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.

Graphical Abstract

Keywords:

• Hyperspectral images
• visible-near infrared spectroscopy
• soil organic carbon
• multi-feature fusion

Disclosure statement

No potential conflict of interest was reported by the authors

Additional information

Funding

This work has received funding from the Natural Science Foundation of Shandong Province, grant numbers are ZR2021QF028, ZR2021MD093, and ZR2021MD10, and the National Natural Science Foundation of China, grant numbers are U2006209, 32171578, and 42307483, .