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Position: Home > Articles > Missing Data Imputation Approach for Soil Database Based on Pedotransfer Functions Soils 2019,51 (5) 1036-1041

基于传递函数的土壤数据库缺失数据的填补研究

作  者：
韩光中;杨银华;吴彬;李山泉
单  位：
邢台学院资源与环境学院;内江市东兴区气象局;内江师范学院地理与资源科学学院
关键词：
土壤数据库;数据缺失;传递函数;数据填补
摘  要：
数据缺失在土壤调查研究中是一个非常普遍的现象,处理不当一定程度上会影响研究结果的可靠性.土壤转换函数(pedotransfer functions,PTFs)是简单、快速、大批量填补土壤数据库缺失信息的有效手段.但目前分析和厘定我国土壤数据库缺失数据特征的研究较少,针对土壤数据库缺失数据的填补方法也亟待规范.本文对我国第二次土壤普查数据库进行分析,探讨该数据库的数据缺失特征,并对数据缺失严重的土壤属性进行预测,以期为今后的土壤数据库缺失数据填补工作提供参考.总体来看,质地(砂粒、粉粒和黏粒含量)、pH、有机质、全氮、全磷、全钾是土壤普查中最基础的调查项目,这些土壤属性信息的完整性最好.有效磷、速效钾和阳离子交换量数据有一定的缺失.碱解氮、容重、砾石含量、各种类型氧化铁数据缺失严重.在填补缺失数据时,建议首先考虑模型的稳定性,尽量使用那些相对稳定且数据完整性好的土壤属性来预测缺失数据.我国第二次土壤普查数据库基本都缺少空间属性信息,在填补缺失数据时最好采用简单而相对稳定的回归模型.利用回归分析得到的土壤传递函数可以较好地实现容重、碱解氮和部分阳离子交换量缺失数据的填补工作.尽管如此,由于部分土壤属性信息有一定的时效性,应用传递函数时要注意数据源的历史背景.
译  名：
Missing Data Imputation Approach for Soil Database Based on Pedotransfer Functions
作  者：
HAN Guangzhong;YANG Yinhua;WU Bin;LI Shanquan;College of Geography and Resources Science, Neijiang Normal University;Dongxing Meteorological Bureau;College of Resources and Environment,Xingtai University;
关键词：
Soil database;;Data missing;;Pedotransfer functions;;Data imputation
摘  要：
Data missing is a common problem in soil survey and related researches. When this problem proposed, the common solution in most studies was to neglect it or remove records that have missing data due to the lack of the understanding of the importance of data missing. Obviously, this solution could not to satisfy the needs of practical studies. The application of pedotransfer functions(PTFs) provides a broad prospect for the interpolation of missing data of soil database in a simple, rapid and batch processing way. At present, few studies were carried to analyze or interpolate the missing data of soil database in China. More importantly, the method to interpolate the missing data of soil database needs to be standardized. In this study, the characteristics of missing data in Chinese Soil Database from the Second National Soil Survey were analyzed, and the interpolations of serious missing date of soil properties were tried in order to provide knowledge for future researches. Results showed, the data of soil texture(sand, silt and clay contents), pH, organic matter, total nitrogen, total phosphorus and total potassium were most complete for they are the basic survey factors in soil survey. The data of available phosphorus, available potassium and cation exchange capacity had a certain miss. The data of alkaline nitrogen, bulk density and iron oxides were missed seriously. Considering that the stability of prediction model is essential, so soil properties with complete data would be used with top priority in the interpolation of data missing. The existing Chinese Soil Database is in short of spatial attribution data, so it is better to use regression model than use spatial interpolation in the interpolation of missing data of soil database. In this study, PTFs from regression analysis could meet the requirement of data interpolation of bulk density, alkaline nitrogen and partial cation exchange capacity. Besides, some soil properties such as available potassium could be time limited, so the historical background of data sources should be considered in the application of PTFs.

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