不同颗粒大小对高光谱估算土壤有机质含量的影响  

Influence of Soil Particle Size on the Estimate of Soil Organic Matter by Hyperspectral Spectroscopy

作　　者：司海青[1] 姚艳敏[1] 王德营[1] 刘影[1] 

机构地区：[1]中国农业科学院农业资源与农业区划研究所/农业部农业信息技术重点实验室,北京100081

出　　处：《中国农学通报》2015年第18期173-178,共6页Chinese Agricultural Science Bulletin

基　　金：全球变化研究国家重大科学研究计划(973计划)子课题"气候变化对我国粮食产区农田土壤肥力影响机理研究"(2010CB951501-2);高分辨率对地观测系统国家科技重大专项(09-Y30B03-9001-13/15)

摘　　要：研究旨在对不同颗粒大小的土样进行土壤有机质含量高光谱估算建模,以期得到土样制备时合适的土样颗粒大小,减少不必要的工作量。笔者对过10、20、60、100目筛的土样进行高光谱数据测量,并对光谱数据进行反射率(R)、反射率一阶导数(R’)和反射率倒数对数[Log(1/R)]3种光谱数据变换,然后运用偏最小二乘回归法(PLSR)、支持向量机法(SVM)和PLSR-SVM相结合的方法建立土壤有机质含量估算模型。研究结果表明:土壤颗粒大小对土壤光谱反射率有明显影响,颗粒越小,土壤光谱反射率越高;运用PLSR-SVM建立的SOM估算模型比只利用PLSR或SVM建立的模型精度高;当土壤颗粒大小<0.25 mm时,对于SOM光谱估算模型精度的提高没有太大的帮助。该试验为进行土壤有机质含量高光谱估算制备土样提供指导。In order to study the influence of soil particle size on the estimate of soil organic matter （SOM） content by hyperspectral spectroscopy and obtain the appropriate soil particle size and reduce the workload, soil samples were sieved by 10, 20, 60 and 100 meshes, then the author scanned soil samples with hyperspectral data. The soil hyperspectral data were mathematically transformed into spectral reflectance （R）, first derivatives of reflectance （R ＂） and the logarithm of the inverse of the reflectance [Log （l/R）]. Partial Least Squares Regression （PLSR）, Support Vector Machine （SVM） and PLSR-SVM were used to build SOM content estimate model. The results showed that soil particle size had an obvious effect on the soil spectral reflectance, soil spectral reflectance increased with the decrease of soil particle size. The precision of SOM estimate model built by PLSR-SVM was better than the model built by PLSR and SVM. When the soil particle size was less than 0.25 mm, it was not helpful to improving the precision of this model. The results could provide guidance for soil samples ＇ pretreatment when using hyperspectral spectroscopy to predict SOM content.

关 键 词：土壤有机质 高光谱 土壤颗粒 偏最小二乘回归 支持向量机 

分 类 号：S-3[农业科学]