Hyperspectral detection of soil organic matter content based on random forest algorithm
Received date: 2019-03-20
Revised date: 2019-07-18
Online published: 2019-11-18
In order to explore how to retain the spectral information and accurately detect the soil organic matter content, this paper investigated the possibility of using spectral processing techniques such as wavelet decomposition and random forest method to estimate the soil organic matter content and analyze the spectral curves of different wavelet decomposition reconstruction spectra in different soil types using spectroscopy data. This study took the soil samples as the study objects which were collected in Weigan River Oasis of Kuqa County, a typical arid area oasis at north-central of the Tarim Basin in Xinjiang, China. The soil organic matter content of these samples was determined. The ASD Field Spec FR was used to measure the soil samples’spectrum, and the spectral data were preprocessed by wavelet decomposition and mathematical transformation. Discrete wavelet transform (DWT) has the function of multi-scale analysis, which can transform multi-scale wavelet decomposition of soil near infrared spectroscopy data to analyze the spectral curves of different wavelet decomposition reconstruction spectra in different organic matter content and different soil types. The correlation analysis was used to determine the maximum wavelet decomposition layer and filter sensitive bands. Finally, a multi-variant linear prediction model about soil organic matter content was established based on the optimal characteristic spectrum produced by combining grey correlation analysis, random forest method to analyze the significance of different wavelet decomposition characteristic spectra. The results showed as follows: (1) The spectral reflectance of each wavelet decomposed is decreased with the increase of organic matter content. At the same time, the spectral curve of cultivated soil and forest soil shows a more gradual change than that of the saline soil and desert soil. (2) The correlation between the decomposition spectrum of the wavelet transform and the soil organic matter content is decreased first and then increased with the increase of the decomposition layer. In the sixth layer, the characteristic spectral curve and the number of sensitive bands tend to be stable, which helps to determine this layer as the largest decomposition layer of wavelet transform. (3) Compared with the gray correlation analysis, the random forest model is in line with the expectation for screening the factors of wavelet decomposition at each layer, and it comes a list of descending order according to the impact on soil organic matter content as follows: L3-(1/LgR)′、L4-(1/LgR)′、L6-(1/LgR)′、L5-(1/LgR)′、L2-(1/LgR)′、L0-1/LgR、L1-1/LgR. (4) Combining all SOM estimation models for statistical analysis,the model based on L-MC has the highest accuracy. The research shows that the monitoring of soil spectral organic matter content based on machine learning classification method combined with wavelet decomposition can effectively reduce noise band interference and improve the classification prediction accuracy of feature bands. The random forest prediction classification model has significant advantages over the traditional linear prediction classification model, such as gray correlation analysis. The random forest model not only outperforms the grey correlation analysis in statistical results, but also shows better reliability and stability in predicting ability. The results could provide scientific reference and support for the study of soil nutrients in the arid zone and local precision agriculture.
