引用本文:刘秀英,石兆勇,常庆瑞,刘晨洲,黄 明,古 星.黄绵土钾含量高光谱估算模型研究[J].土壤学报,2018,55(2):325-337.
LIU Xiuying,SHI Zhaoyong,CHANG Qingrui,LIU Chenzhou,HUANG Ming,GU Xing.Hyperspectral Model for Estimation of Soil Potassium Content in Loessal Soil[J].Acta Pedologica Sinica,2018,55(2):325-337
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黄绵土钾含量高光谱估算模型研究
刘秀英1, 石兆勇1, 常庆瑞2, 刘晨洲1, 黄 明1, 古 星1
1.河南科技大学农学院;2.西北农林科技大学资源环境学院
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。
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
DOI:10.11766/trxb201706040137
分类号:
基金项目: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)和大学生研究训练计划项目(2017297)
Hyperspectral Model for Estimation of Soil Potassium Content in Loessal Soil
LIU Xiuying1, SHI Zhaoyong1, CHANG Qingrui2, LIU Chenzhou1, HUANG Ming1, GU Xing1
1.College of Agronomy, Henan University of Science and Technology;2.Henan University of Science and Technology
Abstract:
【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–1024i spectroradiometer. Three types of pretreatments, including First-order 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 squares 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 were generally higher than the MLR models in prediction accuracy. The models built up with the PLSR method incorporating the 4 spectral variables, could estimate TK content accurately, specially the PLSR model based on DBD as independent variable, of which coefficient of determination (R2pre) of the validation set was > 0.90, root mean square error of the prediction (RMSEpre) was 0.054, and residual predictive deviation (RPDpre) was 3.310. The PLSR model based on BD followed. However, the PLSR models based on the 4 spectral variables were relatively low in calibration and validation accuracy and only some models could roughly predict AK contents. The PLSR model based on DSSR as independent variable, with RPDprebeing 1.619 and 1.572 was the best for accurate prediction of soil AK contents. 【Conclusion】All the finding in this study demonstrate that the visible and near infrared spectroscopy coupled with MLR and PLSR can be used to predict rapidly and accurately TK content in the loessal soil of the Weibei Rainfed Highland, but only roughly AK contents therein.
Key words:  Hyperspectrum  Multiple linear regression  Partial least square regression  Loess  Total potassium  Readily available potassium