黄河三角洲不同盐渍度土壤有机质含量的高光谱预测研究  

Hyperspectral Prediction of Organic Matter in Soils of Different Salinity Levels in the Yellow River Delta

作　　者：侯化刚 王丹阳 马斯琦 潘剑君[1] 李兆富[1] HOU HuaGang;WANG DanYang;MA SiQi;PAN JianJun;LI ZhaoFu(College of Resources and Environment Science,Nanjing Agriculture University,Nanjing 210095)

机构地区：[1]南京农业大学资源与环境科学学院,南京210095

出　　处：《中国农业科学》2023年第10期1905-1919,共15页Scientia Agricultura Sinica

基　　金：山东省重大科技创新工程项目(2019JZZY020614)。

摘　　要：【目的】探究土壤有机质和盐分的光谱响应,分析不同盐分含量对土壤有机质预测模型的影响,建立快速、有效的盐渍土有机质含量高光谱预测模型。【方法】以黄河三角洲地区粉质壤土为研究对象,根据不同盐分含量将土壤样本分为非盐(SA)、轻度(SB)、中度(SC)和重度(SD)4组,分别进行室内高光谱测量;其次采用双因素方差分析法,探究土壤有机质和盐分光谱响应程度;进而对原始光谱(raw spectral reflectance,R)进行一阶微分(first order differential reflectance,FD)、连续统去除(continuous statistical removal,CR)、对数(logarithmic,Log)和多元散射校正(multipication scatter correction,MSC)4种变换;最后分别基于盐渍土的4组样本结合4种变换光谱构建多元线性回归(multiple linear regression,MLR)、偏最小二乘回归(partial least squares regression,PLSR)和支持向量回归(support vector machine,SVR)3种土壤有机质含量高光谱预测模型。【结果】土壤有机质和盐分在400—900 nm范围内光谱响应程度显著且变化规律基本一致,二者的敏感波段存在重叠;通过划分不同盐渍度分组建模能够提高土壤有机质预测精度,且随着盐分含量增加,模型的预测精度下降,FD处理更能突出光谱特征差异,提高有机质含量与光谱反射率的相关性。对比3种模型结果,利用FD处理结合SVR建立土壤有机质预测模型精度最高,最优结果建模集和验证集的决定系数R~2为0.86、0.82,均方根误差RMSE为2.71、2.96 g·kg~(-1),相对分析误差RPD为2.42。【结论】土壤盐分与有机质在可见光波段附近(400—900 nm)的敏感波段存在重叠,通过划分不同盐渍度能够有效提高有机质预测模型精度。【Objective】The aim of this study was to investigate the spectral response of soil organic matter and salt,to analyze the effects of different salt content on soil organic matter prediction models,and to establish a rapid and effective hyperspectral prediction model for organic matter content in saline soils.【Method】In this study,according to different salinity contents for indoor hyperspectral measurements,the soil samples were divided into four groups of non-saline(SA),slightly saline(SB),moderately saline(SC),and heavy saline(SD).Then,ANOVA was used to explore the degree of organic matter and salinity spectral response of soils with different salinity degrees respectively.The raw spectra reflectances(raw spectral reflectance,R)were subjected to first order differential reflectance(first order differential reflectance,FD),continuous statistical removal(continuous statistical removal,CR),logarithmic(logarithmic,Log)and multiple scatter correction(multipication scatter correction,MSC)transformations were applied to the raw spectra reflectance;finally,three soil organic matter prediction models,namely multiple linear regression(multiple linear regression,MLR),partial least squares regression(partial least squares regression,PLSR)and support vector regression(support vector machine,SVR),were constructed based on four sets of samples of saline soils combined with the four transformed spectra,respectively.【Result】Soil organic matter and salinity had significant spectral response in the range of 400-900 nm and the change pattern were the same basically,and the sensitive bands of the two overlap.Modeling by dividing different salinity levels could improve the prediction accuracy of soil organic matter,but the prediction accuracy of the model decreased with the increase of salinity content.FD treatment could better highlight the difference of spectral characteristics and improved the correlation between organic matter content and spectral reflectance.Comparing the results of the three models,the highest accur

关 键 词：盐渍土 有机质 光谱响应 高光谱预测 

分 类 号：S156.41[农业科学—土壤学]