基于案例推理的土壤光谱识别和分类研究

The recognition of soil types automatically has important academic value and application significance in soil science, land use planning, agricultural industry and environment assessment. The spectral reflectance characteristic of soil is the comprehensive representation of soil properties, and has great potential in the soil type recognition quickly. The soil series survey in China has implemented for several years, and detailed knowledge and experience of soil identification and classification according to the Chinese Soil Taxonomic Classification has obtained in this survey. The purpose of this project is to identify soil types using soil spectral reflectance based on case-based reasoning (CBR). The first objective of this project is the development of soil spectral database which will cover most soil types in China by using the archived soil samples from soil series survey. The second is based on the relation between soil development, composition and spectral reflectance characteristics of soil types at different classification levels, to find representative cases of the spectral identification of soil types, and build the case library of the spectral identification of soil types. The third is to find the identification methods of soil type using similarity computing based on spectral matching and spectral index, and then identify soil types using cased-based reasoning with the case library. Finally, the method is applied on the soils sampled from two test area, Nan jing-Zhen jiang Hilly regions in Jiangsu province and Low hilly regions in Jiangxi province , in order to assess if can acceptably be used. This project develop a new method for soil type identification, based on representative cases of the spectral identification of soil types which implicit soil classification knowledge and experience, to identify soil types quickly and correctly without the participation of soil classification experts.

土壤类型的自动识别具有重要的学术价值和应用意义。土壤的反射光谱特性是土壤理化性状信息的综合反映，在土壤类型的快速识别上有很大的应用潜力。我国土系调查项目积累了丰富的土系样本和土壤系统分类知识及经验。本项目拟利用我国土系调查样本，建设覆盖我国最多土壤类型的光谱数据库；分析不同层级土壤分类类型的发生发育-物质组成-光谱反射率特性之间的关系，获得土壤类型的代表性光谱识别案例，建立不同层级土壤类型的光谱识别案例库；研究基于光谱匹配的土壤类型识别和基于光谱特征指标的土壤类型识别方法，运用案例推理，实现基于光谱的土壤自动识别和分类；并选择宁镇丘陵区和赣东北低丘岗地区作为试验区，验证基于案例推理的土壤光谱识别和分类的有效性。本项目将案例推理运用到土壤类型的光谱识别中，使土壤分类知识和经验隐含在土壤光谱识别案例中，从而在缺少土壤分类专家参与的情况下, 实现快速、较为客观的土壤分类结果。

土壤的反射光谱特性是土壤理化性状信息的综合反映，在土壤剖面类型的快速自动识别上具有较大应用潜力。本项目利用15个省份（地区）土系调查存档土样，测量其可见光-近红外漫反射光谱，建设了包含1509个剖面(6070个土样)的土壤光谱数据库。收集并整合土壤剖面的地理位置、分类信息、生境信息、性态信息、理化信息和光谱信息等多元属性，形成了面向土纲、亚纲、土类、亚类四个层级的土壤光谱识别案例库，该库覆盖了中国土壤系统分类体系中的12个土纲、31个亚纲、87个土类、213个亚类。研究基于光谱曲线匹配的土壤剖面类型识别方法和基于光谱特征匹配的土壤剖面类型识别方法，形成多个土壤类型光谱匹配识别算法，并研发了基于光谱的土壤剖面识别系统。