黄绵土钾含量高光谱估算模型研究  

Hyperspectral Model for Estimation of Soil Potassium Content in Loessal Soil

作　　者：刘秀英[1] 石兆勇[1] 常庆瑞[2] 刘晨洲[1] 黄明[1] 古星 LIU Xiuying;SHI Zhaoyong;CHANG Qingrui;LIU Chenzhou;HUANG Ming;GU xing(College of Agronomy, Henan University of Science and Technology, Luoyang, Henan 471023, China;College of Resources and Environment, Northwest A＆F University, Yangling, Shaanxi 712100, China)

机构地区：[1]河南科技大学农学院,河南洛阳471023 [2]西北农林科技大学资源环境学院,陕西杨凌712100

出　　处：《土壤学报》2018年第2期325-337,共13页Acta Pedologica Sinica

基　　金：国家高技术研究发展计划项目(2013AA102401-2);河南科技大学博士科研启动基金(13480074);大学生研究训练计划项目(2017297)资助~~

摘　　要：为了研究可见/近红外光谱法估算渭北旱塬区黄绵土钾含量的可行性,以陕西省乾县试验田采集的120个土壤样品为研究对象,在室内进行土壤全钾、速效钾含量及反射光谱数据测量的基础上,应用多元线性回归(MLR)和偏最小二乘回归(PLSR)方法建立土壤钾含量的估算模型,并用独立样本进行验证。结果表明,以土壤光谱反射率一阶微分(DSSR)为自变量建立的多元线性回归模型(MLR)能进行土壤全钾含量准确估算。以波段深度一阶微分(DBD)为自变量建立的PLSR模型,验证集的决定系数(R2pre)大于0.90,预测均方根误差(RMSEpre)等于0.054,预测相对分析误差(RPDpre)等于3.310,是估算土壤全钾含量的最优模型;而以DSSR为自变量建立的PLSR模型,RPDpre值为1.619和1.572,是估算土壤速效钾含量的最优模型。本研究表明可见/近红外光谱结合多元线性回归和偏最小二乘回归方法能对渭北旱塬区黄绵土全钾含量进行快速、准确估算,但对速效钾含量仅能进行粗略估算。【Objective】Soil is the important carrier of agricultural production, while the potassium in soil is one of the nutrient elements essential for plant growth, so it is very important to quickly and accurately assess soil potassium content in farmland. Conventional soil potassium content determination methods are expensive and time-consuming. The visible and near infrared reflectance spectroscopy（VIS–NIR）, which possess the advantages of non-destructive and rapid detection, has been a useful tool for quantitative analysis of soils of numerous attributes. The object of this study is to investigate feasibility to use the visible and near infrared reflectance spectroscopy in estimating soil potassium contents in the Weibei Rainfed Highland. 【Method】A total of 120 loessal soil samples were collected from the farmfields in Qian County of Shaanxi Province for analysis of total potassium（TK） and readily available potassium（AK） contents in lab with conventional chemical methods. Reflectance spectroscopic data of the soil samples were acquired with the SVC HR–1024 i spectroradiometer. Three types of pretreatments, including Firstorder differential of soil spectral reflectance（DSSR）, band depth（BD） and First-order differential of band depth（DBD）, were adopted to amplify the weak absorption characteristics, eliminate noises in the system and external disturbances. The continuum-removal method was used to extracted band-depths（BD） of the soil reflectance spectra and based on correlation analysis a model was built up for prediction of TK and AK contents in the loessal soil, using the multiple linear regression（MLR） and partial least square regression（PLSR） methods and validated with independent samples.【Result】Results show that the multiple linear regression model based on DSSR as independent variable could accurately estimate TK contents while the other multivariate linear regression models could not do TK and AK contents so accurately. Comparison shows that the PLSR models we

关 键 词：高光谱 多元线性回归 偏最小二乘回归 黄绵土 全钾 速效钾 

分 类 号：S153[农业科学—土壤学]