摘要
为了对比不同方法建模效果的差异,筛选潮土有机质高光谱最佳预测模型,该研究采集国家潮土土壤肥力与肥料效益长期监测站不同施肥处理耕层土样83份,采用25种光谱预处理方法(15种单一预处理方法,10种预处理方法相加算法)结合3种建模方法(多元线性回归、偏最小二乘回归、支持向量机回归),构建不同的潮土有机质高光谱预测模型。对比模型预测结果表明,最佳光谱建模方法是偏最小二乘回归法,该方法结合多种预处理方法均获得了较高的模型预测精度和可靠性,25个检验模型的平均决定系数、均方根误差值RMSEv和相对分析误差RPD值分别为0.913、1.264 g/kg和3.299。使用预处理方法相加算法能更好地提升模型精度,相比使用单一预处理方法,3种建模方法的检验模型平均决定系数分别提高了0.049、0.033和0.071,RMSEv分别降低了0.318、0.204和0.528 g/kg,RPD值分别提高了0.530、0.307和1.144。先用多元散射校正法再进行5个平滑点数的一阶导数预处理在3种建模方法中表现均较好(平均决定系数=0.934,平均RMSEv=1.17 g/kg,平均RPD=3.59),可作为潮土有机质预测模型的通用预处理方法。偏最小二乘回归模型结合最大值标准化预处理所建模型(决定系数=0.948,RMSEv=0.972 g/kg,RPD=4.276)精度高、可靠性强,且建模过程数据运算更为简便,是筛选出的最佳潮土有机质高光谱预测模型。该研究结果对潮土有机质高光谱预测建模有一定的指导作用,并为筛选最佳高光谱预测模型提供技术参考。
With the continuous development and wide application of multivariate statistical analysis methods, more and more spectral pre-processing and modeling methods are used to analyze the spectral data in order to establish high-precision hyperspectral prediction models. This study selected soil samples from National Long-term （more than 20 years） Monitoring Station of Fluvo-aquic Soil Fertility and Fertilizer Effects. The soil can represent Huang-Huai-Hai Fluvo-aquic soil type and fertilization models. A total of 83 soil samples were collected from the depth of 0-20 cm from treatments with different ratio of fertilization. Reflectance measurements from 350 nm to 2 500 nm were obtained using FieldSpec 3 Hi Spectroradiometer （Analytical Spectral Devices Inc.） in laboratory after soils were air-dried and sieved （0.18 mm）. Twenty five pre-processing methods including 15 single pre-processing methods （standard normal variate transformation、normalization、multiple scatter correction、derivative method with different smoothing points and operational parameters） and 10 pre-processing methods adding operations of spectral data and three multivariate techniques （stepwise multiple linear regression, SMLR,partial least-squares regression, PLSR,support vector machine regression, SVMR） were compared with the aim of identifying the best combination to predict fluvo-aquic soil organic matter content. The coefficient of determination、the root mean square error （RMSEv） and relative prediction deviation （RPD） of validation set were used to evaluate the models. The result showed that the best multivariate technique was PLSR, which associated with variety pre-processing methods could resulted in high accuracy and reliability of models. The averaged coefficient of determination、RMSEv and RPD of 25 prediction methods were 0.913、1.264 g/kg and 3.299 respectively. The optimal pre-processing method varied with the multivariate technique used. Compared with the single pre-processing methods, pre-processing methods add operations were better for data preparation among the 3 multivariate techniques, of which average coefficient of determination was higher 0.049、0.033and 0.071 than the single ones, respectively, and the average RPD was higher 0.530、0.307 and 1.144 than the single ones, respectively, but the average RMSEv lower 0.318、0.204 and 0.528 g/kg than the single ones , respectively. The optimal pre-processing method was multiple scatter correction added Savitzky-Golay 1st derivative with a search window of 5 measurements（MSC-SGF5-2） since it performed best among the 3 multivariate techniques, with the average coefficient of determination=0.934, RMSEv=1.17 g/kg and RPD=3.59. This pre-processing method probably can be used as a common spectral data preparation method for fluvo-aquic soil organic matter content prediction model. Among the tested models,the best prediction model for fluvo-aquic soil organic matter was PLSR multivariate techniques associated with normalization by the maximum value pre-processing method （coefficient of determination=0.948, RMSEv=0.972 g/kg, RPD=4.276）, and it has high accuracy, reliability and was easy to operate.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2014年第21期192-200,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点基础研究发展计划(863)项目(2013CB127400)
公益性行业(农业)科研专项(201203030)
河南科技创新项目(201315605)
关键词
光谱分析
土壤
模型
潮土有机质
对比分析
spectrum analysis
soils
models
fluvo-aquic soil organic matter
comparison
