可见-近红外光谱的滨海土壤“除水”盐分估测  

Study on Soil Salinity Estimation Method of“Moisture Resistance”Using Visible-Near Infrared Spectroscopy in Coastal Region

作　　者：杨晗 曹见飞 王召海 吴泉源 YANG Han;CAO Jian-fei;WANG Zhao-hai;WU Quan-yuan(College of Geography and Environment,Shandong Normal University,Ji’nan 250014,China)

机构地区：[1]山东师范大学地理与环境学院,山东济南250014

出　　处：《光谱学与光谱分析》2021年第10期3077-3082,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41371395);山东省重点研发计划项目(2017CXGC0304)资助。

摘　　要：滨海盐碱区土壤盐分的快速、准确监测对土地合理利用和保护具有重要意义。可见光近红外(Vis-NIR)光谱技术已广泛用于土壤属性的高效估测。然而,水分对含盐土壤光谱的干扰导致传统土壤盐分估测模型的精度降低。旨在探究分段直接标准化(PDS)和正交信号校正(OSC)在含水条件下土壤盐分估测中的应用,从而建立面向滨海盐碱区的“除水”Vis-NIR定量模型。为此,将获取的144份黄河三角洲滨海盐碱区表层(0~20 cm)土壤盐分数据划分为建模集(17个样本)和验证集(127个样本)。通过严格加水控制实验,测量10个含水率梯度(0%,1%,5%,10%,15%,20%,25%,30%,40%和50%)的建模集土壤光谱数据,验证集的土壤光谱则是根据生成的1~50随机整数,通过随机加水实验测量获取。采用PDS和OSC与偏最小二乘回归(PLSR)结合的建模策略,构建土壤盐分估测模型,并进行性能验证和比较。结果表明,OSC比PDS更能有效减轻水分在土壤盐分估测中的建模干扰。具体来说,光谱校正前后生成的所有PLSR模型均取得一定的成功(R_(P)^(2)=0.79~0.91,RMSE_(P)=2.6~3.98 g·kg^(-1),RPD=1.98~2.37)。OSC-PLSR模型的土壤盐分估测精度提高,R_(P)^(2),RMSE_(P)和RPD分别为0.91和2.6 g·kg^(-1)和2.37。而PDS-PLSR模型效果不理想,R_(P)^(2),RMSE P和RPD分别为0.79,3.98 g·kg^(-1)和1.98。模型整体表现出了OSC-PLSR>PLSR>PDS-PLSR的土壤盐分估测性能。此外,提出了变量投影重要性(VIP)和Spearman相关系数(r)结合的分析策略,进一步探究了模型的估测机理。模型的重要波长(VIP>1)与土壤盐分敏感波长(|r|>0.4)吻合,对估测模型有重要意义。比较而言,OSC-PLSR精确提炼了位于830,1940和2050 nm附近的模型估测的关键波长,而常规的PLSR和PDS-PLSR包含了大量的冗余信息。综合来看,OSC-PLSR模型在Vis-NIR土壤盐分估测中具有较好的除水效果,为土壤含水状态下的土壤盐分研究提供可靠方法。Rapid and accurate monitoring of soil salinity in the coastal regions are of great significance to the rational use and protection of land.Visible-near infrared spectroscopy has been widely used for the efficient estimation of soil properties.However,the interference of soil moisture on the spectrum decreases the estimation accuracy of traditional soil salinity estimation models.This paper aimed to explore the capacity of piecewise direct standardization(PDS)and orthogonal signal correction(OSC)in estimating the soil salt content under the condition of moisture interferedand establishing“moisture resistance”Vis-NIR models in the coastal saline regions.To this end,114 soil samples were collected from the Yellow River Delta(0~20 cm)and divided the data into a modeling dataset(17 samples)and a validation dataset(127 samples).A control rewetting process obtained the soil spectral of the modeling dataset with 10 moisture content levels(0%,1%,5%,10%,15%,20%,25%,30%,40%and 50%).The soil spectral of the validation dataset was measured after a fully randomized trial,according to the generated 1~50 random integer.The modeling strategy combining PDS and OSC with partial least squares regression(PLSR)was proposed to build soil salinity estimation models.These models were validated and compared.Results showed that OSC was more effective than PDS in reducing modeling interference of moisture content in soil salinity estimation.Specifically,all of the PLSR models generated before and after spectral correction have achieved a certain level of success in soil salinity estimation(R_(P)^(2)=0.79~0.91,RMSE_(P)=2.6~3.98 g·kg^(-1),RPD=1.98~2.37).Compared with PLSR(R_(P)^(2)=0.86,RMSE_(P)=3.02 g·kg^(-1),RPD=2.21),OSC-PLSR could effectively improve the soil salinity estimation accuracy with R_(P)^(2)=0.91,RMSE_(P)=2.6 g·kg^(-1),RPD=2.37,respectively.However,the PDS-PLSR model was not effective with R_(P)^(2)=0.79,RMSE P=3.98 g·kg^(-1)and RPD=1.98,respectively.The representation order of the model was OSC-PLSR>PLSR>PDS-PLSR.Furth

关 键 词：Vis-NIR光谱 土壤盐分 水分校正 正交信号校正 滨海盐碱区 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]