摘要
为改善高光谱技术对荒漠土壤有机质的估测效果,该文采集了以色列Seder Boker地区的荒漠土壤,经预处理、理化分析后将土样分为砂质土和黏壤土2类,再通过光谱采集、处理得到6种光谱指标:反射率(reflectivity,REF)、倒数之对数变换(inverse-log reflectance,LR)、去包络线处理(continuum removal,CR)、标准正态变量变换(standard normal variable reflectance,SNV)、一阶微分变换(first order differential reflectance,FDR)和二阶微分变换(second order differential reflectance,SDR)。通过灰度关联(gray correlation,GC)法确定SNV、FDR、SDR为敏感光谱指标,采用偏最小二乘回归(partial least squares regression,PLSR)法和岭回归(ridge regression,RR)法,构建基于敏感光谱指标的土壤有机质高光谱反演模型,并对模型精度进行比较。结果表明:砂质土有机质含量的反演效果要优于黏壤土;基于SNV指标建立的模型决定系数R^2和相对分析误差RPD均为最高、均方根误差RMSE最低,所以SNV是土壤有机质的最佳光谱反演指标;对SNV-PLSR模型和SNV-RR模型综合比较得出,SNV-RR模型仅用全谱4%左右的波段建模,实现了更为理想的反演效果:其中,对砂质土有机质的预测能力极强(R_p^2为0.866,RMSE为0.610 g/kg、RPD为2.72),对黏壤土有机质的预测能力很好(Rp2为0.863,RMSE为0.898 g/kg、RPD为2.37)。荒漠土壤有机质GC-SNV-RR反演模型的建立为高光谱模型的优化、土壤有机质的快速测定提供了一种新的途径。
Organic matter content in soil is one of the most significant indicators evaluating the soil fertility, and its dynamic monitoring is good for further development of accurate agriculture. In recent years, obtaining Vis-NIR（visible-near infrared） continuous spectrum data of soil through hyperspectral technique and realizing accurate inversion prediction according to organic matter spectrum reflection characteristics have become a hot topic in current remote sensing field. However, in the hyperspectral inversion process of desert soil organic matter, there exists the problem of ＂low organic matter content, weak spectrum response and low model precision＂. The research collected different soil samples in Seder Boker region, south of Israel, divided the experimental soil samples into sandy soil and clay loam after particle size analysis in the lab, and applied potassium dichromate external heating method to measure the organic matter content in the soil. The raw hyperspectral reflectance of soil samples was measured by the ASD Field Spec 3 instrument. After data preprocessing and different mathematical manipulation, 6 spectral indicators were obtained, i.e. reflectivity（REF）, inverse-log reflectance（LR）, continuum removal reflectance（CR）, standard normal variable reflectance（SNV）, first-order differential reflectance（FDR） and second-order differential reflectance（SDR）. Then, gray correlation degree（GCD） between different spectral indicators and organic matter content was calculated, and SNV, FDR and SDR through gray correlation（GC） test（GCD0.90） were chosen as the sensitive spectral indicators. Moreover, hyperspectral inversion model of soil organic matter was built based on sensitive spectral indicator using partial least squares regression（PLSR） method and ridge regression（RR） method, and the precision of inversion result was verified and compared. And then, the performances of these models were evaluated by the determination coefficient for calibration set（Rc2�
作者
王海峰
张智韬
Arnon Karnieli
陈俊英
韩文霆
Wang Haifeng;Zhang Zhitao;Arnon Karnieli;Chen Junying;Han Wenting(College of Water Resources and Architectural Engineering,Key Laboratory of Agricultural Soil and Water Engineering in the Ministry of Education,Northwest A＆F University,Yangling 712100,China;Institute of Water Saving Agriculture in Arid Areas of China,Northwest A＆F University,Yangling 712100,China;Jocob Blaustein Institute for Desert Research,Ben-Gurion University of the Negev,Sede Boker 84990,Israel)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第14期124-131,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2017YFC0403302
2016YFD0200700)
杨凌示范区科技计划项目(2016NY-26)
关键词
遥感
模型
有机质
荒漠土壤
高光谱
灰度关联
岭回归
remote sensing
models
organic matter
desert soil
hyperspectral
gray correlation
ridge regression
