Local wavelet packet decomposition of soil hyperspectral for SOM estimation

It is a critical work to accurate extraction of spectral characteristics of soil organic matter (SOM), which will help to improve the model performance for SOM estimation. In this paper, we propose a new SOM prediction model using local wavelet characteristics of soil spectrum. Specifically, six level local wavelet packets decomposition are considered with different moving window sizes and sliding lengths, and thus formulate two new spectral indicators, namely, local wavelet packet energy spectrum (LWE) and local logarithmic wavelet packet energy spectrum (LLWE). The LWE and LLWE are then viewed as model inputs, which are respectively used to forecast SOM content. We test the prediction performance of the LWE and LLWE based on the two prediction models, that is, multiple linear regression (MLR) and ridge regression with cross-validation (RCV). The result shows that the two new spectral indicators help to enhance the spectral response information of SOM. Among the four pre-diction models, the LLWE-MLR is the most outstanding. Compared to the original hyperspectral and energy features vectors, the LWE and LLWE bring the better model performance. Since both the high and low-frequency components of local information of soil hyperspectral are fully extracted, the two new spectral indicators significantly improve the prediction accuracy and reliability of SOM.