Hyper-spectral estimation of soil organic matter in apple orchard based on CWT

Abstract

In the quantitative estimation by spectra of soil organic matter (SOM), there are great difficulties in the extraction of characteristic spectral information. It is difficult to effectively improve the correlation with SOM by ordinary spectral transformations, and the spectral estimation models are not high in accuracy and applicability. The purpose of this paper is to explore a more accurate and more suitable non-destructive evaluation model for apple orchard soil. In this paper, an apple orchard in Shuangquan Town, Changqing District, Jinan City, was selected as the study area. The continuous wavelet transform (CWT) was used to process the original soil spectra at multiple scales, and the effect of the estimation model on the SOM correlation at different resolutions was investigated. The results showed that the CWT treatment significantly improved the degree of spectral response to SOM in the orchard. Compared with the original spectra, the prediction accuracy of the model constructed by CWT was higher. After wavelet algorithm processing, the prediction ability of the model tends to increase with the decrease of spectral resolution. The best prediction accuracy was found at the spectral resolution of 20 nm, with the coefficient of determination R2= 0.7547 and the root means square error RMSE=0.0042; the validation accuracy R2=0.8015 and the root mean square error RMSE=0.0039. The model achieved a more satisfactory prediction result at the spectral resolution of 5 nm also. The results show that the broad-band spectral data processed by CWT can be used for accurate monitoring of SOM in apple orchards.