technology emerged,the soil category linkage method（SCLM）,linking means or median values of properties of the soils of the same soil category with their corresponding polygons in the soil map,or linking soil properties with polygons based on pedological expertise（including type of the soil and its location）,was the major method used in mapping of soil organic carbon density（SOCD）. Even nowadays,it is still of quite high practical value,because it is quite hard to build up a DSM model for relationships of external environmental covariates with SOCD in deep soil layers and/or on large scale,e.g. Provincial,continental and global. To understand in-depth relative efficiency of the two linking methods,it is necessary to perform some comparative studies. In terms of the DSM technology,most of the comparative studies have come to the conclusions that sophisticated machine learning models are superior to simple ones and that mixed models（regression Kriging）are of high superiority in most cases. However,there are a few papers reported some contradictory results. All the conclusions suggest that SOCD mapping quality could not be explained merely by method and also affected by the effectiveness and accuracy of the parameters used in the method. To elaborate in-depth the contradictory conclusions and to analyze the essence of the problems,in this paper a comparison was performed of SCLM with weighted least squares regression（WLS）,geographically weighted regression（GWR）,random forest（RF）,ordinary kriging（OK）and regression kriging（RK）in SOCD mapping,and establishment of relationships between abilities of the methods to explain SOCD variability and effects of their mapping was discussed. 【Method】A tract of land,26 600 km2 in area,in Nanyang of Henan Province,was selected as a study area,of which soil categories,elevation,slope,aspect,and normalized difference vegetation index（NDVI）,and wetness（TCW）of tasseled cap transformation（TC�

关 键 词：土壤有机碳密度 数字土壤制图 土壤类型连接法 随机森林 方法对比 

分 类 号：S159.9[农业科学—土壤学] P934[农业科学—农业基础科学]