A review of spectral feature extraction and multi-feature fusion methods in predicting soil organic carbon

Abstract

The estimation of soil organic carbon based on visible near-infrared spectroscopy (VNIR) and hyperspectral image (HSI) has many advantages. However, the estimation accuracy has been a challenge that limits the wide application of spectral and hyperspectral imaging. Fully extracting the spectral and hyperspectral features of soil carbon information helps improve the estimation accuracy of soil organic carbon. Therefore, feature extraction is an important part of soil organic carbon estimation. This paper introduces the research on soil organic carbon prediction based on VNIR and HSI, the feature extraction methods, and the multi-feature fusion methods. The feature extraction methods introduce handcrafted feature extraction methods and deep learning feature extraction methods. Multi-feature fusion methods are divided into multi-feature fusion in handcrafted feature extraction methods and deep learning feature extraction methods. This paper also points out the future research direction and presents new ideas to improve the prediction of soil organic carbon. Soil organic carbon prediction based on VNIR and HSI, when combined with the multi-feature fusion method, is of great significance in extracting effective features and improving the prediction accuracy of soil organic carbon. It provides technical support for studying carbon cycling and carbon sinks, also guides the prediction of other soil properties.