Abstract: In order to realize the rapid and quantitative monitoring of soil organic carbon content in farmland, nine conventional pretreatment analysis methods were used to optimize the original spectral information and analyze the relationship between each pretreatment spectrum and wheat soil organic carbon content, and the continuous projection algorithm (SPA) was used to extract the spectral characteristics of soil organic carbon, and two types of soil organic carbon spectral monitoring models based on full spectrum and spectral characteristic bands were established. The results showed that the correlation between the pretreatment spectrum and soil organic carbon in wheat could be significantly improved compared with the original spectrum. In the meantime, the SPA method was used to extract and confirm the important information of soil organic carbon content in the spectral regions of 400～450 nm, 510～620 nm, 1010～1 060 nm and 2 000～2 400 nm. Comparing the performance of the two types of models, it can be seen that the continuous projection algorithm-multiple linear regression (SPA-MLR) model is better than the partial least squares regression (PLSR) model under the same pretreatment spectra, and the overall performance of the soil organic carbon content estimation model based on multivariate scattering correction (MSC) pretreatment spectra is the best (Rv2=0.726, RMSEv=0.109, RPDv=1.956), and it has practical application potential. This study confirms that spectral pretreatment can improve the correlation between spectral reflectance and soil organic carbon content in wheat to a certain extent, and affect the performance of the monitoring model, and the model construction method may have a more positive effect on the accuracy of model estimation. The results of this study can provide a theoretical basis and practical exploration for the detection of soil organic carbon content in farmland.
Key words: farmland soil; organic carbon; pretreatment; hyperspectral; responsive features
（Acta Laser Biology Sinica, 2024, 33(4): 316-325）