摘要
采用PLSR偏最小二乘法回归结合留一法交叉验证，利用长期定位试验田以及直湖港小流域面上的水稻土土壤样本建立最优模型，研究了不同光谱预处理方式对水稻土全磷可见-近红外高光谱反演精度的影响，探索水稻土全磷光谱反演的可行性；并结合简单相关系数法以及PLSR模型回归系数法分析了水稻土全磷光谱反演的重要波段。结果表明，光谱预处理方法对土壤全磷反演精度的影响不大；基于PLSR建立的水稻土全磷光谱反演模型的校正决定系数达0．85，交叉验证决定系数为0．70，RPD为1．8，有较好的模型精度；440～740nm为土壤全磷光谱反演的重要波段。利用PLSR对水稻土全磷进行光谱预测是可行的。
PLSR regression with cross validation（LOOCV） was used to establish the optimal model using the dataset of a long-term plot experiment in Suzhou and dataset of Zhihugang river watershed aimed to discuss the influence of different pretreatment methods on prediction of total phosphorus（TP） in paddy soil and explor the feasibility of total phosphorus prediction with hyperspectrum. Important spectrum bands for total phosphorus prediction were also explored with correlation analysis and PLSR regression coefficient analysis. Results showed that effect ofpretreatment methods of hyperspectral data on the precision of PLSR model was little. The precision of optimum PLSR model was good with the coefficient of determination （R2） of 0.85 for calibration and 0.70 for validation, and RPD of 1.8. 440-740 nm was important for TP prediction with hyperspectrum. It is feasible to predict soil TP using visible-near infrared reflectance spectroscopy with PLSR regression method.
出处
《土壤》
CAS
CSCD
北大核心
2014年第1期47-53,共7页
Soils
基金
国家自然科学基金项目(40901104
41171235)
中国科学院知识创新工程重要方向项目(KZCX2-YW-QN406)资助
关键词
土壤全磷
可见-近红外光谱
偏最小二乘回归
光谱预处理
敏感波段
水稻土
Soil total phosphorus, Visible-near infrared reflectance spectroscopy, Partial least square regression, Hyperspectrum pretreatment, Sensitive band, Paddy soil
