高光谱反射率的滨海地区土壤全磷含量反演

Estimation of Soil Total Phosphorus Content in Coastal Areas Based on Hyperspectral Reflectance

反射光谱在近年来广泛应用于土壤属性的估算。作为一种有效估算土壤全磷含量的手段,反射光谱技术可以很大程度上减少传统化学测量方法所损耗的人力物力。以江苏滨海土壤为研究对象,在30个采样点采集了共147个土样,测量土壤样品光谱反射率及全磷含量。利用原始光谱反射率数据及6种不同的光谱变换结果,通过随机抽样(RS)、KS、SPXY三种样本集划分方法,基于偏最小二乘回归(PLSR)和支持向量机(SVM)方法分别建立土壤全磷含量的估算模型,对比分析了三种样本集划分方法对估算结果精度的影响。结果表明:(1)以原始光谱反射率为数据,PLSR模型,RS方法在多数情况下可以获得较为稳定的模型精度,明显优于KS和SPXY方法;在SVM模型中,采用SPXY方法获得的模型结果最优,KS次之,RS结果最差。(2)不同的样本集划分方法所合适的光谱变换方法不同,对于三种划分样本集方法,PLSR和SVM对应的最优光谱变换分别是对数的倒数和一阶导数(KS方法),原始光谱和一阶导数(RS方法),一阶导数和多元散射校正(SPXY方法)。其中采用KS方法划分样本集,PLSR和SVM均能获得最佳的预测结果。并非所有光谱变换方法都可以提高模型精度,部分光谱变换后PLSR模型预测精度显著降低;(3)在所有的样本集划分方法中,SVM的建模效果优于PLSR,采用RS方法划分样本集,PLSR的预测精度高于SVM,而采用KS和SPXY方法划分样本集,SVM的预测精度整体高于PLSR。综上所述,本研究区域估算土壤全磷含量的最佳模型是基于KS样本集划分方法和一阶导数光谱变换建立的SVM模型,此时拟合优度(R_(p)^(2))为0.82。结果表明反射光谱可以对滨海地区的土壤全磷含量进行有效预测,对土壤磷元素的高效快速反演具有一定的指导意义。

In recent decades,reflectance spectroscopy technology has developed rapidly and has been widely used in soil science,especially soil property estimation.It can greatly reduce the manpower and material resources consumed by traditional chemical measurement methods as an effective method to estimate total phosphorus content in soil.In this paper,147 soil samples were collected from 30 sampling sites in coastal soil of Jiangsu Province,China.The spectral data and total phosphorus content of the soil were measured,respectively.Three different sample set partitioning methods were performed on the original spectral data and six different spectral transformation results,including Random Sampling(RS),Kennard-Stone(KS)and Sample Set Partitioning Based on Joint X-Y Distance Algorithm(SPXY).In order to compare and analyze the influence of three sample set partitioning methods on the accuracy of estimation results,partial least square regression(PLSR)and support vector machine(SVM)methods were used to establish the estimation models of total phosphorus content in the soil.The results are as follows:(1)Under the condition of original spectral data,the RS method can obtain better results and more stable model accuracy in most cases for PLSR,which is superior to KS and SPXY.In the SVM model,the result obtained by SPXY method is the best,KS is the second,RS is the worst.(2)The appropriate spectral transformation methods for different sample set partitioning methods are also different.Among the three sample set partitioning methods,the optimal spectral transformations of PLSR and SVM are respectively the reciprocal of logarithm and the first derivative(KS method),the original spectrum and the first derivative(RS method),the first derivative and multiple scattering correction(SPXY method).Using the KS method to divide the sample set,PLSR and SVM model can obtain the optimal prediction results.Not all spectral transformation methods can improve the model accuracy.The prediction accuracy of the PLSR model is significantly reduced after partial spectral transformation.(3)Among all sample set partitioning methods,SVM has a better modeling effect than PLSR.Using the RS method to divide the sample set,the prediction accuracy of PLSR is higher than that of SVM.The results were reversed when KS and SPXY were used.According to the comprehensive results,the best estimation model for the study area was obtained using the KS sample set partitioning method,and the first derivative transformation method,combined with the SVM method,the R^(2) of the prediction result was 0.82.This study shows that reflectance spectroscopy can effectively predict the total phosphorus content of the soil in coastal areas and have a certain guiding significance for the efficient and rapid inversion of soil phosphorus.