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土壤含水量的高光谱反演是当今研究的热点。以土壤多样化的陕西省横山县为研究区,通过野外采集土壤样品,室内利用ASD Field Spec FR地物光谱仪测定土壤样品光谱,采用称重法计算出土壤样品含水量,并分析了不同含水量土壤样品的光谱特性。针对土壤含水量光谱反演中光谱反演因子的构建问题,在研究一阶微分(FD)-主成分分析(PCA)、小波包变换(WPT)-FD-PCA反演输入因子生成方法及存在的不足的基础上,提出了基于谐波分析(HA)的WPT-FD-HA-PCA的反演输入因子构建方法。以上述三种反演输入因子为基础,建立了土壤含水量反演的FD-PCA-反向传播(BP)、WPT-FD-PCA-BP、WPT-FD-HA-PCA-BP三种BP反演模型。通过比较土壤含水量实测值与三种反演输入因子的反演结果,得出WPT-FD-HA-PCA-BP模型的反演精度最高,决定性系数R2达到0.9599,均方根误差为1.667%,其反演结果明显优于其他两种模型。这表明通过WPT和谐波分析能有效地抑制光谱噪声并压缩信号,在一定程度上明显提高了土壤含水量反演精度。
Hyperspectral inversion of soil moisture content is a hot topic in current research. With soil diversification in Hengshan County, Shaanxi Province as the research area, the soil samples were collected from the field, the spectra of soil samples were determined by ASD Field Spec FR spectrophotometer, the water content of soil samples was calculated by the weighing method, and the soil samples with different water content Spectral properties of soil samples. In order to solve the problem of spectral inversion factor in spectral inversion of soil moisture content, this paper studied the generation and existence of input factor by first derivative (FD) -PCA and WPT-FD-PCA Based on the harmonic analysis (HA) WPT-FD-HA-PCA inversion input factor building method. Based on the above three inversion input factors, three kinds of BP-PCA-backpropagation (BP), WPT-FD-PCA-BP and WPT-FD-HA-PCA- Inversion model. The results show that the inversion accuracy of WPT-FD-HA-PCA-BP model is the highest with the determinant coefficient R2 reaching 0.9599 and the root mean square error of 1.667% , The inversion result is obviously better than the other two models. This shows that by WPT and harmonic analysis can effectively suppress the spectral noise and compress the signal, to a certain extent, significantly improve the soil moisture retrieval accuracy.