土壤有机质含量可见-近红外光谱反演模型校正集优选方法

土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone(KS)法,Rank-KS(RKS)和Sample set Partitioning based on joint X-Y distance(SPXY)法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型。结果表明:KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比(ratio of performance to standard deviation,RPD)2.0的前提下减少30%的校正样本;基于SPXY法的模型,当子校正集样本比例为总校正集的50%时达到最佳的模型预测精度,RPD为2.557;RKS法能够在保证预测精度的情况下(RPD2.0),最多减少总校正集70%的样本,对应模型RPD为2.212。当校正集与验证集的有机质含量分布相近时,能够以较少的建模样本达到与总校正集相近甚至更高的模型预测精度,提升土壤有机质光谱反演模型的实用性。

Optimization method of calibration dataset for VIS-NIR spectral inversion model of soil organic matter content

Abstract: Soil organic matter (SOM) is not only an important indicator of soil fertility but also an important source and sink of the global carbon cycle. Therefore, it is essential to acquire the information of SOM for soil management. Visible and near-infrared (VIS-NIR) reflectance spectroscopy, known as a novel, rapid, accurate, environment-friendly and efficient approach compared with conventional laboratory analyses, has proven to be promising in the acquisition of various soil properties. Construction of a calibration set is key to the VIS-NIR quantitative analysis in building up a prediction model of high quality. The aim of this paper was to explore how the sample selection method and the number of samples may affect the accuracy of VIS-NIR models for SOM estimation. A total of 100 paddy soil samples (0-15 cm) were collected from the Honghu City, which is located in the Jianghan Plain, China. After air drying, grinding and sieving (0.25 mm), reflectance of these pretreated samples was measured with FieldSpec3 (Analytical Spectral Devices Inc., America). Three samples were neglected after outlier detections of spectra and SOM content. Out of the remaining 97 samples, 20 samples were selected by means of concentration gradient, which then formed the validation sample set. The remaining 77 samples formed the total calibration set. With SOM content or soil spectral information as inputs, 3 sample selection methods, namely Kennard-Stone (KS), sample set partitioning based on joint X-Y distance (SPXY) and Rank-KS, were used in the construction of calibration subsets with different proportions of the samples in total calibration set, such as 10% and 20%. Based on the different calibration subsets, partial least squares regression (PLSR) was used for model calibrations. Results showed that the calibration set selected by KS approach could not improve model predictive capability compared with the total calibration set. The KS approach, however, could reduce as many as 30% samples of the total calibration set while the ratio of performance to standard deviation (RPD) was retained above 2.0. The SPXY approach performed the best when 50% samples of the total calibration set were selected in the model calibration. The determination coefficient for calibration (Rc2) reached 0.922, the determination coefficient for prediction (Rp2) was 0.848, and the RPD reached 2.557. This was because the SPXY approach took into account both SOM content and soil spectra in the sample selection process. With only 30% samples of the total calibration set selected by the Rank-KS method, it had the lowest cost of calibration with satisfactory performance (Rc2=0.872, Rp2=0.802 and RPD=2.212). Overall, such results indicate that it is possible to reduce the number of calibration samples while retaining or even improving the predictive capacity of VIS-NIR models for SOM estimation. All the 3 calibration selection approaches have been proven to be useful for the improvement of model practicability.