【摘要】 【目的 】剔除土壤高光谱中包含的大量冗余和无效信息，探明土壤有效磷（SAP）的敏感波段，简化SAP的高光谱估算模型并提高模型的预测精度。【方法 】文章以四川省崇州市西河流域110个土壤样本为研究对象，利用ASD Fieldspec3地物光谱仪在室内条件下测定350～2 500 nm波段范围的土壤高光谱数据。对光谱数据进行预处理后，采用连续投影算法（SPA）和竞争性自适应重加权算法（CARS）优选的波长变量作为建模参数，运用偏最小二乘回归（PLSR）方法建立模型并比较其精度。【结果 】结果表明，标准正态变换预处理方法是SAP的最佳土壤光谱数据预处理方法。基于标准正态变换后的光谱数据，CARS、SPA算法可将预测SAP的关键波段变量分别压缩至54和13个，CARS-PLSR模型与SPAPLSR模型相比，相关系数由0.894提高到0.945，均方根误差由5.73降低到3.56。【结论 】土壤高光谱数据经标准正态变换后，采用CARS-PLSR算法可有效提高有效磷含量预测的鲁棒性。该结果可为高光谱数据快速反演土壤有效磷含量提供理论依据。更多还原

【Abstract】 [ Purpose ] To remove a large amount of redundancy and invalid information in soil hyperspectrum,select the sensitive wavelengths of soil available phosphorus(SAP),simplify the SAP hyperspectral estimation model and improve the prediction accuracy of the models. [Method ] A total of 110 soil samples were collected from Xihe basin in Sichuan province. The raw hyperspectral reflectance of soil samples in the range of 350～2 500 nm wavelengths were measured by the standard procedure with a spectrometer of ASD Field Spec3 equipped with a high intensity contact probe under the laboratory conditions. Based on the pre-processing spectra,the optimized wavelength variables were selected by using the successive projections algorithm(SPA)and competitive adaptive reweighted sampling algorithm(CARS). Final model with partial least square regression(PLSR) were established. [ Result ] The results showed that SNV was the best soil spectral data pre-processing method of SAP. Based on the pre-processing spectral data by SNV,the CARS and SPA selected 54 and 13 key wavelength variables from full-spectrum to predict SAP concentrations, respectively. Compared with the SPA-PLSR model,the correlation coefficient of the CARS-PLSR model increased from 0. 894to 0. 945,and the root mean square error reduced from 5. 73 to 3. 56. [ Conclusion ] The utilization of CARS-PLSR algorithm based on soil hyperspectral data that is pre-processed by SNV could enhance SAP forecasting capability and reduce the model complexity,which could provide theoretical basis for the rapid inversion of SAP concentration by using hyperspectral data.更多还原